158 lines
7.3 KiB
Markdown
158 lines
7.3 KiB
Markdown
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# SAM
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## Overview
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SAM (Segment Anything Model) was proposed in [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
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The model can be used to predict segmentation masks of any object of interest given an input image.
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The abstract from the paper is the following:
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*We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks. We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive -- often competitive with or even superior to prior fully supervised results. We are releasing the Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and 11M images at [https://segment-anything.com](https://segment-anything.com) to foster research into foundation models for computer vision.*
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Tips:
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- The model predicts binary masks that states the presence or not of the object of interest given an image.
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- The model predicts much better results if input 2D points and/or input bounding boxes are provided
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- You can prompt multiple points for the same image, and predict a single mask.
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- Fine-tuning the model is not supported yet
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- According to the paper, textual input should be also supported. However, at this time of writing this seems to be not supported according to [the official repository](https://github.com/facebookresearch/segment-anything/issues/4#issuecomment-1497626844).
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This model was contributed by [ybelkada](https://huggingface.co/ybelkada) and [ArthurZ](https://huggingface.co/ArthurZ).
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The original code can be found [here](https://github.com/facebookresearch/segment-anything).
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Below is an example on how to run mask generation given an image and a 2D point:
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```python
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import torch
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from PIL import Image
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import requests
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from transformers import SamModel, SamProcessor
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model = SamModel.from_pretrained("facebook/sam-vit-huge").to(device)
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processor = SamProcessor.from_pretrained("facebook/sam-vit-huge")
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img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
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raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
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input_points = [[[450, 600]]] # 2D location of a window in the image
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inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to(device)
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with torch.no_grad():
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outputs = model(**inputs)
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masks = processor.image_processor.post_process_masks(
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outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()
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)
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scores = outputs.iou_scores
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```
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You can also process your own masks alongside the input images in the processor to be passed to the model.
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```python
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import torch
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from PIL import Image
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import requests
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from transformers import SamModel, SamProcessor
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model = SamModel.from_pretrained("facebook/sam-vit-huge").to(device)
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processor = SamProcessor.from_pretrained("facebook/sam-vit-huge")
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img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
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raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
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mask_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
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segmentation_map = Image.open(requests.get(mask_url, stream=True).raw).convert("RGB")
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input_points = [[[450, 600]]] # 2D location of a window in the image
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inputs = processor(raw_image, input_points=input_points, segmentation_maps=mask, return_tensors="pt").to(device)
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with torch.no_grad():
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outputs = model(**inputs)
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masks = processor.image_processor.post_process_masks(
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outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()
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)
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scores = outputs.iou_scores
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```
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## Resources
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A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with SAM.
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- [Demo notebook](https://github.com/huggingface/notebooks/blob/main/examples/segment_anything.ipynb) for using the model.
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- [Demo notebook](https://github.com/huggingface/notebooks/blob/main/examples/automatic_mask_generation.ipynb) for using the automatic mask generation pipeline.
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- [Demo notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/SAM/Run_inference_with_MedSAM_using_HuggingFace_Transformers.ipynb) for inference with MedSAM, a fine-tuned version of SAM on the medical domain. 🌎
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- [Demo notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/SAM/Fine_tune_SAM_(segment_anything)_on_a_custom_dataset.ipynb) for fine-tuning the model on custom data. 🌎
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## SlimSAM
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SlimSAM, a pruned version of SAM, was proposed in [0.1% Data Makes Segment Anything Slim](https://arxiv.org/abs/2312.05284) by Zigeng Chen et al. SlimSAM reduces the size of the SAM models considerably while maintaining the same performance.
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Checkpoints can be found on the [hub](https://huggingface.co/models?other=slimsam), and they can be used as a drop-in replacement of SAM.
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## Grounded SAM
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One can combine [Grounding DINO](grounding-dino) with SAM for text-based mask generation as introduced in [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://arxiv.org/abs/2401.14159). You can refer to this [demo notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Grounding%20DINO/GroundingDINO_with_Segment_Anything.ipynb) 🌍 for details.
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/grounded_sam.png"
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alt="drawing" width="900"/>
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<small> Grounded SAM overview. Taken from the <a href="https://github.com/IDEA-Research/Grounded-Segment-Anything">original repository</a>. </small>
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## SamConfig
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[[autodoc]] SamConfig
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## SamVisionConfig
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[[autodoc]] SamVisionConfig
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## SamMaskDecoderConfig
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[[autodoc]] SamMaskDecoderConfig
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## SamPromptEncoderConfig
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[[autodoc]] SamPromptEncoderConfig
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## SamProcessor
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[[autodoc]] SamProcessor
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## SamImageProcessor
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[[autodoc]] SamImageProcessor
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## SamModel
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[[autodoc]] SamModel
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- forward
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## TFSamModel
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[[autodoc]] TFSamModel
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- call
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