Image Classification
Transformers
PyTorch
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use RaymundoSGlz/vit_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RaymundoSGlz/vit_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="RaymundoSGlz/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("RaymundoSGlz/vit_model") model = AutoModelForImageClassification.from_pretrained("RaymundoSGlz/vit_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 28a5bdef5db285bc69ff4ca0dcedc1fcd712cb4470e9ab67bf9a28c61479a73c
- Size of remote file:
- 343 MB
- SHA256:
- 75d3dbecc6bd7f1dfc65f72de43e476cbc3a385d35e5a602a91403de2f670acd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.