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# SAM

## Overview

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.

The model can be used to predict segmentation masks of any object of interest given an input image. 

![example image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-output.png)

The abstract from the paper is the following:

*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.*

Tips:

- The model predicts binary masks that states the presence or not of the object of interest given an image.
- The model predicts much better results if input 2D points and/or input bounding boxes are provided
- You can prompt multiple points for the same image, and predict a single mask. 
- Fine-tuning the model is not supported yet
- 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). 


This model was contributed by [ybelkada](https://huggingface.co/ybelkada) and [ArthurZ](https://huggingface.co/ArthurZ).
The original code can be found [here](https://github.com/facebookresearch/segment-anything).

Below is an example on how to run mask generation given an image and a 2D point:

```python
import torch
from PIL import Image
import requests
from transformers import SamModel, SamProcessor

device = "cuda" if torch.cuda.is_available() else "cpu"
model = SamModel.from_pretrained("facebook/sam-vit-huge").to(device)
processor = SamProcessor.from_pretrained("facebook/sam-vit-huge")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]]  # 2D location of a window in the image

inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to(device)
with torch.no_grad():
    outputs = model(**inputs)

masks = processor.image_processor.post_process_masks(
    outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()
)
scores = outputs.iou_scores
```

You can also process your own masks alongside the input images in the processor to be passed to the model.

```python
import torch
from PIL import Image
import requests
from transformers import SamModel, SamProcessor

device = "cuda" if torch.cuda.is_available() else "cpu"
model = SamModel.from_pretrained("facebook/sam-vit-huge").to(device)
processor = SamProcessor.from_pretrained("facebook/sam-vit-huge")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
mask_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
segmentation_map = Image.open(requests.get(mask_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]]  # 2D location of a window in the image

inputs = processor(raw_image, input_points=input_points, segmentation_maps=mask, return_tensors="pt").to(device)
with torch.no_grad():
    outputs = model(**inputs)

masks = processor.image_processor.post_process_masks(
    outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()
)
scores = outputs.iou_scores
```

## Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with SAM.

- [Demo notebook](https://github.com/huggingface/notebooks/blob/main/examples/segment_anything.ipynb) for using the model.
- [Demo notebook](https://github.com/huggingface/notebooks/blob/main/examples/automatic_mask_generation.ipynb) for using the automatic mask generation pipeline.
- [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. 🌎
- [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. 🌎

## SlimSAM

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.

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.

## SamConfig

[[autodoc]] SamConfig

## SamVisionConfig

[[autodoc]] SamVisionConfig

## SamMaskDecoderConfig

[[autodoc]] SamMaskDecoderConfig

## SamPromptEncoderConfig

[[autodoc]] SamPromptEncoderConfig


## SamProcessor

[[autodoc]] SamProcessor


## SamImageProcessor

[[autodoc]] SamImageProcessor


## SamModel

[[autodoc]] SamModel
    - forward


## TFSamModel

[[autodoc]] TFSamModel
    - call
