Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use acaballero76/vit-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use acaballero76/vit-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="acaballero76/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("acaballero76/vit-model") model = AutoModelForImageClassification.from_pretrained("acaballero76/vit-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a58a2ca4f037041ed2ba7e8727ef1dff9208140356fcadfb6fbb110b85dc730d
- Size of remote file:
- 3.58 kB
- SHA256:
- fa1a7d6cea6c9c71307acb7c288fa3111b295bcbbc5692b926c26cffd0136e75
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.