Sam Vit Base

🚀 Model Card for Segment Anything Model (SAM) - ViT Base (ViT-B) version

The Segment Anything Model (SAM) is a powerful tool for image segmentation. It can generate high - quality object masks from input prompts like points or boxes, and is capable of generating masks for all objects in an image. Trained on a large dataset, it shows strong zero - shot performance on various segmentation tasks.

🚀 Quick Start

The Segment Anything Model (SAM) can be quickly utilized for image segmentation tasks. You can start by following the usage examples below to generate masks for your images.

✨ Features

• High - Quality Mask Generation: Produces high - quality object masks from input prompts such as points or boxes.
• Zero - Shot Performance: Demonstrates strong zero - shot performance on a variety of segmentation tasks.
• Large - Scale Training: Trained on a dataset of 11 million images and 1.1 billion masks.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

Prompted - Mask - Generation

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-base")
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")

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 localization of a window

inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to("cuda")
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

Among 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. For more details, refer to this notebook, which shows a walk throught of how to use the model, with a visual example!

Automatic - Mask - Generation

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

import matplotlib.pyplot as plt
from PIL import Image
import numpy as np

def show_mask(mask, ax, random_color=False):
    if random_color:
        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
    else:
        color = np.array([30 / 255, 144 / 255, 255 / 255, 0.6])
    h, w = mask.shape[-2:]
    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
    ax.imshow(mask_image)
    

plt.imshow(np.array(raw_image))
ax = plt.gca()
for mask in outputs["masks"]:
    show_mask(mask, ax=ax, random_color=True)
plt.axis("off")
plt.show()

📚 Documentation

Model Details

The SAM model is made up of 3 modules:

• The VisionEncoder: a VIT based image encoder. It computes the image embeddings using attention on patches of the image. Relative Positional Embedding is used.
• The PromptEncoder: generates embeddings for points and bounding boxes
• 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 fed
• The Neck: predicts the output masks based on the contextualized masks produced by the MaskDecoder.

Citation

If you use this model, please use the following BibTeX entry.

@article{kirillov2023segany,
  title={Segment Anything},
  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},
  journal={arXiv:2304.02643},
  year={2023}
}

📄 License

This model is licensed under the Apache - 2.0 license.