Instructions to use aehrc/cxrmate-multi-tf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aehrc/cxrmate-multi-tf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="aehrc/cxrmate-multi-tf", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("aehrc/cxrmate-multi-tf", trust_remote_code=True) model = AutoModel.from_pretrained("aehrc/cxrmate-multi-tf", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from aehrc/cxrmate-multi-tf: direct link, hf CLI and curl.
- Browser
- Download file 679 Bytes
-
https://huggingface.co/aehrc/cxrmate-multi-tf/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://aehrc/cxrmate-multi-tf/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/aehrc/cxrmate-multi-tf/resolve/main/preprocessor_config.json
679 Bytes
| { | |
| "_valid_processor_keys": [ | |
| "images", | |
| "do_resize", | |
| "size", | |
| "crop_pct", | |
| "resample", | |
| "do_rescale", | |
| "rescale_factor", | |
| "do_normalize", | |
| "image_mean", | |
| "image_std", | |
| "return_tensors", | |
| "data_format", | |
| "input_data_format" | |
| ], | |
| "crop_pct": 0.875, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "ConvNextFeatureExtractor", | |
| "image_mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "image_processor_type": "ConvNextFeatureExtractor", | |
| "image_std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "shortest_edge": 384 | |
| } | |
| } | |