Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use poolrf2001/platzi-vit-model-pool-river with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use poolrf2001/platzi-vit-model-pool-river with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="poolrf2001/platzi-vit-model-pool-river") 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("poolrf2001/platzi-vit-model-pool-river") model = AutoModelForImageClassification.from_pretrained("poolrf2001/platzi-vit-model-pool-river") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66815b525328f220dfb4352cf67e31b4818bf194f2d318294a853489c9ae0b83
- Size of remote file:
- 343 MB
- SHA256:
- 8a5bedd0157f779424799fed3a0b96ab7b5afb92d4aceb64afa85ec5347132f6
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