Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use JuandaBula/vit-model-juan-bula with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JuandaBula/vit-model-juan-bula with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="JuandaBula/vit-model-juan-bula") 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("JuandaBula/vit-model-juan-bula") model = AutoModelForImageClassification.from_pretrained("JuandaBula/vit-model-juan-bula", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cfa6a114c505b66a077a1f1c121c2941a6b92f0ee49cde89ebff2b1f44e59f5f
- Size of remote file:
- 3.39 kB
- SHA256:
- a42628cdd00386057b442aebc5bd5002023a7f08afdb631abaa8f5544875e294
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.