Table of Contents
Contents

Built-In Zoo Models¶

This page lists all of the natively available models in the FiftyOne Model Zoo.

Check out the API reference for complete instructions for using the Model Zoo.

alexnet-imagenet-torch

AlexNet model architecture from "One weird trick for parallelizing convolutional neural networks" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

centernet-hg104-1024-coco-tf2

CenterNet model from "Objects as Points" with the Hourglass-104 backbone trained on COCO resized to 1024x1024

Detection,Coco,TensorFlow-2

centernet-hg104-512-coco-tf2

CenterNet model from "Objects as Points" with the Hourglass-104 backbone trained on COCO resized to 512x512

Detection,Coco,TensorFlow-2

centernet-mobilenet-v2-fpn-512-coco-tf2

CenterNet model from "Objects as Points" with the MobileNetV2 backbone trained on COCO resized to 512x512

Detection,Coco,TensorFlow-2

centernet-resnet101-v1-fpn-512-coco-tf2

CenterNet model from "Objects as Points" with the ResNet-101v1 backbone + FPN trained on COCO resized to 512x512

Detection,Coco,TensorFlow-2

centernet-resnet50-v1-fpn-512-coco-tf2

CenterNet model from "Objects as Points" with the ResNet-50-v1 backbone + FPN trained on COCO resized to 512x512

Detection,Coco,TensorFlow-2

centernet-resnet50-v2-512-coco-tf2

CenterNet model from "Objects as Points" with the ResNet-50v2 backbone trained on COCO resized to 512x512

Detection,Coco,TensorFlow-2

classification-transformer-torch

Hugging Face Transformers model for image classification

Classification,Logits,Embeddings,PyTorch,Transformers

clip-vit-base32-torch

CLIP text/image encoder from "Learning Transferable Visual Models From Natural Language Supervision" trained on 400M text-image pairs

Classification,Logits,Embeddings,PyTorch,Clip,Zero-shot

deeplabv3-cityscapes-tf

DeepLabv3+ semantic segmentation model from "Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation" with Xception backbone trained on the Cityscapes dataset

Segmentation,Cityscapes,TensorFlow

deeplabv3-mnv2-cityscapes-tf

DeepLabv3+ semantic segmentation model from "Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation" with MobileNetV2 backbone trained on the Cityscapes dataset

Segmentation,Cityscapes,TensorFlow

deeplabv3-resnet101-coco-torch

DeepLabV3 model from "Rethinking Atrous Convolution for Semantic Image Segmentation" with ResNet-101 backbone trained on COCO

Segmentation,Coco,PyTorch

deeplabv3-resnet50-coco-torch

DeepLabV3 model from "Rethinking Atrous Convolution for Semantic Image Segmentation" with ResNet-50 backbone trained on COCO

Segmentation,Coco,PyTorch

densenet121-imagenet-torch

Densenet-121 model from "Densely Connected Convolutional Networks" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

densenet161-imagenet-torch

Densenet-161 model from "Densely Connected Convolutional Networks" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

densenet169-imagenet-torch

Densenet-169 model from "Densely Connected Convolutional Networks" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

densenet201-imagenet-torch

Densenet-201 model from "Densely Connected Convolutional Networks" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

depth-estimation-transformer-torch

Hugging Face Transformers model for monocular depth estimation

Depth,PyTorch,Transformers

detection-transformer-torch

Hugging Face Transformers model for object detection

Detection,Logits,Embeddings,PyTorch,Transformers

dinov2-vitb14-torch

DINOv2: Learning Robust Visual Features without Supervision. Model: ViT-B/14 distilled

Embeddings,PyTorch

dinov2-vitg14-torch

DINOv2: Learning Robust Visual Features without Supervision. Model: ViT-g/14

Embeddings,PyTorch

dinov2-vitl14-torch

DINOv2: Learning Robust Visual Features without Supervision. Model: ViT-L/14 distilled

Embeddings,PyTorch

dinov2-vits14-torch

DINOv2: Learning Robust Visual Features without Supervision. Model: ViT-S/14 distilled

Embeddings,PyTorch

efficientdet-d0-512-coco-tf2

EfficientDet-D0 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO resized to 512x512

Detection,Coco,TensorFlow-2

efficientdet-d0-coco-tf1

EfficientDet-D0 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO

Detection,Coco,TensorFlow-1

efficientdet-d1-640-coco-tf2

EfficientDet-D1 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO resized to 640x640

Detection,Coco,TensorFlow-2

efficientdet-d1-coco-tf1

EfficientDet-D1 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO

Detection,Coco,TensorFlow-1

efficientdet-d2-768-coco-tf2

EfficientDet-D2 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO resized to 768x768

Detection,Coco,TensorFlow-2

efficientdet-d2-coco-tf1

EfficientDet-D2 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO

Detection,Coco,TensorFlow-1

efficientdet-d3-896-coco-tf2

EfficientDet-D3 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO resized to 896x896

Detection,Coco,TensorFlow-2

efficientdet-d3-coco-tf1

EfficientDet-D3 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO

Detection,Coco,TensorFlow-1

efficientdet-d4-1024-coco-tf2

EfficientDet-D4 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO resized to 1024x1024

Detection,Coco,TensorFlow-2

efficientdet-d4-coco-tf1

EfficientDet-D4 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO

Detection,Coco,TensorFlow-1

efficientdet-d5-1280-coco-tf2

EfficientDet-D5 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO resized to 1280x1280

Detection,Coco,TensorFlow-2

efficientdet-d5-coco-tf1

EfficientDet-D5 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO

Detection,Coco,TensorFlow-1

efficientdet-d6-1280-coco-tf2

EfficientDet-D6 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO resized to 1280x1280

Detection,Coco,TensorFlow-2

efficientdet-d6-coco-tf1

EfficientDet-D6 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO

Detection,Coco,TensorFlow-1

efficientdet-d7-1536-coco-tf2

EfficientDet-D7 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO resized to 1536x1536

Detection,Coco,TensorFlow-2

faster-rcnn-inception-resnet-atrous-v2-coco-tf

Faster R-CNN model from "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" atrous version with Inception backbone trained on COCO

Detection,Coco,TensorFlow

faster-rcnn-inception-resnet-atrous-v2-lowproposals-coco-tf

Faster R-CNN model from "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" atrous version with low-proposals and Inception backbone trained on COCO

Detection,Coco,TensorFlow

faster-rcnn-inception-v2-coco-tf

Faster R-CNN model from "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" with Inception v2 backbone trained on COCO

Detection,Coco,TensorFlow

faster-rcnn-nas-coco-tf

Faster R-CNN model from "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" with NAS-net backbone trained on COCO

Detection,Coco,TensorFlow

faster-rcnn-nas-lowproposals-coco-tf

Faster R-CNN model from "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" with low-proposals and NAS-net backbone trained on COCO

Detection,Coco,TensorFlow

faster-rcnn-resnet101-coco-tf

Faster R-CNN model from "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" with ResNet-101 backbone trained on COCO

Detection,Coco,TensorFlow

faster-rcnn-resnet101-lowproposals-coco-tf

Faster R-CNN model from "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" with low-proposals and ResNet-101 backbone trained on COCO

Detection,Coco,TensorFlow

faster-rcnn-resnet50-coco-tf

Faster R-CNN model from "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" with ResNet-50 backbone trained on COCO

