Image Classification
Transformers
Safetensors
English
decision-model
medical-imaging
chest-ct
multi-label-classification
calibration
Eval Results (legacy)
Instructions to use hcltech-robotics/rev-ct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hcltech-robotics/rev-ct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hcltech-robotics/rev-ct") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hcltech-robotics/rev-ct", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download processor_config.json from hcltech-robotics/rev-ct: direct link, hf CLI and curl.
- Browser
- Download file 405 Bytes
-
https://huggingface.co/hcltech-robotics/rev-ct/resolve/main/processor_config.json
- Command line
-
hf download hf://hcltech-robotics/rev-ct/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/hcltech-robotics/rev-ct/resolve/main/processor_config.json
405 Bytes
| { | |
| "processor_class": "RevProcessor", | |
| "encoder": { | |
| "repo_id": "microsoft/colipri", | |
| "revision": "892e42ec363f6c561b89c459d60fcd95ac1a99bf", | |
| "package_version": "0.1.3", | |
| "pool": true, | |
| "project": true, | |
| "normalize": false, | |
| "input_size": [ | |
| 192, | |
| 192, | |
| 192 | |
| ], | |
| "dtype": "float32", | |
| "feature_storage_dtype": "float16" | |
| }, | |
| "preprocessing": "reference" | |
| } | |