Add model card for agromind-plant-disease-mobilenet

README.md

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+---
+license: apache-2.0
+tags:
+  - image-classification
+  - plant-disease
+  - agriculture
+  - mobilenet
+  - pytorch
+library_name: pytorch
+---
+
+# AgroMind Plant Disease Classifier (MobileNetV2)
+
+## Model Description
+MobileNetV2 image classifier trained on the New Plant Diseases Dataset to detect 38 plant disease classes. Serves as a lightweight fallback for the NFNet-F1 model.
+
+## Framework
+- **Architecture**: MobileNetV2 (torchvision)
+- **Format**: PyTorch checkpoint (.pth)
+- **Input size**: 224×224 RGB (resize to 256, center crop to 224)
+- **Normalization**: ImageNet (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+## Usage
+```python
+from huggingface_hub import hf_hub_download
+import torch
+from torchvision import models, transforms
+from PIL import Image
+
+repo_id = "Arko007/agromind-plant-disease-mobilenet"
+ckpt = hf_hub_download(repo_id, "newplant_model_final.pth")
+labels_path = hf_hub_download(repo_id, "labels.txt")
+
+with open(labels_path) as f:
+    labels = [l.strip() for l in f if l.strip()]
+
+model = models.mobilenet_v2(pretrained=False)
+model.classifier[1] = torch.nn.Linear(model.classifier[1].in_features, len(labels))
+state = torch.load(ckpt, map_location="cpu")
+model.load_state_dict(state)
+model.eval()
+
+transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+])
+
+img = Image.open("leaf.jpg").convert("RGB")
+with torch.no_grad():
+    logits = model(transform(img).unsqueeze(0))
+    print(labels[logits.argmax(dim=1).item()])
+```
+
+## Output
+Returns logits for 38 plant disease classes. See `labels.txt` for class names.