facebook/sam-vit-base

1---2license: apache-2.03tags:4- vision5---6 7# Model Card for Segment Anything Model (SAM) - ViT Base (ViT-B) version8 9<p>10	<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-architecture.png" alt="Model architecture">11	<em> Detailed architecture of Segment Anything Model (SAM).</em>12</p>13 14 15#  Table of Contents16 170. [TL;DR](#TL;DR)181. [Model Details](#model-details)192. [Usage](#usage)203. [Citation](#citation)21 22# TL;DR23 24 25[Link to original repository](https://github.com/facebookresearch/segment-anything)26 27| <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-beancans.png" alt="Snow" width="600" height="600"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-dog-masks.png" alt="Forest" width="600" height="600"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-car-seg.png" alt="Mountains" width="600" height="600"> |28|---------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------|29 30 31The **Segment Anything Model (SAM)** produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a [dataset](https://segment-anything.com/dataset/index.html) of 11 million images and 1.1 billion masks, and has strong zero-shot performance on a variety of segmentation tasks.32The abstract of the paper states:33 34>  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.35 36**Disclaimer**: Content from **this** model card has been written by the Hugging Face team, and parts of it were copy pasted from the original [SAM model card](https://github.com/facebookresearch/segment-anything).37 38# Model Details39 40The SAM model is made up of 3 modules:41  - The `VisionEncoder`: a VIT based image encoder. It computes the image embeddings using attention on patches of the image. Relative Positional Embedding is used.42  - The `PromptEncoder`: generates embeddings for points and bounding boxes43  - The `MaskDecoder`: a two-ways transformer which performs cross attention between the image embedding and the point embeddings (->) and between the point embeddings and the image embeddings. The outputs are fed44  - The `Neck`: predicts the output masks based on the contextualized masks produced by the `MaskDecoder`.45# Usage46 47 48## Prompted-Mask-Generation49 50```python51from PIL import Image52import requests53from transformers import SamModel, SamProcessor54 55model = SamModel.from_pretrained("facebook/sam-vit-base")56processor = SamProcessor.from_pretrained("facebook/sam-vit-base")57 58img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"59raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")60input_points = [[[450, 600]]] # 2D localization of a window61```62 63 64```python65inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to("cuda")66outputs = model(**inputs)67masks = processor.image_processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu())68scores = outputs.iou_scores69```70Among other arguments to generate masks, you can pass 2D locations on the approximate position of your object of interest, a bounding box wrapping the object of interest (the format should be x, y coordinate of the top right and bottom left point of the bounding box), a segmentation mask. At this time of writing, passing a text as input is not supported by the official model according to [the official repository](https://github.com/facebookresearch/segment-anything/issues/4#issuecomment-1497626844).71For more details, refer to this notebook, which shows a walk throught of how to use the model, with a visual example! 72 73## Automatic-Mask-Generation74 75The model can be used for generating segmentation masks in a "zero-shot" fashion, given an input image. The model is automatically prompt with a grid of `1024` points76which are all fed to the model. 77 78The pipeline is made for automatic mask generation. The following snippet demonstrates how easy you can run it (on any device! Simply feed the appropriate `points_per_batch` argument)79```python80from transformers import pipeline81generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)82image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"83outputs = generator(image_url, points_per_batch = 256)84```85Now to display the image: 86```python87import matplotlib.pyplot as plt88from PIL import Image89import numpy as np90 91def show_mask(mask, ax, random_color=False):92    if random_color:93        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)94    else:95        color = np.array([30 / 255, 144 / 255, 255 / 255, 0.6])96    h, w = mask.shape[-2:]97    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)98    ax.imshow(mask_image)99    100 101plt.imshow(np.array(raw_image))102ax = plt.gca()103for mask in outputs["masks"]:104    show_mask(mask, ax=ax, random_color=True)105plt.axis("off")106plt.show()107```108 109 110# Citation111 112If you use this model, please use the following BibTeX entry.113 114```115@article{kirillov2023segany,116  title={Segment Anything},117  author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross},118  journal={arXiv:2304.02643},119  year={2023}120}121```