Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,69 @@
+---
+license: mit
+tags:
+- anomaly-detection
+- efficientad
+- mvtec-ad
+- pytorch
+- computer-vision
+datasets:
+- mvtec_ad
+metrics:
+- auroc
+---
+
+# EfficientAD - Bottle
+
+Anomaly detection model trained on MVTec AD bottle dataset using EfficientAD.
+
+## Model Details
+
+- **Architecture**: EfficientAD (medium)
+- **Dataset**: MVTec AD - bottle
+- **Task**: Anomaly Detection & Localization
+- **Framework**: PyTorch
+
+## Files
+
+- `teacher_final.pth`: Teacher network weights
+- `student_final.pth`: Student network weights
+- `autoencoder_final.pth`: Autoencoder network weights
+- `normalization.pth`: Normalization parameters for inference
+
+## Usage
+
+```python
+from huggingface_hub import hf_hub_download
+
+# Download weights
+teacher_path = hf_hub_download(
+    repo_id="MSherbinii/efficientad-bottle",
+    filename="teacher_final.pth"
+)
+student_path = hf_hub_download(
+    repo_id="MSherbinii/efficientad-bottle",
+    filename="student_final.pth"
+)
+autoencoder_path = hf_hub_download(
+    repo_id="MSherbinii/efficientad-bottle",
+    filename="autoencoder_final.pth"
+)
+normalization_path = hf_hub_download(
+    repo_id="MSherbinii/efficientad-bottle",
+    filename="normalization.pth"
+)
+
+# Load with PyTorch
+import torch
+teacher = torch.load(teacher_path, map_location='cpu')
+student = torch.load(student_path, map_location='cpu')
+autoencoder = torch.load(autoencoder_path, map_location='cpu')
+```
+
+## Citation
+
+Based on EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
+
+## License
+
+MIT