Update README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: image-segmentation
+tags:
+- medical
+- segmentation
+datasets:
+- nielsr/breast-cancer
+---
+
+## Description :
+
+**Breast cancer segmentation** is the task of identifying and segmenting the breast tumor region in **medical images**, 
+such as mammograms and ultrasound images. This is an important task in the diagnosis and treatment of breast cancer, 
+as it helps clinicians to better understand the extent of the disease and plan appropriate interventions.
+
+**The Segment Anything Model (SAM)** is a state-of-the-art deep learning model for image segmentation. 
+SAM is a vision transformer-based model that has been shown to achieve excellent performance on a 
+variety of natural image segmentation tasks.
+
+## Base Model:
+
+**The 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 of 11 million 
+images and 1.1 billion masks and has strong zero-shot performance on a variety of segmentation tasks.
+**https://github.com/facebookresearch/segment-anything**
+
+
+## Get Started with the Model
+``` python
+device = "cuda" if torch.cuda.is_available() else "cpu"
+processor = SamProcessor.from_pretrained("wanglab/medsam-vit-base")
+model = SamModel.from_pretrained("ayoubkirouane/Breast-Cancer_SAM_v1").to(device)
+```