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README.md

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+---
+license: apache-2.0
+tags:
+- image-classification
+- dog-breeds
+- fine-grained
+- arcface
+- convnext
+- pytorch
+datasets:
+- stanford-dogs
+metrics:
+- accuracy
+pipeline_tag: image-classification
+model-index:
+- name: Petus Breed Classifier (convnextv2_tiny)
+  results:
+  - task:
+      type: image-classification
+    dataset:
+      name: Stanford Dogs
+      type: stanford-dogs
+    metrics:
+    - name: Top-1 Accuracy (Val)
+      type: accuracy
+      value: 91.4
+    - name: Top-5 Accuracy (Val)
+      type: accuracy
+      value: 99.2
+---
+
+# Petus Breed Classifier (convnextv2_tiny)
+
+Dog breed classifier trained on Stanford Dogs (120 breeds) using **convnextv2_tiny** backbone with **ArcFace** angular margin loss and progressive resizing.
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| Backbone | convnextv2_tiny |
+| Loss | ArcFace (s=30.0, m=0.3) |
+| Parameters | 28,323,200 |
+| Input Size | 336px |
+| Val Top-1 | **91.4%** |
+| Val Top-5 | **99.2%** |
+| Training | 2-phase (frozen head → unfrozen backbone) |
+| Progressive Resize | 224 → 336px |
+
+## Training Recipe (v3)
+
+1. **Phase 1**: Frozen backbone, train ArcFace head only (2 epochs)
+2. **Phase 2**: Unfreeze backbone with 1/100th LR, cosine annealing (48 epochs)
+   - 3-epoch linear LR warmup after unfreeze
+   - Progressive resize from 224→336 mid-training
+   - ArcFace angular margin loss (no MixUp/CutMix needed)
+   - Early stopping with patience=10
+
+## Usage
+
+```python
+import torch
+from torchvision import transforms
+from PIL import Image
+
+# Load model
+checkpoint = torch.load("convnextv2_tiny_best.pt", map_location="cpu")
+
+# Preprocess
+transform = transforms.Compose([
+    transforms.Resize(384),  # 336 * 1.14
+    transforms.CenterCrop(336),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+image = Image.open("dog.jpg").convert("RGB")
+input_tensor = transform(image).unsqueeze(0)
+
+# Inference
+model.eval()
+with torch.no_grad():
+    logits = model(input_tensor)
+    pred = logits.argmax(dim=1).item()
+    confidence = logits.softmax(dim=1).max().item()
+```
+
+## Breeds
+
+120 dog breeds from the Stanford Dogs dataset (synsets from ImageNet).
+
+## Citation
+
+```bibtex
+@misc{petus-breed-ml,
+  author = {199 Biotechnologies},
+  title = {Petus Breed Classifier},
+  year = {2026},
+  url = {https://github.com/199-biotechnologies/petus-breed-ml}
+}
+```
+
+## License
+
+Apache 2.0