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:
- 5631aa8deac7c592e54cf471aff1cab4b00214cacda38f32b6e0774f6707d0c9
- Size of remote file:
- 343 MB
- SHA256:
- 9c2bcb3e1634c7b875bb603e0b9832d59419d8e1a163e8aaf5a2e693221aefbf
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