AI-Lab-Makerere/beans
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How to use ferno22/vit-beans-finetuned with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="ferno22/vit-beans-finetuned")
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("ferno22/vit-beans-finetuned")
model = AutoModelForImageClassification.from_pretrained("ferno22/vit-beans-finetuned")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 |
|---|---|---|---|---|
| 0.193 | 1.0 | 117 | 0.1099 | 0.9808 |
| 0.0462 | 2.0 | 234 | 0.0857 | 0.9808 |
| 0.0171 | 3.0 | 351 | 0.1237 | 0.9712 |
| 0.0123 | 4.0 | 468 | 0.1088 | 0.9712 |
| 0.0095 | 5.0 | 585 | 0.1135 | 0.9712 |
| 0.0081 | 6.0 | 702 | 0.1162 | 0.9712 |
| 0.0073 | 7.0 | 819 | 0.1158 | 0.9712 |
| 0.0066 | 8.0 | 936 | 0.1152 | 0.9712 |
| 0.0061 | 9.0 | 1053 | 0.1160 | 0.9712 |
| 0.0061 | 10.0 | 1170 | 0.1157 | 0.9712 |
Base model
google/vit-base-patch16-224-in21k