Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use 3b3r/vit_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 3b3r/vit_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="3b3r/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("3b3r/vit_model") model = AutoModelForImageClassification.from_pretrained("3b3r/vit_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 049ae4e308fe9e9921e97f9125394e5d78059495cca9522cd72e5f2fdc09d43a
- Size of remote file:
- 3.9 kB
- SHA256:
- 6f7ca5acebbf2107acf822a29451be33640dc87c5250e641d4300b99beab3354
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.