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:
- 27b048213bd03eec334eee0c74315aee068c1f9ae49ea6844222f3fdf13d8850
- Size of remote file:
- 3.45 kB
- SHA256:
- d9e7017b55ae27b1def0af2faa3abc0a2a670641826bcd2595e6c83fd8157152
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