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
Use the canonical RevCT social card
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by chrisvoncsefalvay - opened
README.md
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- multi-label-classification
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- calibration
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- safetensors
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thumbnail: https://
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model-index:
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- name: Rev CT
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results:
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encoder_license: MIT
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<p align="center">
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<a href="https://github.com/hcltech-robotics/rev-ct">Code and documentation</a><br>
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