Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use platzi/platzi-vit-model-jonathan-narvaez with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use platzi/platzi-vit-model-jonathan-narvaez with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="platzi/platzi-vit-model-jonathan-narvaez") 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("platzi/platzi-vit-model-jonathan-narvaez") model = AutoModelForImageClassification.from_pretrained("platzi/platzi-vit-model-jonathan-narvaez") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 943a3d7b5baef9470a38ffa2df3b9f65b321e393fe424f4f6a5c4454b519d23b
- Size of remote file:
- 3.96 kB
- SHA256:
- a8a1c3819f7cd0cf5ff673482629cea3a4ef5f3548cb96cf62f4608e7215403a
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