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:
- b7686e01e3159bc9538a9bda9f71e5d50685eebb786a96fc99aa8eff57d800df
- Size of remote file:
- 343 MB
- SHA256:
- 91d2c3e5dd73bc609d7bd0d625c0101e30bdb5843bcebe2c276a8d790ab55641
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