ethz/food101
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How to use RickyIG/image_classification with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="RickyIG/image_classification")
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("RickyIG/image_classification")
model = AutoModelForImageClassification.from_pretrained("RickyIG/image_classification")This model is a fine-tuned version of google/vit-base-patch16-224-in21k 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 |
|---|---|---|---|---|
| 2.7254 | 0.99 | 62 | 2.5418 | 0.819 |
| 1.8131 | 2.0 | 125 | 1.8025 | 0.852 |
| 1.5991 | 2.98 | 186 | 1.6367 | 0.889 |
Base model
google/vit-base-patch16-224-in21k