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149 changes: 61 additions & 88 deletions Yolo/android/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -3,17 +3,24 @@
## 🐦 Android Bird Detection App with ExecuTorch

A real-time bird detection and species identification Android app using YOLO + EfficientNet models deployed via Meta's ExecuTorch framework. The app provides session-based bird watching with automatic logging, thumbnails, and timestamps for backyard bird enthusiasts.
Bird Detection Feature: Uses a two-stage pipeline where YOLO (COCO class 14) detects birds in camera frames, then EfficientNet classifier identifies the specific species from 525 possible bird types with 96.8% accuracy.

## Model Download and Conversion

### Step 1: Download Models
**Bird Detection Feature:** Uses a two-stage pipeline where YOLO (COCO class 14) detects birds in camera frames, then EfficientNet classifier identifies the specific species from 525 possible bird types with 96.8% accuracy.

#### Bird Classifier Model
## Prerequisites

Install dependencies
Install required dependencies:

```bash
pip install torch transformers executorch ultralytics
```

## Model Download and Conversion

### Step 1: Download and Convert Bird Classifier Model

Create `convert_bird_classifier.py`:

```python
import torch
from transformers import AutoModelForImageClassification
from torch.export import export
Expand All @@ -37,79 +44,41 @@ et_program = edge_program.to_executorch()
with open("bird_classifier.pte", "wb") as f:
et_program.write_to_file(f)
print("Bird classifier converted to bird_classifier.pte")
```

Run it from a regular terminal (not Claude Code) to avoid the proxy block:
Run the script:

cd /home/sidart/executorch
```bash
python convert_bird_classifier.py
"
```

## Detection Model

The app supports both v8 and v26 and automatically detects which version you're using based on the model output format.
### Step 2: Download YOLO Detection Model

Install Ultralytics:
The app supports both YOLOv8 and YOLOv26, and automatically detects which version you're using based on the model output format.

```
```bash
pip install ultralytics
```

### Download model

#### Option 1: v8
python -c "
from ultralytics import
model = ('v8n.pt') # nano version for mobile
print('v8 model downloaded')
"
```

#### Option 2: v26 (Recommended - Faster & More Accurate)
python -c "
from ultralytics import
model = ('26n.pt') # nano version for mobile
print('v26 model downloaded')
"
#### Option 1: YOLOv8
```python
from ultralytics import YOLO
model = YOLO('yolov8n.pt') # nano version for mobile
print('YOLOv8 model downloaded')
```

## Model Conversion to ExecuTorch

### Step 2: Convert Models to .pte Format

#### Convert Bird Classifier

convert_bird_classifier.py

```
import torch
from transformers import AutoModelForImageClassification
from torch.export import export
from executorch.exir import to_edge_transform_and_lower
from executorch.backends.xnnpack.partition.xnnpack_partitioner import XnnpackPartitioner
Load model
model = AutoModelForImageClassification.from_pretrained('./bird_classifier_model')
model.eval()
Export to ExecuTorch
example_input = torch.randn(1, 3, 224, 224)
exported_program = export(model, (example_input,))
edge_program = to_edge_transform_and_lower(
exported_program,
partitioner=[XnnpackPartitioner()]
)
et_program = edge_program.to_executorch()
Save as .pte file
with open("bird_classifier.pte", "wb") as f:
et_program.write_to_file(f)
print("Bird classifier converted to bird_classifier.pte")
#### Option 2: YOLOv26 (Recommended - Faster & More Accurate)
```python
from ultralytics import YOLO
model = YOLO('yolo26n.pt') # nano version for mobile
print('YOLOv26 model downloaded')
```

### Convert YOLO Model
### Step 3: Convert YOLO Model to .pte Format

convert_yolo.py
This script works for both YOLOv8 and YOLOv26 - just change the model filename.
Create `convert_yolo.py`:

```
```python
from ultralytics import YOLO
import torch
from torch.export import export
Expand Down Expand Up @@ -137,25 +106,28 @@ with open("yolo_detector.pte", "wb") as f:
print("YOLO model converted to yolo_detector.pte")
```

#### Auto-Detection: The app automatically detects which YOLO version you're using (v8 or v26) based on the model's output format. No code changes needed when switching between versions!
**Auto-Detection:** The app automatically detects which YOLO version you're using (v8 or v26) based on the model's output format. No code changes needed when switching between versions!

### Generate Bird Species Names
### Step 4: Generate Bird Species Names

extract_species_names.py
Create `extract_species_names.py`:

```
```python
from transformers import AutoModelForImageClassification
import json

model = AutoModelForImageClassification.from_pretrained('./bird_classifier_model')
species_names = [model.config.id2label[i] for i in range(len(model.config.id2label))]

with open('bird_species.json', 'w') as f:
json.dump(species_names, f, indent=2)
json.dump(species_names, f, indent=2)

print(f"Saved {len(species_names)} bird species names to bird_species.json")
```

## Deploying Models to Android

### Step 3: Deploy Models to Android Device
### Step 5: Deploy Models to Android Device

#### Copy Models to Android Assets

Expand All @@ -166,58 +138,59 @@ cp yolo_detector.pte /path/to/android/app/src/main/assets/
cp bird_species.json /path/to/android/app/src/main/assets/
```

### Alternative: Push via ADB (for testing)
#### Alternative: Push via ADB (for testing)

Connect Android device and enable USB debugging

```
```bash
# Connect Android device and enable USB debugging
adb devices
Create directory on device

# Create directory on device
adb shell mkdir -p /data/local/tmp/bird_detection/
Push model files

# Push model files
adb push bird_classifier.pte /data/local/tmp/bird_detection/
adb push yolo_detector.pte /data/local/tmp/bird_detection/
adb push bird_species.json /data/local/tmp/bird_detection/
Verify files are transferred

# Verify files are transferred
adb shell ls -la /data/local/tmp/bird_detection/
```

### File Structure

```
app/src/main/assets/
├── bird_classifier.pte # EfficientNet bird species classifier (8.5MB)
├── yolo_detector.pte # YOLOv8n bird detection model (6MB)
└── bird_species.json # List of 525 bird species names
├── bird_classifier.pte # EfficientNet bird species classifier (~8.5MB)
├── yolo_detector.pte # YOLO bird detection model (~6MB)
└── bird_species.json # List of 525 bird species names
```

## App Features

### Main Detection Screen

- Camera Preview: Real-time video feed from device camera
- Live Detection: Green bounding boxes around detected birds with species labels
- Session Controls: Start/Stop buttons for bird watching sessions
- **Camera Preview:** Real-time video feed from device camera
- **Live Detection:** Green bounding boxes around detected birds with species labels
- **Session Controls:** Start/Stop buttons for bird watching sessions

### Session Management

#### "Start Session" Button:

**Start Session Button:**
- Activates bird detection and logging
- Changes camera from passive viewing to active detection mode
- Begins collecting bird sightings with timestamps and thumbnails
- Button turns red and displays "Stop Session"
- "Stop Session" Button:

**Stop Session Button:**
- Deactivates detection and shows session summary
- Displays total birds detected and unique species count
- Button turns green and displays "Start Session"
- Preserves collected data for viewing

### Bird Log Viewer

#### "View Logs" Button:

**View Logs Button:**
- Opens detailed session log showing all detected birds
- Displays bird thumbnails, species names, detection times, and confidence scores
- Organized as scrollable list with visual bird identification records
- Useful for reviewing and verifying bird watching session results
- Useful for reviewing and verifying bird watching session results