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README.md

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+---
+license: apache-2.0
+tags:
+- image-classification
+- plant-pathology
+- efficientnet
+- pytorch
+datasets:
+- plant-pathology-2020-fgvc7
+metrics:
+- accuracy
+library_name: pytorch
+---
+
+# Plant Pathology EfficientNet-B2
+
+This model classifies plant diseases using EfficientNet-B2 architecture. It was trained on the Plant Pathology 2020 FGVC7 dataset.
+
+## Model Description
+
+- **Architecture**: EfficientNet-B2 (pretrained on ImageNet)
+- **Task**: Multi-class image classification (4 classes)
+- **Input Size**: 260x260 RGB images
+- **Classes**:
+  - healthy
+  - multiple_diseases
+  - rust
+  - scab
+
+## Performance
+
+- **Validation Accuracy**: 96.04%
+- **Test Accuracy**: 97.00%
+
+### Requirements
+
+```bash
+pip install torch torchvision Pillow safetensors
+```
+
+### Inference Code
+
+```python
+import torch
+import torch.nn as nn
+from torchvision import transforms
+from torchvision.models import efficientnet_b2
+from PIL import Image
+from safetensors.torch import load_file
+
+# Define the model architecture
+class PlantPathologyModel(nn.Module):
+    def __init__(self, num_classes=4):
+        super(PlantPathologyModel, self).__init__()
+        self.backbone = efficientnet_b2(pretrained=False)
+        in_features = self.backbone.classifier[1].in_features
+        self.backbone.classifier = nn.Sequential(
+            nn.Dropout(p=0.3, inplace=True),
+            nn.Linear(in_features, num_classes)
+        )
+
+    def forward(self, x):
+        return self.backbone(x)
+
+# Load model
+model = PlantPathologyModel(num_classes=4)
+state_dict = load_file("plant-pathology-efficientnetb2.safetensors")
+model.load_state_dict(state_dict)
+model.eval()
+
+# Define preprocessing
+transform = transforms.Compose([
+    transforms.Resize((260, 260)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# Inference
+image = Image.open("your_plant_image.jpg").convert("RGB")
+input_tensor = transform(image).unsqueeze(0)
+
+with torch.no_grad():
+    output = model(input_tensor)
+    probabilities = torch.nn.functional.softmax(output[0], dim=0)
+    predicted_class = torch.argmax(probabilities).item()
+
+# Class names
+class_names = ["healthy", "multiple_diseases", "rust", "scab"]
+print(f"Predicted: {class_names[predicted_class]}")
+print(f"Confidence: {probabilities[predicted_class]:.2%}")
+```
+
+## Training Details
+
+### Training Data
+- Dataset: Plant Pathology 2020 FGVC7
+- Training samples: 1,310
+- Validation samples: 328
+- Test samples: 181
+
+### Training Configuration
+- **Optimizer**: Adam (lr=0.001)
+- **Loss Function**: CrossEntropyLoss
+- **Batch Size**: 32
+- **Epochs**: 15
+- **Learning Rate Scheduler**: ReduceLROnPlateau
+- **Data Augmentation**:
+  - Random horizontal flip
+  - Random vertical flip
+  - Random rotation (+/- 20 degrees)
+  - Color jitter (brightness=0.2, contrast=0.2)
+
+### Hardware
+- GPU training on CUDA-enabled device
+
+## Limitations
+
+- Model is trained specifically on apple leaf diseases from the Plant Pathology 2020 dataset
+- Performance may vary on other plant species or different imaging conditions
+- Requires consistent image preprocessing (resize to 260x260, normalize with ImageNet stats)
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{plant-pathology-efficientnetb2,
+  author = {Nahuel},
+  title = {Plant Pathology EfficientNet-B2},
+  year = {2026},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/nahuelnb/plant-pathology-efficientnetb2}}
+}
+```
+
+## License
+
+Apache 2.0