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
File size: 405 Bytes
c0ef3c7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"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"
}
|