A2H0H0R1/plant-disease
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How to use A2H0H0R1/resnet-50-plant-disease with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="A2H0H0R1/resnet-50-plant-disease")
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("A2H0H0R1/resnet-50-plant-disease")
model = AutoModelForImageClassification.from_pretrained("A2H0H0R1/resnet-50-plant-disease", device_map="auto")This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder 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 |
|---|---|---|---|---|
| 3.4023 | 1.0 | 158 | 3.2949 | 0.4071 |
| 1.9184 | 2.0 | 316 | 1.5580 | 0.7788 |
| 0.94 | 3.0 | 474 | 0.7401 | 0.8761 |
| 0.6491 | 4.0 | 633 | 0.4772 | 0.9118 |
| 0.5516 | 5.0 | 791 | 0.3857 | 0.9242 |
| 0.5164 | 5.99 | 948 | 0.3609 | 0.9286 |
Base model
microsoft/resnet-50