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README.md

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+---
+language: en
+license: mit
+tags:
+  - image-classification
+  - efficientnet
+  - vm-ai
+  - activity-recognition
+datasets:
+  - maxf-coder/task_image_classifier
+metrics:
+  - accuracy
+  - f1
+---
+
+# VM.AI — Image Classifier
+
+EfficientNet-B4 trained on 14 activity categories for the image-to-prompt pipeline.
+
+## Performance
+
+| Metric | Value |
+|--------|-------|
+| Test samples | {test_samples} |
+| Top-1 accuracy | {top1} |
+| Top-3 accuracy | {top3} |
+| Macro F1 | {macro_f1} |
+| Weighted F1 | {weighted_f1} |
+
+## Per-Class Metrics
+
+| Class | Precision | Recall | F1 | Support |
+|-------|-----------|--------|------|---------|
+{class_rows}
+## Usage
+
+```python
+import torch
+import timm
+from PIL import Image
+from torchvision import transforms
+
+model = timm.create_model("efficientnet_b4", pretrained=False, num_classes=14)
+model.load_state_dict(torch.load("efficientnet_b4_classifier.pth", map_location="cpu"))
+model.eval()
+
+transform = transforms.Compose([
+    transforms.Resize((380, 380)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+])
+
+img = Image.open("photo.jpg").convert("RGB")
+tensor = transform(img).unsqueeze(0)
+with torch.no_grad():
+    logits = model(tensor)
+pred = logits.argmax(1).item()
+```
+
+## Training
+
+Two-phase training: 5 frozen epochs (head only) + 20 unfrozen epochs (last 2 blocks).
+Optimizer: AdamW with cosine annealing. Mixed precision (AMP).
+See [train_classifier.py](https://github.com/Infiteri/VM.AI) for details.
+
+## Charts
+
+![Confusion matrix](confusion_matrix.png)
+![Per-class metrics](per_class_metrics.png)
+![Top-K accuracy](topk_accuracy.png)