Detection,Coco,TensorFlow

faster-rcnn-resnet50-fpn-coco-torch

Faster R-CNN model from "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" with ResNet-50 FPN backbone trained on COCO

Detection,Coco,PyTorch

faster-rcnn-resnet50-lowproposals-coco-tf

Faster R-CNN model from "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" with low-proposals and ResNet-50 backbone trained on COCO

Detection,Coco,TensorFlow

fcn-resnet101-coco-torch

FCN model from "Fully Convolutional Networks for Semantic Segmentation" with ResNet-101 backbone trained on COCO

Segmentation,Coco,PyTorch

fcn-resnet50-coco-torch

FCN model from "Fully Convolutional Networks for Semantic Segmentation" with ResNet-50 backbone trained on COCO

Segmentation,Coco,PyTorch

googlenet-imagenet-torch

GoogLeNet (Inception v1) model from "Going Deeper with Convolutions" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

inception-resnet-v2-imagenet-tf1

Inception v2 model from "Rethinking the Inception Architecture for Computer Vision" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,TensorFlow-1

inception-v3-imagenet-torch

Inception v3 model from "Rethinking the Inception Architecture for Computer Vision" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

inception-v4-imagenet-tf1

Inception v4 model from "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,TensorFlow-1

keypoint-rcnn-resnet50-fpn-coco-torch

Keypoint R-CNN model from "Mask R-CNN" with ResNet-50 FPN backbone trained on COCO

Keypoints,Coco,PyTorch

mask-rcnn-inception-resnet-v2-atrous-coco-tf

Mask R-CNN model from "Mask R-CNN" atrous version with Inception backbone trained on COCO

Instances,Coco,TensorFlow

mask-rcnn-inception-v2-coco-tf

Mask R-CNN model from "Mask R-CNN" with Inception backbone trained on COCO

Instances,Coco,TensorFlow

mask-rcnn-resnet101-atrous-coco-tf

Mask R-CNN model from "Mask R-CNN" atrous version with ResNet-101 backbone trained on COCO

Instances,Coco,TensorFlow

mask-rcnn-resnet50-atrous-coco-tf

Mask R-CNN model from "Mask R-CNN" atrous version with ResNet-50 backbone trained on COCO

Instances,Coco,TensorFlow

mask-rcnn-resnet50-fpn-coco-torch

Mask R-CNN model from "Mask R-CNN" with ResNet-50 FPN backbone trained on COCO

Instances,Coco,PyTorch

mnasnet0.5-imagenet-torch

MNASNet model from from "MnasNet: Platform-Aware Neural Architecture Search for Mobile" with depth multiplier of 0.5 trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

mnasnet1.0-imagenet-torch

MNASNet model from "MnasNet: Platform-Aware Neural Architecture Search for Mobile" with depth multiplier of 1.0 trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

mobilenet-v2-imagenet-tf1

MobileNetV2 model from "MobileNetV2: Inverted Residuals and Linear Bottlenecks" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,TensorFlow-1

mobilenet-v2-imagenet-torch

MobileNetV2 model from "MobileNetV2: Inverted Residuals and Linear Bottlenecks" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

open-clip-torch

OPEN CLIP text/image encoder from "Learning Transferable Visual Models From Natural Language Supervision" trained on 400M text-image pairs

Classification,Logits,Embeddings,PyTorch,Clip,Zero-shot

resnet-v1-50-imagenet-tf1

ResNet-50 v1 model from "Deep Residual Learning for Image Recognition" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,TensorFlow-1

resnet-v2-50-imagenet-tf1

ResNet-50 v2 model from "Deep Residual Learning for Image Recognition" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,TensorFlow-1

resnet101-imagenet-torch

ResNet-101 model from "Deep Residual Learning for Image Recognition" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

resnet152-imagenet-torch

ResNet-152 model from "Deep Residual Learning for Image Recognition" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

resnet18-imagenet-torch

ResNet-18 model from "Deep Residual Learning for Image Recognition" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

resnet34-imagenet-torch

ResNet-34 model from "Deep Residual Learning for Image Recognition" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

resnet50-imagenet-torch

ResNet-50 model from "Deep Residual Learning for Image Recognition" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

resnext101-32x8d-imagenet-torch

ResNeXt-101 32x8d model from "Aggregated Residual Transformations for Deep Neural Networks" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

resnext50-32x4d-imagenet-torch

ResNeXt-50 32x4d model from "Aggregated Residual Transformations for Deep Neural Networks" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

retinanet-resnet50-fpn-coco-torch

RetinaNet model from "Focal Loss for Dense Object Detection" with ResNet-50 FPN backbone trained on COCO

Detection,Coco,PyTorch

rfcn-resnet101-coco-tf

R-FCN object detection model from "R-FCN: Object Detection via Region-based Fully Convolutional Networks" with ResNet-101 backbone trained on COCO

Detection,Coco,TensorFlow

rtdetr-l-coco-torch

RT-DETR-l model trained on COCO

Detection,Coco,PyTorch,Transformer

rtdetr-x-coco-torch

RT-DETR-x model trained on COCO

Detection,Coco,PyTorch,Transformer

segment-anything-2-hiera-base-plus-image-torch

Segment Anything Model 2 (SAM2) from "SAM2: Segment Anything in Images and Videos"

Segment-anything,PyTorch,Zero-shot

segment-anything-2-hiera-base-plus-video-torch

Segment Anything Model 2 (SAM2) from "SAM2: Segment Anything in Images and Videos"

Segment-anything,PyTorch,Zero-shot,Video

segment-anything-2-hiera-large-image-torch

Segment Anything Model 2 (SAM2) from "SAM2: Segment Anything in Images and Videos"

Segment-anything,PyTorch,Zero-shot

segment-anything-2-hiera-large-video-torch

Segment Anything Model 2 (SAM2) from "SAM2: Segment Anything in Images and Videos"

Segment-anything,PyTorch,Zero-shot,Video

segment-anything-2-hiera-small-image-torch

Segment Anything Model 2 (SAM2) from "SAM2: Segment Anything in Images and Videos"

Segment-anything,PyTorch,Zero-shot

segment-anything-2-hiera-small-video-torch

Segment Anything Model 2 (SAM2) from "SAM2: Segment Anything in Images and Videos"

Segment-anything,PyTorch,Zero-shot,Video

segment-anything-2-hiera-tiny-image-torch

Segment Anything Model 2 (SAM2) from "SAM2: Segment Anything in Images and Videos"

Segment-anything,PyTorch,Zero-shot

segment-anything-2-hiera-tiny-video-torch

Segment Anything Model 2 (SAM2) from "SAM2: Segment Anything in Images and Videos"

Segment-anything,PyTorch,Zero-shot,Video

segment-anything-vitb-torch

Segment Anything Model (SAM) from "Segment Anything" with ViT-B/16 backbone trained on SA-1B

Segment-anything,Sa-1b,PyTorch,Zero-shot

segment-anything-vith-torch

Segment Anything Model (SAM) from "Segment Anything" with ViT-H/16 backbone trained on SA-1B

Segment-anything,Sa-1b,PyTorch,Zero-shot

segment-anything-vitl-torch

Segment Anything Model (SAM) from "Segment Anything" with ViT-L/16 backbone trained on SA-1B

