Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use MaxP/vit-base-riego with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MaxP/vit-base-riego with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MaxP/vit-base-riego") 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("MaxP/vit-base-riego") model = AutoModelForImageClassification.from_pretrained("MaxP/vit-base-riego", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 73d608c4d2667ac689b1aa81e6ed3a42793ab12bd3e975f659ba045eeacdb59d
- Size of remote file:
- 343 MB
- SHA256:
- f56c963b463fdd577f918614cf92549777e8c98ea9ed2ec7784892eb2987e53c
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