ethz/food101
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How to use ManishW/vit-base-patch16-224-food101-v1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="ManishW/vit-base-patch16-224-food101-v1")
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("ManishW/vit-base-patch16-224-food101-v1")
model = AutoModelForImageClassification.from_pretrained("ManishW/vit-base-patch16-224-food101-v1")This model is a fine-tuned version of google/vit-base-patch16-224 on the food101 dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0682 | 0.99 | 31 | 0.3073 | 0.908 |
| 0.0425 | 1.98 | 62 | 0.2663 | 0.915 |
| 0.0262 | 2.98 | 93 | 0.2173 | 0.928 |
| 0.0446 | 4.0 | 125 | 0.2195 | 0.937 |
| 0.0642 | 4.96 | 155 | 0.2359 | 0.924 |