AI-Lab-Makerere/beans
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How to use abelkrw/beans_image_classification with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="abelkrw/beans_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("abelkrw/beans_image_classification")
model = AutoModelForImageClassification.from_pretrained("abelkrw/beans_image_classification")This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans 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 | 0.94 | 8 | 1.3666 | 0.66 |
| 0.3651 | 2.0 | 17 | 0.3823 | 0.84 |
| 0.5622 | 2.94 | 25 | 0.3333 | 0.86 |
| 0.3373 | 4.0 | 34 | 0.1274 | 0.97 |
| 0.2055 | 4.94 | 42 | 0.1882 | 0.93 |
| 0.1819 | 6.0 | 51 | 0.2265 | 0.9 |
| 0.1819 | 6.94 | 59 | 0.2395 | 0.91 |
| 0.2428 | 8.0 | 68 | 0.1451 | 0.97 |
| 0.1305 | 8.94 | 76 | 0.1554 | 0.94 |
| 0.1203 | 9.41 | 80 | 0.1705 | 0.92 |
Base model
google/vit-base-patch16-224-in21k