| { |
| "model_type": "resnet", |
| "task": "image-classification", |
| "framework": "pytorch", |
| "pipeline_tag": "image-classification", |
| "num_classes": 3, |
| "class_labels": ["fa", "p_def", "blb"], |
| "input_shape": [224, 224, 3], |
| "preprocessing": { |
| "resize": 256, |
| "center_crop": 224, |
| "normalize": [0.485, 0.456, 0.406], |
| "normalize_std": [0.229, 0.224, 0.225] |
| }, |
| "metrics": { |
| "validation_accuracy": "93.82%", |
| "per_class_accuracy": { |
| "fa": "100.00%", |
| "p_def": "86.21%", |
| "blb": "95.00%" |
| } |
| }, |
| "license": "apache-2.0", |
| "tags": [ |
| "image-classification", |
| "crop-anomaly-detection", |
| "agriculture", |
| "resnet50", |
| "deep-learning" |
| ], |
| "description": "ResNet50 model for 3-class anomaly detection. The model classifies images as Fall Armyworm (fa), Phosphorus Deficiency (p_def), or Bacterial Leaf Blight (blb)." |
| } |
|
|
