Instructions to use bryanzhou008/vit-base-patch16-224-in21k-finetuned-inaturalist with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bryanzhou008/vit-base-patch16-224-in21k-finetuned-inaturalist with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="bryanzhou008/vit-base-patch16-224-in21k-finetuned-inaturalist") 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("bryanzhou008/vit-base-patch16-224-in21k-finetuned-inaturalist") model = AutoModelForImageClassification.from_pretrained("bryanzhou008/vit-base-patch16-224-in21k-finetuned-inaturalist", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# vit-base-patch16-224-in21k-finetuned-inaturalist
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the
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It achieves the following results on the evaluation set:
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- Loss: 0.7703
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- Accuracy: 0.8542
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# vit-base-patch16-224-in21k-finetuned-inaturalist
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the inaturalist dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7703
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- Accuracy: 0.8542
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