ethz/food101
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How to use lalfaro/my_test_food_model with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="lalfaro/my_test_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("lalfaro/my_test_food_model")
model = AutoModelForImageClassification.from_pretrained("lalfaro/my_test_food_model", device_map="auto")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 |
|---|---|---|---|---|
| No log | 1.0 | 1 | 4.6241 | 0.0 |
| No log | 2.0 | 2 | 4.4367 | 0.25 |
| No log | 3.0 | 3 | 4.3700 | 0.5 |
Base model
google/vit-base-patch16-224-in21k