Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use alexrods/vit_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alexrods/vit_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="alexrods/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("alexrods/vit_model") model = AutoModelForImageClassification.from_pretrained("alexrods/vit_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2113cf95c2402909529d61db2239117a6731c45ca85b56324f8b7ce5aff9d8c0
- Size of remote file:
- 3.39 kB
- SHA256:
- 83bd8628fe0fc5b6e85f0e4444c10d2458fe052ff13afd80f6f450622fcdd3c5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.