Update README.md

README.md

@@ -1,3 +1,95 @@
----
-license: gpl-3.0
----
+---
+license: gpl-3.0
+tags:
+  - bird-classification
+  - onnx
+  - efficientnet
+  - raspberry-pi
+  - hailo
+  - computer-vision
+  - real-time
+datasets:
+  - duyminhle/nabirds
+model-index:
+  - name: EfficientNet-B7 Backyard Feeder Bird Classifier
+    results: []
+---
+
+# 🐦 EfficientNet-B7 — Backyard Feeder Bird Classifier
+
+Custom ONNX-based bird classification model trained on a filtered subset of the [NABirds dataset](https://dl.allaboutbirds.org/nabirds), optimized for backyard bird feeders.
+
+> Designed to run on a Raspberry Pi + Hailo-8 setup in real-time as part of the [Birdwatcher Project](https://github.com/n2b8/birdwatcher)
+
+---
+
+## 🧠 Model Details
+
+- Architecture: EfficientNet-B7
+- Resolution: `600×600`
+- Precision: Mixed (AMP)
+- Format: ONNX
+- Classes: 95 backyard species + `not_a_bird`
+- Validation Accuracy: **93.14%** @ Epoch 23
+- Optimized for inference on edge devices (e.g., Raspberry Pi 5 + Hailo-8)
+
+---
+
+## 📁 Files
+
+- `efficientnet_b7_backyard_feeder_birds.onnx` — Trained ONNX model
+- `class_labels_v3.txt` — One class label per line
+
+---
+
+## 🛠️ Intended Use
+
+- **Use Case:** Real-time fine-grained classification of birds visiting backyard feeders
+- **Hardware:** Optimized for Raspberry Pi + Hailo-8 AI accelerator
+- **Pipeline:** Captures images via YOLOv8 detection, then classifies via this model
+
+---
+
+## 🐍 Example Inference Code
+
+```python
+import onnxruntime as ort
+import numpy as np
+from PIL import Image
+
+# Load model
+session = ort.InferenceSession("efficientnet_b7_backyard_feeder_birds.onnx")
+
+# Preprocess
+img = Image.open("bird.jpg").resize((600, 600))
+x = np.array(img).astype(np.float32) / 255.0
+x = np.transpose(x, (2, 0, 1))[np.newaxis, :]  # CHW + batch
+
+# Predict
+outputs = session.run(None, {"input": x})
+pred_idx = np.argmax(outputs[0])
+```
+
+---
+
+## 📊 Training
+
+- Dataset: Subset of NABirds (filtered for common backyard species)
+- Augmentations: Flip, rotation, brightness
+- Regularization: Dropout, label smoothing
+- Loss: Cross-entropy
+- Optimizer: AdamW
+- Early stopping enabled
+
+---
+
+## 🔗 Related Repos
+
+- [Birdwatcher Project (GitHub)](https://github.com/n2b8/birdwatcher)
+- [NABirds Dataset (Kaggle)](https://www.kaggle.com/datasets/duyminhle/nabirds)
+
+---
+
+## 📜 License
+
+GPLv3