Image Classification
Transformers
TensorBoard
Safetensors
beit
Generated from Trainer
Eval Results (legacy)
Instructions to use ricardoSLabs/CIDAUT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ricardoSLabs/CIDAUT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ricardoSLabs/CIDAUT") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ricardoSLabs/CIDAUT") model = AutoModelForImageClassification.from_pretrained("ricardoSLabs/CIDAUT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ricardoSLabs/CIDAUT: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/ricardoSLabs/CIDAUT/resolve/main/training_args.bin
- Command line
-
hf download hf://ricardoSLabs/CIDAUT/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ricardoSLabs/CIDAUT/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- 908fce0b5f8b334d3f08a3ec54510fa022ef369a3da6a5e40e2b715f20327de2
- Size of remote file:
- 5.24 kB
- SHA256:
- 4cdc69f3930059c893fbd9e42cd12a3eeaef38e7d1ffd8c18810d658c087ec81
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