Segment-anything,Sa-1b,PyTorch,Zero-shot

segmentation-transformer-torch

Hugging Face Transformers model for semantic segmentation

Segmentation,PyTorch,Transformers

shufflenetv2-0.5x-imagenet-torch

ShuffleNetV2 model from "ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" with 0.5x output channels trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

shufflenetv2-1.0x-imagenet-torch

ShuffleNetV2 model from "ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" with 1.0x output channels trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

squeezenet-1.1-imagenet-torch

SqueezeNet 1.1 model from "the official SqueezeNet repo" trained on ImageNet

Classification,Imagenet,PyTorch

squeezenet-imagenet-torch

SqueezeNet model from "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and" trained on ImageNet

Classification,Imagenet,PyTorch

ssd-inception-v2-coco-tf

Inception Single Shot Detector model from "SSD: Single Shot MultiBox Detector" trained on COCO

Detection,Coco,TensorFlow

ssd-mobilenet-v1-coco-tf

Single Shot Detector model from "SSD: Single Shot MultiBox Detector" with MobileNetV1 backbone trained on COCO

Detection,Coco,TensorFlow

ssd-mobilenet-v1-fpn-640-coco17

MobileNetV1 model from "MobileNetV2: Inverted Residuals and Linear Bottlenecks" resized to 640x640

Detection,Coco,TensorFlow-2

ssd-mobilenet-v1-fpn-coco-tf

FPN Single Shot Detector model from "SSD: Single Shot MultiBox Detector" with MobileNetV1 backbone trained on COCO

Detection,Coco,TensorFlow

ssd-mobilenet-v2-320-coco17

MobileNetV2 model from "MobileNetV2: Inverted Residuals and Linear Bottlenecks" resized to 320x320

Detection,Coco,TensorFlow-2

ssd-resnet50-fpn-coco-tf

FPN Single Shot Detector model from "SSD: Single Shot MultiBox Detector" with ResNet-50 backbone trained on COCO

Detection,Coco,TensorFlow

vgg11-bn-imagenet-torch

VGG-11 model from "Very Deep Convolutional Networks for Large-Scale Image Recognition" with batch normalization trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

vgg11-imagenet-torch

VGG-11 model from "Very Deep Convolutional Networks for Large-Scale Image Recognition" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

vgg13-bn-imagenet-torch

VGG-13 model from "Very Deep Convolutional Networks for Large-Scale Image Recognition" with batch normalization trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

vgg13-imagenet-torch

VGG-13 model from "Very Deep Convolutional Networks for Large-Scale Image Recognition" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

vgg16-bn-imagenet-torch

VGG-16 model from "Very Deep Convolutional Networks for Large-Scale Image Recognition" with batch normalization trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

vgg16-imagenet-tf1

VGG-16 model from "Very Deep Convolutional Networks for Large-Scale Image Recognition" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,TensorFlow-1

vgg16-imagenet-torch

VGG-16 model from "Very Deep Convolutional Networks for Large-Scale Image Recognition" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

vgg19-bn-imagenet-torch

VGG-19 model from "Very Deep Convolutional Networks for Large-Scale Image Recognition" with batch normalization trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

vgg19-imagenet-torch

VGG-19 model from "Very Deep Convolutional Networks for Large-Scale Image Recognition" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

wide-resnet101-2-imagenet-torch

Wide ResNet-101-2 model from "Wide Residual Networks" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

wide-resnet50-2-imagenet-torch

Wide ResNet-50-2 model from "Wide Residual Networks" trained on ImageNet

Classification,Embeddings,Logits,Imagenet,PyTorch

yolo-nas-torch

YOLO-NAS is an open-source training library for advanced computer vision models. It specializes in accuracy and efficiency, supporting tasks like object detection

