ethz/food101
Viewer • Updated • 101k • 30.4k • 135
How to use gabrielganan/efficientnet_b1-food101 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="gabrielganan/efficientnet_b1-food101")
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("gabrielganan/efficientnet_b1-food101")
model = AutoModelForImageClassification.from_pretrained("gabrielganan/efficientnet_b1-food101")This model is a fine-tuned version of google/efficientnet-b1 on food101 dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 47 | 4.3674 | 0.1548 |
| No log | 2.0 | 94 | 3.1870 | 0.8915 |
| No log | 3.0 | 141 | 0.8758 | 0.9751 |
| No log | 4.0 | 188 | 0.1010 | 0.9858 |
| No log | 5.0 | 235 | 0.0503 | 0.9893 |
Base model
google/efficientnet-b1