Instructions to use mmrech/MedSam-Breast-Cancer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mmrech/MedSam-Breast-Cancer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="mmrech/MedSam-Breast-Cancer")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMaskGeneration processor = AutoProcessor.from_pretrained("mmrech/MedSam-Breast-Cancer") model = AutoModelForMaskGeneration.from_pretrained("mmrech/MedSam-Breast-Cancer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from mmrech/MedSam-Breast-Cancer: direct link, hf CLI and curl.
- Browser
- Download file 544 Bytes
-
https://huggingface.co/mmrech/MedSam-Breast-Cancer/resolve/main/config.json
- Command line
-
hf download hf://mmrech/MedSam-Breast-Cancer/config.json
-
curl -L -o config.json https://huggingface.co/mmrech/MedSam-Breast-Cancer/resolve/main/config.json
544 Bytes
| { | |
| "_name_or_path": "MichaelSoloveitchik/MedSam-Breast-Cancer", | |
| "architectures": [ | |
| "SamModel" | |
| ], | |
| "initializer_range": 0.02, | |
| "mask_decoder_config": { | |
| "model_type": "" | |
| }, | |
| "model_type": "sam", | |
| "prompt_encoder_config": { | |
| "model_type": "" | |
| }, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.41.0", | |
| "vision_config": { | |
| "dropout": 0.0, | |
| "initializer_factor": 1.0, | |
| "intermediate_size": 6144, | |
| "model_type": "", | |
| "projection_dim": 512 | |
| }, | |
| "id2label": { | |
| "0": 0, | |
| "1": 1, | |
| "2": 2 | |
| } | |
| } | |