Classification,PyTorch,Yolo

yolo-v2-coco-tf1

YOLOv2 model from "YOLO9000: Better, Faster, Stronger" trained on COCO

Detection,Coco,TensorFlow-1

yolov10l-coco-torch

YOLOv10-L model trained on COCO

Detection,Coco,PyTorch,Yolo

yolov10m-coco-torch

YOLOv10-M model trained on COCO

Detection,Coco,PyTorch,Yolo

yolov10n-coco-torch

YOLOv10-N model trained on COCO

Detection,Coco,PyTorch,Yolo

yolov10s-coco-torch

YOLOv10-S model trained on COCO

Detection,Coco,PyTorch,Yolo

yolov10x-coco-torch

YOLOv10-X model trained on COCO

Detection,Coco,PyTorch,Yolo

yolov5l-coco-torch

Ultralytics YOLOv5l model trained on COCO

Detection,Coco,PyTorch,Yolo

yolov5m-coco-torch

Ultralytics YOLOv5m model trained on COCO

Detection,Coco,PyTorch,Yolo

yolov5n-coco-torch

Ultralytics YOLOv5n model trained on COCO

Detection,Coco,PyTorch,Yolo

yolov5s-coco-torch

Ultralytics YOLOv5s model trained on COCO

Detection,Coco,PyTorch,Yolo

yolov5x-coco-torch

Ultralytics YOLOv5x model trained on COCO

Detection,Coco,PyTorch,Yolo

yolov8l-coco-torch

Ultralytics YOLOv8l model trained on COCO

Detection,Coco,PyTorch,Yolo

yolov8l-obb-dotav1-torch

YOLOv8l Oriented Bounding Box model

Detection,PyTorch,Yolo,Polylines,Obb

yolov8l-oiv7-torch

Ultralytics YOLOv8l model trained Open Images v7

Detection,Oiv7,PyTorch,Yolo

yolov8l-seg-coco-torch

Ultralytics YOLOv8l Segmentation model trained on COCO

Segmentation,Coco,PyTorch,Yolo

yolov8l-world-torch

YOLOv8l-World model

Detection,PyTorch,Yolo,Zero-shot

yolov8m-coco-torch

Ultralytics YOLOv8m model trained on COCO

Detection,Coco,PyTorch,Yolo

yolov8m-obb-dotav1-torch

YOLOv8m Oriented Bounding Box model

Detection,PyTorch,Yolo,Polylines,Obb

yolov8m-oiv7-torch

Ultralytics YOLOv8m model trained Open Images v7

Detection,Oiv7,PyTorch,Yolo

yolov8m-seg-coco-torch

Ultralytics YOLOv8m Segmentation model trained on COCO

Segmentation,Coco,PyTorch,Yolo

yolov8m-world-torch

YOLOv8m-World model

Detection,PyTorch,Yolo,Zero-shot

yolov8n-coco-torch

Ultralytics YOLOv8n model trained on COCO

Detection,Coco,PyTorch,Yolo

yolov8n-obb-dotav1-torch

YOLOv8n Oriented Bounding Box model

Detection,PyTorch,Yolo,Polylines,Obb

yolov8n-oiv7-torch

Ultralytics YOLOv8n model trained on Open Images v7

Detection,Oiv7,PyTorch,Yolo

yolov8n-seg-coco-torch

Ultralytics YOLOv8n Segmentation model trained on COCO

Segmentation,Coco,PyTorch,Yolo

yolov8s-coco-torch

Ultralytics YOLOv8s model trained on COCO

Detection,Coco,PyTorch,Yolo

yolov8s-obb-dotav1-torch

YOLOv8s Oriented Bounding Box model

Detection,PyTorch,Yolo,Polylines,Obb

yolov8s-oiv7-torch

Ultralytics YOLOv8s model trained on Open Images v7

Detection,Oiv7,PyTorch,Yolo

yolov8s-seg-coco-torch

Ultralytics YOLOv8s Segmentation model trained on COCO

Segmentation,Coco,PyTorch,Yolo

yolov8s-world-torch

YOLOv8s-World model

Detection,PyTorch,Yolo,Zero-shot

yolov8x-coco-torch

Ultralytics YOLOv8x model trained on COCO

Detection,Coco,PyTorch,Yolo

yolov8x-obb-dotav1-torch

YOLOv8x Oriented Bounding Box model

Detection,PyTorch,Yolo,Polylines,Obb

yolov8x-oiv7-torch

Ultralytics YOLOv8x model trained Open Images v7

Detection,Oiv7,PyTorch,Yolo

yolov8x-seg-coco-torch

Ultralytics YOLOv8x Segmentation model trained on COCO

Segmentation,Coco,PyTorch,Yolo

yolov8x-world-torch

YOLOv8x-World model

Detection,PyTorch,Yolo,Zero-shot

yolov9c-coco-torch

YOLOv9-C model trained on COCO

Detection,Coco,PyTorch,Yolo

yolov9c-seg-coco-torch

YOLOv9-C Segmentation model trained on COCO

Segmentation,Coco,PyTorch,Yolo

yolov9e-coco-torch

YOLOv9-E model trained on COCO

Detection,Coco,PyTorch,Yolo

yolov9e-seg-coco-torch

YOLOv9-E Segmentation model trained on COCO

Segmentation,Coco,PyTorch,Yolo

zero-shot-classification-transformer-torch

Hugging Face Transformers model for zero-shot image classification

Classification,Logits,Embeddings,PyTorch,Transformers,Zero-shot

zero-shot-detection-transformer-torch

Hugging Face Transformers model for zero-shot object detection

Detection,Logits,Embeddings,PyTorch,Transformers,Zero-shot

Torch models¶

alexnet-imagenet-torch¶

AlexNet model architecture from One weird trick for parallelizing convolutional neural networks trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("alexnet-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

classification-transformer-torch¶

Hugging Face Transformers model for image classification.

Details

Requirements

  • Packages: torch, torchvision, transformers

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("classification-transformer-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

clip-vit-base32-torch¶

CLIP text/image encoder from Learning Transferable Visual Models From Natural Language Supervision trained on 400M text-image pairs.

Details

  • Model name: clip-vit-base32-torch

  • Model source: https://github.com/openai/CLIP

  • Model size: 337.58 MB

  • Exposes embeddings? yes

  • Tags: classification, logits, embeddings, torch, clip, zero-shot

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("clip-vit-base32-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

#
# Make zero-shot predictions with custom classes
#

model = foz.load_zoo_model(
    "clip-vit-base32-torch",
    text_prompt="A photo of a",
    classes=["person", "dog", "cat", "bird", "car", "tree", "chair"],
)

dataset.apply_model(model, label_field="predictions")
session.refresh()

deeplabv3-resnet101-coco-torch¶

DeepLabV3 model from Rethinking Atrous Convolution for Semantic Image Segmentation with ResNet-101 backbone trained on COCO.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("deeplabv3-resnet101-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

deeplabv3-resnet50-coco-torch¶

DeepLabV3 model from Rethinking Atrous Convolution for Semantic Image Segmentation with ResNet-50 backbone trained on COCO.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("deeplabv3-resnet50-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

densenet121-imagenet-torch¶

Densenet-121 model from Densely Connected Convolutional Networks trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("densenet121-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

densenet161-imagenet-torch¶

Densenet-161 model from Densely Connected Convolutional Networks trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("densenet161-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

densenet169-imagenet-torch¶

Densenet-169 model from Densely Connected Convolutional Networks trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("densenet169-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

densenet201-imagenet-torch¶

Densenet-201 model from Densely Connected Convolutional Networks trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("densenet201-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

depth-estimation-transformer-torch¶

Hugging Face Transformers model for monocular depth estimation.

Details

Requirements

  • Packages: torch, torchvision, transformers

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("depth-estimation-transformer-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

detection-transformer-torch¶

Hugging Face Transformers model for object detection.

Details

Requirements

  • Packages: torch, torchvision, transformers

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("detection-transformer-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

dinov2-vitb14-torch¶

DINOv2: Learning Robust Visual Features without Supervision. Model: ViT-B/14 distilled.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("dinov2-vitb14-torch")

embeddings = dataset.compute_embeddings(model)

dinov2-vitg14-torch¶

DINOv2: Learning Robust Visual Features without Supervision. Model: ViT-g/14.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("dinov2-vitg14-torch")

embeddings = dataset.compute_embeddings(model)

dinov2-vitl14-torch¶

DINOv2: Learning Robust Visual Features without Supervision. Model: ViT-L/14 distilled.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("dinov2-vitl14-torch")

embeddings = dataset.compute_embeddings(model)

dinov2-vits14-torch¶

DINOv2: Learning Robust Visual Features without Supervision. Model: ViT-S/14 distilled.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("dinov2-vits14-torch")

embeddings = dataset.compute_embeddings(model)

faster-rcnn-resnet50-fpn-coco-torch¶

Faster R-CNN model from Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks with ResNet-50 FPN backbone trained on COCO.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("faster-rcnn-resnet50-fpn-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

fcn-resnet101-coco-torch¶

FCN model from Fully Convolutional Networks for Semantic Segmentation with ResNet-101 backbone trained on COCO.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("fcn-resnet101-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

fcn-resnet50-coco-torch¶

FCN model from Fully Convolutional Networks for Semantic Segmentation with ResNet-50 backbone trained on COCO.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("fcn-resnet50-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

googlenet-imagenet-torch¶

GoogLeNet (Inception v1) model from Going Deeper with Convolutions trained on ImageNet.

Details

Requirements

  • Packages: scipy, torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("googlenet-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

inception-v3-imagenet-torch¶

Inception v3 model from Rethinking the Inception Architecture for Computer Vision trained on ImageNet.

Details

Requirements

  • Packages: scipy, torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("inception-v3-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

keypoint-rcnn-resnet50-fpn-coco-torch¶

Keypoint R-CNN model from Mask R-CNN with ResNet-50 FPN backbone trained on COCO.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("keypoint-rcnn-resnet50-fpn-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

mask-rcnn-resnet50-fpn-coco-torch¶

Mask R-CNN model from Mask R-CNN with ResNet-50 FPN backbone trained on COCO.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("mask-rcnn-resnet50-fpn-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

mnasnet0.5-imagenet-torch¶

MNASNet model from from MnasNet: Platform-Aware Neural Architecture Search for Mobile with depth multiplier of 0.5 trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("mnasnet0.5-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

mnasnet1.0-imagenet-torch¶

MNASNet model from MnasNet: Platform-Aware Neural Architecture Search for Mobile with depth multiplier of 1.0 trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("mnasnet1.0-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

mobilenet-v2-imagenet-torch¶

MobileNetV2 model from MobileNetV2: Inverted Residuals and Linear Bottlenecks trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("mobilenet-v2-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

open-clip-torch¶

OPEN CLIP text/image encoder from Learning Transferable Visual Models From Natural Language Supervision trained on 400M text-image pairs.

