ethz/food101
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How to use ZachBeesley/food-classifier with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="ZachBeesley/food-classifier")
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("ZachBeesley/food-classifier")
model = AutoModelForImageClassification.from_pretrained("ZachBeesley/food-classifier")This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:
Image-classification model that can identify foods based on pictures
More information needed
More information needed
The following hyperparameters were used during training:
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|---|---|---|---|
| 2.6919 | 1.5372 | 0.848 | 0 |
| 1.1404 | 0.8059 | 0.881 | 1 |
| 0.6375 | 0.6164 | 0.865 | 2 |
| 0.4379 | 0.3822 | 0.915 | 3 |
| 0.3376 | 0.3213 | 0.921 | 4 |
Base model
google/vit-base-patch16-224-in21k