Image Classification
Transformers
PyTorch
TensorBoard
English
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use stbnlen/pokemon_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stbnlen/pokemon_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="stbnlen/pokemon_classifier") 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("stbnlen/pokemon_classifier") model = AutoModelForImageClassification.from_pretrained("stbnlen/pokemon_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3edaa984a2a9ca2a708bfdcb6d2a3fef837d4daa2e5d28bc7f7e6edeafe64e2d
- Size of remote file:
- 3.96 kB
- SHA256:
- f9e0a2dae0d9ea70c656db8040f2910e2273316283559a091eaced6336d64064
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.