Details

Requirements

  • Packages: torch, torchvision, open_clip_torch

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("open-clip-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

#
# Make zero-shot predictions with custom classes
#

model = foz.load_zoo_model(
    "open-clip-torch",
    text_prompt="A photo of a",
    classes=["person", "dog", "cat", "bird", "car", "tree", "chair"],
)

dataset.apply_model(model, label_field="predictions")
session.refresh()

resnet101-imagenet-torch¶

ResNet-101 model from Deep Residual Learning for Image Recognition trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("resnet101-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

resnet152-imagenet-torch¶

ResNet-152 model from Deep Residual Learning for Image Recognition trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("resnet152-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

resnet18-imagenet-torch¶

ResNet-18 model from Deep Residual Learning for Image Recognition trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("resnet18-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

resnet34-imagenet-torch¶

ResNet-34 model from Deep Residual Learning for Image Recognition trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("resnet34-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

resnet50-imagenet-torch¶

ResNet-50 model from Deep Residual Learning for Image Recognition trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("resnet50-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

resnext101-32x8d-imagenet-torch¶

ResNeXt-101 32x8d model from Aggregated Residual Transformations for Deep Neural Networks trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("resnext101-32x8d-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

resnext50-32x4d-imagenet-torch¶

ResNeXt-50 32x4d model from Aggregated Residual Transformations for Deep Neural Networks trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("resnext50-32x4d-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

retinanet-resnet50-fpn-coco-torch¶

RetinaNet model from Focal Loss for Dense Object Detection with ResNet-50 FPN backbone trained on COCO.

Details

Requirements

  • Packages: torch, torchvision>=0.8.0

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("retinanet-resnet50-fpn-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

rtdetr-l-coco-torch¶

RT-DETR-l model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.2.0

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("rtdetr-l-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

rtdetr-x-coco-torch¶

RT-DETR-x model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.2.0

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("rtdetr-x-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

segment-anything-2-hiera-base-plus-image-torch¶

Segment Anything Model 2 (SAM2) from SAM2: Segment Anything in Images and Videos.

Details

  • Model name: segment-anything-2-hiera-base-plus-image-torch

  • Model source: https://ai.meta.com/sam2/

  • Model size: 148.68 MB

  • Exposes embeddings? no

  • Tags: segment-anything, torch, zero-shot

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("segment-anything-2-hiera-base-plus-image-torch")

# Segment inside boxes
dataset.apply_model(
    model,
    label_field="segmentations",
    prompt_field="ground_truth",  # can contain Detections or Keypoints
)

# Full automatic segmentations
dataset.apply_model(model, label_field="auto")

session = fo.launch_app(dataset)

segment-anything-2-hiera-base-plus-video-torch¶

Segment Anything Model 2 (SAM2) from SAM2: Segment Anything in Images and Videos.

Details

  • Model name: segment-anything-2-hiera-base-plus-video-torch

  • Model source: https://ai.meta.com/sam2/

  • Model size: 148.68 MB

  • Exposes embeddings? no

  • Tags: segment-anything, torch, zero-shot, video

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz
from fiftyone import ViewField as F

dataset = foz.load_zoo_dataset("quickstart-video", max_samples=2)

# Only retain detections in the first frame
(
    dataset
    .match_frames(F("frame_number") > 1)
    .set_field("frames.detections", None)
    .save()
)

model = foz.load_zoo_model("segment-anything-2-hiera-base-plus-video-torch")

# Segment inside boxes and propagate to all frames
dataset.apply_model(
    model,
    label_field="segmentations",
    prompt_field="frames.detections",  # can contain Detections or Keypoints
)

session = fo.launch_app(dataset)

segment-anything-2-hiera-large-image-torch¶

Segment Anything Model 2 (SAM2) from SAM2: Segment Anything in Images and Videos.

Details

  • Model name: segment-anything-2-hiera-large-image-torch

  • Model source: https://ai.meta.com/sam2/

  • Model size: 148.68 MB

  • Exposes embeddings? no

  • Tags: segment-anything, torch, zero-shot

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("segment-anything-2-hiera-large-image-torch")

# Segment inside boxes
dataset.apply_model(
    model,
    label_field="segmentations",
    prompt_field="ground_truth",  # can contain Detections or Keypoints
)

# Full automatic segmentations
dataset.apply_model(model, label_field="auto")

session = fo.launch_app(dataset)

segment-anything-2-hiera-large-video-torch¶

Segment Anything Model 2 (SAM2) from SAM2: Segment Anything in Images and Videos.

Details

  • Model name: segment-anything-2-hiera-large-video-torch

  • Model source: https://ai.meta.com/sam2/

  • Model size: 148.68 MB

  • Exposes embeddings? no

  • Tags: segment-anything, torch, zero-shot, video

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz
from fiftyone import ViewField as F

dataset = foz.load_zoo_dataset("quickstart-video", max_samples=2)

# Only retain detections in the first frame
(
    dataset
    .match_frames(F("frame_number") > 1)
    .set_field("frames.detections", None)
    .save()
)

model = foz.load_zoo_model("segment-anything-2-hiera-large-video-torch")

# Segment inside boxes and propagate to all frames
dataset.apply_model(
    model,
    label_field="segmentations",
    prompt_field="frames.detections",  # can contain Detections or Keypoints
)

session = fo.launch_app(dataset)

segment-anything-2-hiera-small-image-torch¶

Segment Anything Model 2 (SAM2) from SAM2: Segment Anything in Images and Videos.

Details

  • Model name: segment-anything-2-hiera-small-image-torch

  • Model source: https://ai.meta.com/sam2/

  • Model size: 148.68 MB

  • Exposes embeddings? no

  • Tags: segment-anything, torch, zero-shot

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("segment-anything-2-hiera-small-image-torch")

# Segment inside boxes
dataset.apply_model(
    model,
    label_field="segmentations",
    prompt_field="ground_truth",  # can contain Detections or Keypoints
)

# Full automatic segmentations
dataset.apply_model(model, label_field="auto")

session = fo.launch_app(dataset)

segment-anything-2-hiera-small-video-torch¶

Segment Anything Model 2 (SAM2) from SAM2: Segment Anything in Images and Videos.

Details

  • Model name: segment-anything-2-hiera-small-video-torch

  • Model source: https://ai.meta.com/sam2/

  • Model size: 148.68 MB

  • Exposes embeddings? no

  • Tags: segment-anything, torch, zero-shot, video

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz
from fiftyone import ViewField as F

dataset = foz.load_zoo_dataset("quickstart-video", max_samples=2)

# Only retain detections in the first frame
(
    dataset
    .match_frames(F("frame_number") > 1)
    .set_field("frames.detections", None)
    .save()
)

model = foz.load_zoo_model("segment-anything-2-hiera-small-video-torch")

# Segment inside boxes and propagate to all frames
dataset.apply_model(
    model,
    label_field="segmentations",
    prompt_field="frames.detections",  # can contain Detections or Keypoints
)

session = fo.launch_app(dataset)

segment-anything-2-hiera-tiny-image-torch¶

Segment Anything Model 2 (SAM2) from SAM2: Segment Anything in Images and Videos.

Details

  • Model name: segment-anything-2-hiera-tiny-image-torch

  • Model source: https://ai.meta.com/sam2/

  • Model size: 148.68 MB

  • Exposes embeddings? no

  • Tags: segment-anything, torch, zero-shot

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("segment-anything-2-hiera-tiny-image-torch")

# Segment inside boxes
dataset.apply_model(
    model,
    label_field="segmentations",
    prompt_field="ground_truth",  # can contain Detections or Keypoints
)

# Full automatic segmentations
dataset.apply_model(model, label_field="auto")

session = fo.launch_app(dataset)

segment-anything-2-hiera-tiny-video-torch¶

Segment Anything Model 2 (SAM2) from SAM2: Segment Anything in Images and Videos.

