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:
- 9ddf5983c36376ae5603da11b349ba46bef990096e43c51c22c9cf3d373c0931
- Size of remote file:
- 3.9 kB
- SHA256:
- 2329db1e605a562ded992242e805397993e3c27156ad646c57ffa77f4e07640e
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