Instructions to use hf-internal-testing/tiny-random-MaskFormerModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MaskFormerModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-MaskFormerModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-MaskFormerModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-MaskFormerModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from hf-internal-testing/tiny-random-MaskFormerModel: direct link, hf CLI and curl.
- Browser
- Download file 423 Bytes
-
https://huggingface.co/hf-internal-testing/tiny-random-MaskFormerModel/resolve/473b54a464bc0ccee29bc23b4f6610f32eec05af/preprocessor_config.json
- Command line
-
hf download hf://hf-internal-testing/tiny-random-MaskFormerModel@473b54a464bc0ccee29bc23b4f6610f32eec05af/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/hf-internal-testing/tiny-random-MaskFormerModel/resolve/473b54a464bc0ccee29bc23b4f6610f32eec05af/preprocessor_config.json
423 Bytes
| { | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "MaskFormerFeatureExtractor", | |
| "ignore_index": 255, | |
| "image_mean": [ | |
| 0.48500001430511475, | |
| 0.4560000002384186, | |
| 0.4059999883174896 | |
| ], | |
| "image_std": [ | |
| 0.2290000021457672, | |
| 0.2239999920129776, | |
| 0.22499999403953552 | |
| ], | |
| "max_size": 2560, | |
| "reduce_labels": false, | |
| "resample": 2, | |
| "size": 640, | |
| "size_divisibility": 32 | |
| } | |