Details

  • Model name: segment-anything-2-hiera-tiny-video-torch

  • Model source: https://ai.meta.com/sam2/

  • Model size: 148.68 MB

  • Exposes embeddings? no

  • Tags: segment-anything, torch, zero-shot, video

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz
from fiftyone import ViewField as F

dataset = foz.load_zoo_dataset("quickstart-video", max_samples=2)

# Only retain detections in the first frame
(
    dataset
    .match_frames(F("frame_number") > 1)
    .set_field("frames.detections", None)
    .save()
)

model = foz.load_zoo_model("segment-anything-2-hiera-tiny-video-torch")

# Segment inside boxes and propagate to all frames
dataset.apply_model(
    model,
    label_field="segmentations",
    prompt_field="frames.detections",  # can contain Detections or Keypoints
)

session = fo.launch_app(dataset)

segment-anything-vitb-torch¶

Segment Anything Model (SAM) from Segment Anything with ViT-B/16 backbone trained on SA-1B.

Details

  • Model name: segment-anything-vitb-torch

  • Model source: https://segment-anything.com

  • Model size: 715.34 KB

  • Exposes embeddings? no

  • Tags: segment-anything, sa-1b, torch, zero-shot

Requirements

  • Packages: torch, torchvision, segment-anything

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("segment-anything-vitb-torch")

# Segment inside boxes
dataset.apply_model(
    model,
    label_field="segmentations",
    prompt_field="ground_truth",  # can contain Detections or Keypoints
)

# Full automatic segmentations
dataset.apply_model(model, label_field="auto")

session = fo.launch_app(dataset)

segment-anything-vith-torch¶

Segment Anything Model (SAM) from Segment Anything with ViT-H/16 backbone trained on SA-1B.

Details

  • Model name: segment-anything-vith-torch

  • Model source: https://segment-anything.com

  • Model size: 4.78 MB

  • Exposes embeddings? no

  • Tags: segment-anything, sa-1b, torch, zero-shot

Requirements

  • Packages: torch, torchvision, segment-anything

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("segment-anything-vith-torch")

# Segment inside boxes
dataset.apply_model(
    model,
    label_field="segmentations",
    prompt_field="ground_truth",  # can contain Detections or Keypoints
)

# Full automatic segmentations
dataset.apply_model(model, label_field="auto")

session = fo.launch_app(dataset)

segment-anything-vitl-torch¶

Segment Anything Model (SAM) from Segment Anything with ViT-L/16 backbone trained on SA-1B.

Details

  • Model name: segment-anything-vitl-torch

  • Model source: https://segment-anything.com

  • Model size: 2.33 MB

  • Exposes embeddings? no

  • Tags: segment-anything, sa-1b, torch, zero-shot

Requirements

  • Packages: torch, torchvision, segment-anything

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("segment-anything-vitl-torch")

# Segment inside boxes
dataset.apply_model(
    model,
    label_field="segmentations",
    prompt_field="ground_truth",  # can contain Detections or Keypoints
)

# Full automatic segmentations
dataset.apply_model(model, label_field="auto")

session = fo.launch_app(dataset)

segmentation-transformer-torch¶

Hugging Face Transformers model for semantic segmentation.

Details

Requirements

  • Packages: torch, torchvision, transformers

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("segmentation-transformer-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

shufflenetv2-0.5x-imagenet-torch¶

ShuffleNetV2 model from ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design with 0.5x output channels trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("shufflenetv2-0.5x-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

shufflenetv2-1.0x-imagenet-torch¶

ShuffleNetV2 model from ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design with 1.0x output channels trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("shufflenetv2-1.0x-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

squeezenet-1.1-imagenet-torch¶

SqueezeNet 1.1 model from the official SqueezeNet repo trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("squeezenet-1.1-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

squeezenet-imagenet-torch¶

SqueezeNet model from SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("squeezenet-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

vgg11-bn-imagenet-torch¶

VGG-11 model from Very Deep Convolutional Networks for Large-Scale Image Recognition with batch normalization trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("vgg11-bn-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

vgg11-imagenet-torch¶

VGG-11 model from Very Deep Convolutional Networks for Large-Scale Image Recognition trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("vgg11-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

vgg13-bn-imagenet-torch¶

VGG-13 model from Very Deep Convolutional Networks for Large-Scale Image Recognition with batch normalization trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("vgg13-bn-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

vgg13-imagenet-torch¶

VGG-13 model from Very Deep Convolutional Networks for Large-Scale Image Recognition trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("vgg13-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

vgg16-bn-imagenet-torch¶

VGG-16 model from Very Deep Convolutional Networks for Large-Scale Image Recognition with batch normalization trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("vgg16-bn-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

vgg16-imagenet-torch¶

VGG-16 model from Very Deep Convolutional Networks for Large-Scale Image Recognition trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("vgg16-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

vgg19-bn-imagenet-torch¶

VGG-19 model from Very Deep Convolutional Networks for Large-Scale Image Recognition with batch normalization trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("vgg19-bn-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

vgg19-imagenet-torch¶

VGG-19 model from Very Deep Convolutional Networks for Large-Scale Image Recognition trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("vgg19-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

wide-resnet101-2-imagenet-torch¶

Wide ResNet-101-2 model from Wide Residual Networks trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("wide-resnet101-2-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

wide-resnet50-2-imagenet-torch¶

Wide ResNet-50-2 model from Wide Residual Networks trained on ImageNet.

Details

Requirements

  • Packages: torch, torchvision

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("wide-resnet50-2-imagenet-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolo-nas-torch¶

YOLO-NAS is an open-source training library for advanced computer vision models. It specializes in accuracy and efficiency, supporting tasks like object detection.

Details

Requirements

  • Packages: torch, torchvision, super-gradients

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolo-nas-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov10l-coco-torch¶

YOLOv10-L model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.2.0

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov10l-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov10m-coco-torch¶

YOLOv10-M model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.2.0

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov10m-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov10n-coco-torch¶

YOLOv10-N model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.2.0

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov10n-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov10s-coco-torch¶

YOLOv10-S model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.2.0

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov10s-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov10x-coco-torch¶

YOLOv10-X model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.2.0

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov10x-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov5l-coco-torch¶

Ultralytics YOLOv5l model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov5l-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov5m-coco-torch¶

Ultralytics YOLOv5m model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov5m-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov5n-coco-torch¶

Ultralytics YOLOv5n model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov5n-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov5s-coco-torch¶

Ultralytics YOLOv5s model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov5s-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov5x-coco-torch¶

Ultralytics YOLOv5x model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov5x-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8l-coco-torch¶

Ultralytics YOLOv8l model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8l-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8l-obb-dotav1-torch¶

YOLOv8l Oriented Bounding Box model.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.0

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8l-obb-dotav1-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8l-oiv7-torch¶

Ultralytics YOLOv8l model trained Open Images v7.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8l-oiv7-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8l-seg-coco-torch¶

Ultralytics YOLOv8l Segmentation model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8l-seg-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8l-world-torch¶

YOLOv8l-World model.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.0

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8l-world-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8m-coco-torch¶

Ultralytics YOLOv8m model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8m-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8m-obb-dotav1-torch¶

