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")# pip install -U transformers accelerate # 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
update model card README.md
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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- pokemon-classification
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# pokemon_classifier
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pokemon-classification
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It achieves the following results on the evaluation set:
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- Loss: 8.0935
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- Accuracy: 0.0885
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---
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license: apache-2.0
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tags:
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- image-classification
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- generated_from_trainer
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datasets:
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- pokemon-classification
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# pokemon_classifier
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pokemon-classification and the full datasets.
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It achieves the following results on the evaluation set:
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- Loss: 8.0935
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- Accuracy: 0.0885
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