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") - Inference
- Notebooks
- Google Colab
- Kaggle
Download assets/classification-comparison.png from hcltech-robotics/rev-ct: direct link, hf CLI and curl.
- Browser
- Download file 102 kB
-
https://huggingface.co/hcltech-robotics/rev-ct/resolve/main/assets/classification-comparison.png
- Command line
-
hf download hf://hcltech-robotics/rev-ct/assets/classification-comparison.png
-
curl -L -o classification-comparison.png https://huggingface.co/hcltech-robotics/rev-ct/resolve/main/assets/classification-comparison.png
102 kB

- Xet hash:
- 2b4fe0488bf9f1fda1df701fd771425cc107d7c4df60dc1154a95eb4c53d9c80
- Size of remote file:
- 102 kB
- SHA256:
- df35864c1f034a2d063b9d90d8feac33d74972d7d5e875bb2304a36ecf6a98a1
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