Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- image-classification
+- walnut
+- defect-detection
+- efficientnet
+- timm
+- pytorch
+- surface-defect
+- quality-control
+pipeline_tag: image-classification
+library_name: timm
+base_model: timm/efficientnet_b3.ra2_in1k
+metrics:
+- accuracy
+- f1
+model-index:
+- name: Walnut Shell Defect Classifier
+  results:
+  - task:
+      type: image-classification
+      name: Image Classification
+    dataset:
+      name: Nut Surface Defect Dataset (nutsv2ifolder_split)
+      type: weihaoreal/nut-surface-defect-dataset
+    metrics:
+    - type: accuracy
+      value: 0.9855
+      name: Validation Accuracy
+    - type: f1
+      value: 0.98
+      name: Macro F1
+---
+
+# Walnut Shell Defect Classifier
+
+EfficientNet-B3 finetuned for walnut shell defect classification across 4 categories.
+Trained on the Nut Surface Defect Dataset with class remapping to match walnut-specific defect taxonomy.
+
+## Classes
+
+| Output Label | Remapped From (Dataset) |
+|---|---|
+| Healthy | Excellent |
+| Black Spot | Rusting |
+| Shriveled | Scratches |
+| Damaged | Deformation + Fracture |
+
+## Metrics (Epoch 8 — Best Checkpoint)
+
+| Class | Precision | Recall | F1 |
+|---|---|---|---|
+| Healthy | 0.88 | 1.00 | 0.93 |
+| Black Spot | 1.00 | 0.99 | 1.00 |
+| Shriveled | 1.00 | 0.98 | 0.99 |
+| Damaged | 1.00 | 0.98 | 0.99 |
+| **Macro Avg** | **0.97** | **0.99** | **0.98** |
+| **Weighted Avg** | **0.99** | **0.99** | **0.99** |
+
+**Val Accuracy: 98.55% | Macro F1: 0.98**
+
+## Training Setup
+
+| Parameter | Value |
+|---|---|
+| Base Model | EfficientNet-B3 (pretrained ImageNet) |
+| Image Size | 512×512 px |
+| Batch Size | 18 per GPU × 2 T4 = 36 effective |
+| Optimizer | AdamW (lr=2e-5, wd=1e-2) |
+| Scheduler | Cosine Annealing + 3-epoch warmup |
+| Precision | FP16 (torch.cuda.amp) |
+| Drop Rate | 0.4 |
+| Label Smoothing | 0.05 |
+| Early Stop Patience | 7 epochs |
+| Hardware | Kaggle 2× NVIDIA T4 (16 GB each) |
+
+## Inference
+
+```python
+import torch, timm
+from PIL import Image
+import torchvision.transforms as transforms
+
+CLASSES = ["Healthy", "Black Spot", "Shriveled", "Damaged"]
+
+model = timm.create_model("efficientnet_b3", pretrained=False,
+                           num_classes=4, drop_rate=0.4)
+ckpt  = torch.load("best_model.pth", map_location="cpu")
+state = {k.replace("module.", ""): v for k, v in ckpt["model_state_dict"].items()}
+model.load_state_dict(state)
+model.eval()
+
+transform = transforms.Compose([
+    transforms.Resize((512, 512)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+])
+
+img    = Image.open("walnut.jpg").convert("RGB")
+x      = transform(img).unsqueeze(0)
+probs  = torch.softmax(model(x), dim=1)
+conf, idx = probs.max(0)
+print({"defect_class": CLASSES[idx.item()], "confidence": round(conf.item(), 4)})
+```
+## License
+
+Apache 2.0