YOLOv8m Oriented Bounding Box model.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.0

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8m-obb-dotav1-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8m-oiv7-torch¶

Ultralytics YOLOv8m model trained Open Images v7.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8m-oiv7-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8m-seg-coco-torch¶

Ultralytics YOLOv8m Segmentation model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8m-seg-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8m-world-torch¶

YOLOv8m-World model.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.0

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8m-world-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8n-coco-torch¶

Ultralytics YOLOv8n model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8n-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8n-obb-dotav1-torch¶

YOLOv8n Oriented Bounding Box model.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.0

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8n-obb-dotav1-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8n-oiv7-torch¶

Ultralytics YOLOv8n model trained on Open Images v7.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8n-oiv7-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8n-seg-coco-torch¶

Ultralytics YOLOv8n Segmentation model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8n-seg-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8s-coco-torch¶

Ultralytics YOLOv8s model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8s-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8s-obb-dotav1-torch¶

YOLOv8s Oriented Bounding Box model.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.0

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8s-obb-dotav1-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8s-oiv7-torch¶

Ultralytics YOLOv8s model trained on Open Images v7.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8s-oiv7-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8s-seg-coco-torch¶

Ultralytics YOLOv8s Segmentation model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8s-seg-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8s-world-torch¶

YOLOv8s-World model.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.0

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8s-world-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8x-coco-torch¶

Ultralytics YOLOv8x model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8x-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8x-obb-dotav1-torch¶

YOLOv8x Oriented Bounding Box model.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.0

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8x-obb-dotav1-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8x-oiv7-torch¶

Ultralytics YOLOv8x model trained Open Images v7.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8x-oiv7-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8x-seg-coco-torch¶

Ultralytics YOLOv8x Segmentation model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8x-seg-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov8x-world-torch¶

YOLOv8x-World model.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.0

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov8x-world-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov9c-coco-torch¶

YOLOv9-C model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.0

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov9c-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov9c-seg-coco-torch¶

YOLOv9-C Segmentation model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.42

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov9c-seg-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov9e-coco-torch¶

YOLOv9-E model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.0

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov9e-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolov9e-seg-coco-torch¶

YOLOv9-E Segmentation model trained on COCO.

Details

Requirements

  • Packages: torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.42

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolov9e-seg-coco-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

zero-shot-classification-transformer-torch¶

Hugging Face Transformers model for zero-shot image classification.

Details

Requirements

  • Packages: torch, torchvision, transformers

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("zero-shot-classification-transformer-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

zero-shot-detection-transformer-torch¶

Hugging Face Transformers model for zero-shot object detection.

Details

Requirements

  • Packages: torch, torchvision, transformers

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("zero-shot-detection-transformer-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

TensorFlow models¶

centernet-hg104-1024-coco-tf2¶

CenterNet model from Objects as Points with the Hourglass-104 backbone trained on COCO resized to 1024x1024.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=2|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("centernet-hg104-1024-coco-tf2")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

centernet-hg104-512-coco-tf2¶

CenterNet model from Objects as Points with the Hourglass-104 backbone trained on COCO resized to 512x512.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=2|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("centernet-hg104-512-coco-tf2")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

centernet-mobilenet-v2-fpn-512-coco-tf2¶

CenterNet model from Objects as Points with the MobileNetV2 backbone trained on COCO resized to 512x512.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=2|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("centernet-mobilenet-v2-fpn-512-coco-tf2")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

centernet-resnet101-v1-fpn-512-coco-tf2¶

CenterNet model from Objects as Points with the ResNet-101v1 backbone + FPN trained on COCO resized to 512x512.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=2|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("centernet-resnet101-v1-fpn-512-coco-tf2")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

centernet-resnet50-v1-fpn-512-coco-tf2¶

CenterNet model from Objects as Points with the ResNet-50-v1 backbone + FPN trained on COCO resized to 512x512.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=2|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("centernet-resnet50-v1-fpn-512-coco-tf2")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

centernet-resnet50-v2-512-coco-tf2¶

CenterNet model from Objects as Points with the ResNet-50v2 backbone trained on COCO resized to 512x512.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=2|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("centernet-resnet50-v2-512-coco-tf2")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

deeplabv3-cityscapes-tf¶

DeepLabv3+ semantic segmentation model from Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation with Xception backbone trained on the Cityscapes dataset.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("deeplabv3-cityscapes-tf")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

deeplabv3-mnv2-cityscapes-tf¶

DeepLabv3+ semantic segmentation model from Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation with MobileNetV2 backbone trained on the Cityscapes dataset.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("deeplabv3-mnv2-cityscapes-tf")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

efficientdet-d0-512-coco-tf2¶

EfficientDet-D0 model from EfficientDet: Scalable and Efficient Object Detection trained on COCO resized to 512x512.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=2|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("efficientdet-d0-512-coco-tf2")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

efficientdet-d0-coco-tf1¶

EfficientDet-D0 model from EfficientDet: Scalable and Efficient Object Detection trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=1.14,<2

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=1.14,<2

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("efficientdet-d0-coco-tf1")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

efficientdet-d1-640-coco-tf2¶

EfficientDet-D1 model from EfficientDet: Scalable and Efficient Object Detection trained on COCO resized to 640x640.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=2|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("efficientdet-d1-640-coco-tf2")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

efficientdet-d1-coco-tf1¶

EfficientDet-D1 model from EfficientDet: Scalable and Efficient Object Detection trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=1.14,<2

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=1.14,<2

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("efficientdet-d1-coco-tf1")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

efficientdet-d2-768-coco-tf2¶

EfficientDet-D2 model from EfficientDet: Scalable and Efficient Object Detection trained on COCO resized to 768x768.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=2|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("efficientdet-d2-768-coco-tf2")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

efficientdet-d2-coco-tf1¶

EfficientDet-D2 model from EfficientDet: Scalable and Efficient Object Detection trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=1.14,<2

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=1.14,<2

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("efficientdet-d2-coco-tf1")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

efficientdet-d3-896-coco-tf2¶

EfficientDet-D3 model from EfficientDet: Scalable and Efficient Object Detection trained on COCO resized to 896x896.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=2|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("efficientdet-d3-896-coco-tf2")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

efficientdet-d3-coco-tf1¶

EfficientDet-D3 model from EfficientDet: Scalable and Efficient Object Detection trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=1.14,<2

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=1.14,<2

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("efficientdet-d3-coco-tf1")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

efficientdet-d4-1024-coco-tf2¶

EfficientDet-D4 model from EfficientDet: Scalable and Efficient Object Detection trained on COCO resized to 1024x1024.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=2|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("efficientdet-d4-1024-coco-tf2")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

efficientdet-d4-coco-tf1¶

EfficientDet-D4 model from EfficientDet: Scalable and Efficient Object Detection trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=1.14,<2

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=1.14,<2

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("efficientdet-d4-coco-tf1")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

efficientdet-d5-1280-coco-tf2¶

EfficientDet-D5 model from EfficientDet: Scalable and Efficient Object Detection trained on COCO resized to 1280x1280.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=2|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("efficientdet-d5-1280-coco-tf2")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

efficientdet-d5-coco-tf1¶

EfficientDet-D5 model from EfficientDet: Scalable and Efficient Object Detection trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=1.14,<2

