Instructions to use hf-internal-testing/tiny-random-SamModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SamModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="hf-internal-testing/tiny-random-SamModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMaskGeneration processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-SamModel") model = AutoModelForMaskGeneration.from_pretrained("hf-internal-testing/tiny-random-SamModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from hf-internal-testing/tiny-random-SamModel: direct link, hf CLI and curl.
- Browser
- Download file 446 Bytes
-
https://huggingface.co/hf-internal-testing/tiny-random-SamModel/resolve/bf789c6045248e7d9b68242ee5e7bf9bdc5cee77/preprocessor_config.json
- Command line
-
hf download hf://hf-internal-testing/tiny-random-SamModel@bf789c6045248e7d9b68242ee5e7bf9bdc5cee77/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/hf-internal-testing/tiny-random-SamModel/resolve/bf789c6045248e7d9b68242ee5e7bf9bdc5cee77/preprocessor_config.json
446 Bytes
| { | |
| "crop_size": 24, | |
| "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 | |
| }, | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "longest_edge": 24 | |
| } | |
| } | |