Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use rdsmaia/pokemon_class_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rdsmaia/pokemon_class_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="rdsmaia/pokemon_class_model") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("rdsmaia/pokemon_class_model") model = AutoModelForImageClassification.from_pretrained("rdsmaia/pokemon_class_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rdsmaia/pokemon_class_model: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/rdsmaia/pokemon_class_model/resolve/main/training_args.bin
- Command line
-
hf download hf://rdsmaia/pokemon_class_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rdsmaia/pokemon_class_model/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- 5e3b9005073d398237e65473c02ef8f38cf5262e733fbf97e8a4ef6ed3469b31
- Size of remote file:
- 4.73 kB
- SHA256:
- 4df0c4c57e6f0d66315c87d13a01bb6058d3b872a777de28a1a303d079c3684d
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