Instructions to use Spalne/vit-base-patch16-224-in21k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Spalne/vit-base-patch16-224-in21k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Spalne/vit-base-patch16-224-in21k") 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("Spalne/vit-base-patch16-224-in21k") model = AutoModelForImageClassification.from_pretrained("Spalne/vit-base-patch16-224-in21k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.06302083333333333, | |
| "eval_loss": NaN, | |
| "eval_runtime": 244.7131, | |
| "eval_samples_per_second": 196.148, | |
| "eval_steps_per_second": 3.065, | |
| "max_memory_allocated (GB)": 2.1, | |
| "memory_allocated (GB)": 1.49, | |
| "total_memory_available (GB)": 126.62 | |
| } |