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
File size: 392 Bytes
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"epoch": 80.0,
"eval_accuracy": 0.9541666666666667,
"eval_loss": 0.9975695610046387,
"eval_runtime": 4.9581,
"eval_samples_per_second": 242.029,
"eval_steps_per_second": 2.017,
"total_flos": 7.440697863438336e+18,
"train_loss": 1.5909362745285034,
"train_runtime": 1036.0381,
"train_samples_per_second": 115.826,
"train_steps_per_second": 0.193
} |