Image Classification
Transformers
TensorBoard
Safetensors
beit
Generated from Trainer
Eval Results (legacy)
Instructions to use ricardoSLabs/CIDAUTv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ricardoSLabs/CIDAUTv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ricardoSLabs/CIDAUTv2") 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/CIDAUTv2") model = AutoModelForImageClassification.from_pretrained("ricardoSLabs/CIDAUTv2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ricardoSLabs/CIDAUTv2: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/ricardoSLabs/CIDAUTv2/resolve/main/training_args.bin
- Command line
-
hf download hf://ricardoSLabs/CIDAUTv2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ricardoSLabs/CIDAUTv2/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- 8bb03ea87a371b82c99f7c6060003fc3e146f9250b4cf8aae2f3cfccf8ca56f2
- Size of remote file:
- 5.24 kB
- SHA256:
- b51edf477c9734abe3a62faf19195607849e0df832e7d15b63f445db4e62bd27
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