Instructions to use facebook/sam-vit-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/sam-vit-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="facebook/sam-vit-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMaskGeneration processor = AutoProcessor.from_pretrained("facebook/sam-vit-large") model = AutoModelForMaskGeneration.from_pretrained("facebook/sam-vit-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from facebook/sam-vit-large: direct link, hf CLI and curl.
- Browser
- Download file 466 Bytes
-
https://huggingface.co/facebook/sam-vit-large/resolve/refs%2Fpr%2F2/preprocessor_config.json
- Command line
-
hf download hf://facebook/sam-vit-large@refs/pr/2/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/facebook/sam-vit-large/resolve/refs%2Fpr%2F2/preprocessor_config.json
466 Bytes
| { | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_pad": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "image_processor_type": "SamImageProcessor", | |
| "image_std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "pad_size": { | |
| "height": 1024, | |
| "width": 1024 | |
| }, | |
| "processor_class": "SamProcessor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "longest_edge": 1024 | |
| } | |
| } | |