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=1.14,<2

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("efficientdet-d5-coco-tf1")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

efficientdet-d6-1280-coco-tf2¶

EfficientDet-D6 model from EfficientDet: Scalable and Efficient Object Detection trained on COCO resized to 1280x1280.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=2|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("efficientdet-d6-1280-coco-tf2")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

efficientdet-d6-coco-tf1¶

EfficientDet-D6 model from EfficientDet: Scalable and Efficient Object Detection trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=1.14,<2

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=1.14,<2

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("efficientdet-d6-coco-tf1")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

efficientdet-d7-1536-coco-tf2¶

EfficientDet-D7 model from EfficientDet: Scalable and Efficient Object Detection trained on COCO resized to 1536x1536.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=2|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("efficientdet-d7-1536-coco-tf2")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

faster-rcnn-inception-resnet-atrous-v2-coco-tf¶

Faster R-CNN model from Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks atrous version with Inception backbone trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("faster-rcnn-inception-resnet-atrous-v2-coco-tf")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

faster-rcnn-inception-resnet-atrous-v2-lowproposals-coco-tf¶

Faster R-CNN model from Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks atrous version with low-proposals and Inception backbone trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("faster-rcnn-inception-resnet-atrous-v2-lowproposals-coco-tf")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

faster-rcnn-inception-v2-coco-tf¶

Faster R-CNN model from Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks with Inception v2 backbone trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("faster-rcnn-inception-v2-coco-tf")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

faster-rcnn-nas-coco-tf¶

Faster R-CNN model from Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks with NAS-net backbone trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("faster-rcnn-nas-coco-tf")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

faster-rcnn-nas-lowproposals-coco-tf¶

Faster R-CNN model from Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks with low-proposals and NAS-net backbone trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("faster-rcnn-nas-lowproposals-coco-tf")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

faster-rcnn-resnet101-coco-tf¶

Faster R-CNN model from Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks with ResNet-101 backbone trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("faster-rcnn-resnet101-coco-tf")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

faster-rcnn-resnet101-lowproposals-coco-tf¶

Faster R-CNN model from Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks with low-proposals and ResNet-101 backbone trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("faster-rcnn-resnet101-lowproposals-coco-tf")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

faster-rcnn-resnet50-coco-tf¶

Faster R-CNN model from Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks with ResNet-50 backbone trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("faster-rcnn-resnet50-coco-tf")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

faster-rcnn-resnet50-lowproposals-coco-tf¶

Faster R-CNN model from Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks with low-proposals and ResNet-50 backbone trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("faster-rcnn-resnet50-lowproposals-coco-tf")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

inception-resnet-v2-imagenet-tf1¶

Inception v2 model from Rethinking the Inception Architecture for Computer Vision trained on ImageNet.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow<2

  • GPU support

    • yes

    • Packages: tensorflow-gpu<2

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("inception-resnet-v2-imagenet-tf1")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

inception-v4-imagenet-tf1¶

Inception v4 model from Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning trained on ImageNet.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow<2

  • GPU support

    • yes

    • Packages: tensorflow-gpu<2

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("inception-v4-imagenet-tf1")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

mask-rcnn-inception-resnet-v2-atrous-coco-tf¶

Mask R-CNN model from Mask R-CNN atrous version with Inception backbone trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("mask-rcnn-inception-resnet-v2-atrous-coco-tf")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

mask-rcnn-inception-v2-coco-tf¶

Mask R-CNN model from Mask R-CNN with Inception backbone trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("mask-rcnn-inception-v2-coco-tf")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

mask-rcnn-resnet101-atrous-coco-tf¶

Mask R-CNN model from Mask R-CNN atrous version with ResNet-101 backbone trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("mask-rcnn-resnet101-atrous-coco-tf")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

mask-rcnn-resnet50-atrous-coco-tf¶

Mask R-CNN model from Mask R-CNN atrous version with ResNet-50 backbone trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("mask-rcnn-resnet50-atrous-coco-tf")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

mobilenet-v2-imagenet-tf1¶

MobileNetV2 model from MobileNetV2: Inverted Residuals and Linear Bottlenecks trained on ImageNet.

Details

  • Model name: mobilenet-v2-imagenet-tf1

  • Model source: None

  • Model size: 13.64 MB

  • Exposes embeddings? yes

  • Tags: classification, embeddings, logits, imagenet, tf1

Requirements

  • CPU support

    • yes

    • Packages: tensorflow<2

  • GPU support

    • yes

    • Packages: tensorflow-gpu<2

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("mobilenet-v2-imagenet-tf1")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

resnet-v1-50-imagenet-tf1¶

ResNet-50 v1 model from Deep Residual Learning for Image Recognition trained on ImageNet.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow<2

  • GPU support

    • yes

    • Packages: tensorflow-gpu<2

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("resnet-v1-50-imagenet-tf1")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

resnet-v2-50-imagenet-tf1¶

ResNet-50 v2 model from Deep Residual Learning for Image Recognition trained on ImageNet.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow<2

  • GPU support

    • yes

    • Packages: tensorflow-gpu<2

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("resnet-v2-50-imagenet-tf1")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

rfcn-resnet101-coco-tf¶

R-FCN object detection model from R-FCN: Object Detection via Region-based Fully Convolutional Networks with ResNet-101 backbone trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("rfcn-resnet101-coco-tf")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

ssd-inception-v2-coco-tf¶

Inception Single Shot Detector model from SSD: Single Shot MultiBox Detector trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("ssd-inception-v2-coco-tf")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

ssd-mobilenet-v1-coco-tf¶

Single Shot Detector model from SSD: Single Shot MultiBox Detector with MobileNetV1 backbone trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("ssd-mobilenet-v1-coco-tf")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

ssd-mobilenet-v1-fpn-640-coco17¶

MobileNetV1 model from MobileNetV2: Inverted Residuals and Linear Bottlenecks resized to 640x640.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=2|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("ssd-mobilenet-v1-fpn-640-coco17")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

ssd-mobilenet-v1-fpn-coco-tf¶

FPN Single Shot Detector model from SSD: Single Shot MultiBox Detector with MobileNetV1 backbone trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("ssd-mobilenet-v1-fpn-coco-tf")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

ssd-mobilenet-v2-320-coco17¶

MobileNetV2 model from MobileNetV2: Inverted Residuals and Linear Bottlenecks resized to 320x320.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow>=2|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("ssd-mobilenet-v2-320-coco17")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

ssd-resnet50-fpn-coco-tf¶

FPN Single Shot Detector model from SSD: Single Shot MultiBox Detector with ResNet-50 backbone trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow|tensorflow-macos

  • GPU support

    • yes

    • Packages: tensorflow-gpu|tensorflow>=2|tensorflow-macos

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("ssd-resnet50-fpn-coco-tf")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

vgg16-imagenet-tf1¶

VGG-16 model from Very Deep Convolutional Networks for Large-Scale Image Recognition trained on ImageNet.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow<2

  • GPU support

    • yes

    • Packages: tensorflow-gpu<2

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "imagenet-sample",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("vgg16-imagenet-tf1")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

yolo-v2-coco-tf1¶

YOLOv2 model from YOLO9000: Better, Faster, Stronger trained on COCO.

Details

Requirements

  • CPU support

    • yes

    • Packages: tensorflow<2

  • GPU support

    • yes

    • Packages: tensorflow-gpu<2

Example usage

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import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yolo-v2-coco-tf1")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)