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Update model card (#1)

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- Update model card (2869890888ba16bccc505194b4d5353caf4fd62b)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

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  1. README.md +34 -5
README.md CHANGED
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  ---
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  license: apache-2.0
 
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  library_name: sam2
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  ---
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-
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  Repository for SAM 2: Segment Anything in Images and Videos, a foundation model towards solving promptable visual segmentation in images and videos from FAIR. See the [SAM 2 paper](https://arxiv.org/abs/2408.00714) for more information.
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-
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  The official code is publicly release in this [repo](https://github.com/facebookresearch/segment-anything-2/).
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- To download the SAM 2 (Hiera-B+) checkpoint:
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  ```
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- from huggingface_hub import hf_hub_download
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- hf_hub_download(repo_id = "facebook/sam2-hiera-base-plus", filename="sam2_hiera_base_plus.pt", local_dir = "./")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
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  ### Citation
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  To cite the paper, model, or software, please use the below:
 
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  ---
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  license: apache-2.0
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+ pipeline_tag: mask-generation
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  library_name: sam2
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  ---
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  Repository for SAM 2: Segment Anything in Images and Videos, a foundation model towards solving promptable visual segmentation in images and videos from FAIR. See the [SAM 2 paper](https://arxiv.org/abs/2408.00714) for more information.
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  The official code is publicly release in this [repo](https://github.com/facebookresearch/segment-anything-2/).
 
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+ ## Usage
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+
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+ For image prediction:
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+
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+ ```python
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+ import torch
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+ from sam2.sam2_image_predictor import SAM2ImagePredictor
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+
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+ predictor = SAM2ImagePredictor.from_pretrained("facebook/sam2-hiera-base-plus")
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+
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+ with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
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+ predictor.set_image(<your_image>)
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+ masks, _, _ = predictor.predict(<input_prompts>)
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  ```
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+
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+ For video prediction:
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+
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+ ```python
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+ import torch
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+ from sam2.sam2_video_predictor import SAM2VideoPredictor
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+
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+ predictor = SAM2VideoPredictor.from_pretrained("facebook/sam2-hiera-base-plus")
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+
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+ with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
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+ state = predictor.init_state(<your_video>)
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+
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+ # add new prompts and instantly get the output on the same frame
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+ frame_idx, object_ids, masks = predictor.add_new_points_or_box(state, <your_prompts>):
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+
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+ # propagate the prompts to get masklets throughout the video
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+ for frame_idx, object_ids, masks in predictor.propagate_in_video(state):
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+ ...
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  ```
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+ Refer to the [demo notebooks](https://github.com/facebookresearch/segment-anything-2/tree/main/notebooks) for details.
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+
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  ### Citation
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  To cite the paper, model, or software, please use the below: