Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use eormeno12/platzi_vit_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eormeno12/platzi_vit_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="eormeno12/platzi_vit_model") 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("eormeno12/platzi_vit_model") model = AutoModelForImageClassification.from_pretrained("eormeno12/platzi_vit_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2ecd8cfb5451ed0c4c90b318f112fe36b30dfd89d44ab5daa663315c9e607287
- Size of remote file:
- 343 MB
- SHA256:
- 8e178b342f6a6f30f6757834049d3d9d62f9735286e798462eb24230c612ceb9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.