# pip install -U transformers accelerate
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("Konradowsky/beans-deit-tiny")
model = AutoModelForImageClassification.from_pretrained("Konradowsky/beans-deit-tiny", device_map="auto")Quick Links
beans-deit-tiny
Fine-tuned facebook/deit-tiny-patch16-224 for image classification (3 classes: angular_leaf_spot, bean_rust, healthy).
Trained in Google Colab (GPU), experiments tracked with MLflow, winner selected by validation accuracy.
Training
- Dataset:
AI-Lab-Makerere/beans - Train / val / test: 1034 / 133 / 128
- Learning rate: 0.0002
- Epochs: 3
- Batch size: 32
- MLflow run:
run1_lr0.0002_ep3(a4050afef1c545cd8e87cc47cba1bd02)
Results
| split | accuracy | macro F1 |
|---|---|---|
| validation | 0.9624 | 0.9628 |
| test | 0.9453 | 0.9462 |
Per-class metrics (test)
| class | precision | recall | f1 | support |
|---|---|---|---|---|
| angular_leaf_spot | 1 | 0.8837 | 0.9383 | 43 |
| bean_rust | 0.86 | 1 | 0.9247 | 43 |
| healthy | 1 | 0.9524 | 0.9756 | 42 |
Usage
from transformers import pipeline
clf = pipeline("image-classification", model="Konradowsky/beans-deit-tiny")
clf("image.jpg", top_k=3)
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Base model
facebook/deit-tiny-patch16-224
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Konradowsky/beans-deit-tiny") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")