# segment-anything-3-image-torch Open-vocabulary instance segmentation that finds and segments all objects matching a text concept like ‘person’ or ‘yellow school bus’. **Details** - Model name: `segment-anything-3-image-torch` - Model source: [https://github.com/facebookresearch/sam3](https://github.com/facebookresearch/sam3) - Model author: Nicolas Carion, Laura Gustafson, Yuan-Ting Hu, et al. - Model license: SAM License - Model size: 3.45 GB - Exposes embeddings? no - Tags: `segment-anything, torch, zero-shot, transformer, official` **Requirements** - Packages: `torch, torchvision, sam3` - CPU support - yes - GPU support - yes **Example usage** ```python 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, ) # Concept mode: find and segment all objects matching a text prompt model = foz.load_zoo_model( "segment-anything-3-image-torch", operation_mode="concept", classes=["person", "car", "dog"], ) dataset.apply_model(model, label_field="segmentations_concept") # Exemplar concept mode: find and segment object with text prompts and exemplar Detections model = foz.load_zoo_model( "segment-anything-3-image-torch", operation_mode="concept", classes=["person"], ) dataset.apply_model( model, label_field="segmentations_concept_with_exemplar", prompt_field="person_detections", # contains exemplar Detections with positive / negative labels ) # Visual mode: segment inside boxes or using keypoints model = foz.load_zoo_model( "segment-anything-3-image-torch", operation_mode="visual", ) dataset.apply_model( model, label_field="segmentations", prompt_field="ground_truth", # can contain Detections or Keypoints ) session = fo.launch_app(dataset) ```