Note

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GitHub Repo

Florence2 FiftyOne Remote Model Zoo Implementation#

As of now, Florence2 only works in transformers<4.50.0#

This repository provides a FiftyOne Model Zoo implementation for Florence-2, Microsoft’s powerful multimodal model. The implementation allows seamless integration of Florence-2’s capabilities with FiftyOne’s computer vision tools.

NOTE: Due to recent changes in Transformers 4.50.0 (which are to be patched by Hugging Face) please ensure you have transformers<=4.49.0 installed before running the model

Features#

Florence-2 supports multiple vision-language tasks through this implementation:

  1. Image Captioning

    • Three detail levels: basic, detailed, and more_detailed

    • Generates natural language descriptions of images

  2. Optical Character Recognition (OCR)

    • Text extraction from images

    • Optional region-based detection with bounding boxes

  3. Object Detection

    • Multiple detection modes:

      • Standard object detection

      • Dense region captioning

      • Region proposal generation

      • Open vocabulary detection (with custom prompts)

  4. Phrase Grounding

    • Links phrases to specific regions in images

    • Requires a caption or text prompt

  5. Referring Expression Segmentation

    • Segments objects based on natural language descriptions

    • Returns polygon contours for the referenced objects

Installation#

pip install fiftyone
pip install transformers<=4.49.0

Usage#

Register and download the model (one-time setup)#


import fiftyone.zoo as foz

foz.register_zoo_model_source("https://github.com/harpreetsahota204/florence2", overwrite=True)

foz.download_zoo_model("https://github.com/harpreetsahota204/florence2", model_name="microsoft/Florence-2-base-ft")

Load the model#

model = foz.load_zoo_model(
   "microsoft/Florence-2-base-ft",
    # install_requirements=True #if you are using for the first time and need to download reuirement,
    # ensure_requirements=True #  ensure any requirements are installed before loading the model
   )

There are four available Florence2 checkpoints:

  1. microsoft/Florence-2-base - Base model

  2. microsoft/Florence-2-large - Large model

  3. microsoft/Florence-2-base-ft - Fine-tuned base model

  4. microsoft/Florence-2-large-ft - Fine-tuned large model

Usage#

Switching Between Operations#

The same model instance can be used for different operations by simply changing its properties:

Image Captioning#


model.operation = "caption"
model.detail_level = "detailed"  # Options: "basic", "detailed", "more_detailed"
dataset.apply_model(model, label_field="captions")

OCR#

model.operation = "ocr"
model.store_region_info = True # True will return detected bounding boxes, False will return just the text
dataset.apply_model(model, label_field="text_detections")

Object Detection#

Florence-2 supports four different types of detection operations, each serving a different purpose:

1. Standard Detection (detection_type="detection")#
model.operation = "detection"
model.detection_type = "detection"
dataset.apply_model(model, label_field="standard_detections")

  • Basic object detection mode

  • Detects common objects in the image

  • Returns bounding boxes with object labels

2. Dense Region Captioning (detection_type="dense_region_caption")#
model.operation = "detection"
model.detection_type = "dense_region_caption"
dataset.apply_model(model, label_field="region_captions")

  • Generates detailed captions for different regions in the image

  • Each region comes with a descriptive caption

  • Useful for understanding scene composition

3. Region Proposal (detection_type="region_proposal")#
model.operation = "detection"
model.detection_type = "region_proposal"
dataset.apply_model(model, label_field="region_proposals")

  • Generates potential regions of interest

  • Identifies areas that might contain objects

  • Useful as a preprocessing step for other tasks

4. Open Vocabulary Detection (detection_type="open_vocabulary_detection")#
model.operation = "detection"
model.detection_type = "open_vocabulary_detection"
model.prompt = "Find all the red cars and blue bicycles"
dataset.apply_model(model, label_field="custom_detections")

Phrase Grounding#

model.operation = "phrase_grounding"
model.prompt = "person wearing a red hat"
dataset.apply_model(model, label_field="grounding")

Switch to Segmentation#

model.operation = "segmentation"
model.prompt = "the cat sleeping on the couch"
dataset.apply_model(model, label_field="segments")

You can look at the example notebook for detailed usage syntax.

Output Formats#

  • Captions: Returns string: Returns str

    • Natural language text responses in English

  • OCR: Returns either string or fiftyone.core.labels.Detections

    • Bounding box coordinates are normalized to [0,1] x [0,1]

  • Detection: Returns fiftyone.core.labels.Detections

    • Bounding box coordinates are normalized to [0,1] x [0,1]

  • Phrase Grounding: Returns fiftyone.core.labels.Detections

    • Bounding box coordinates are normalized to [0,1] x [0,1]

  • Segmentation: Returns fiftyone.core.labels.Polylines

    • Normalized point coordinates [0,1] x [0,1]

Device Support#

The implementation automatically selects the appropriate device:

  • CUDA if available

  • Apple M1/M2 MPS if available

  • CPU as fallback

Citation#

@article{xiao2023florence,
  title={Florence-2: Advancing a unified representation for a variety of vision tasks},
  author={Xiao, Bin and Wu, Haiping and Xu, Weijian and Dai, Xiyang and Hu, Houdong and Lu, Yumao and Zeng, Michael and Liu, Ce and Yuan, Lu},
  journal={arXiv preprint arXiv:2311.06242},
  year={2023}
}