Image Classification
Transformers
PyTorch
TensorBoard
cvt
Generated from Trainer
Eval Results (legacy)
Instructions to use LamaAldakhil/SL-CvT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LamaAldakhil/SL-CvT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="LamaAldakhil/SL-CvT") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("LamaAldakhil/SL-CvT") model = AutoModelForImageClassification.from_pretrained("LamaAldakhil/SL-CvT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0181123c7ccd2bd7e2a03d8eff9f89e7f55c7c5693c9e4cf80f99c180da484c1
- Size of remote file:
- 78.7 MB
- SHA256:
- 305f219ec1f152c9a4afbaba745adbd2f416235c90b3476536d5ac71d7f7b1f7
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