ethz/food101
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How to use suncy13/my_awesome_food_model with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="suncy13/my_awesome_food_model")
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("suncy13/my_awesome_food_model")
model = AutoModelForImageClassification.from_pretrained("suncy13/my_awesome_food_model")This model is a fine-tuned version of facebook/dinov2-small-imagenet1k-1-layer 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 |
|---|---|---|---|---|
| 0.4769 | 0.99 | 62 | 0.5877 | 0.805 |
| 0.3517 | 2.0 | 125 | 0.3854 | 0.872 |
| 0.2431 | 2.98 | 186 | 0.2296 | 0.933 |
Base model
facebook/dinov2-small-imagenet1k-1-layer