README.md

---
language:
  - en
license: apache-2.0
tags:
  - image-classification
  - tflite
  - flutter
  - quickdraw
  - doodle-recognition
  - on-device-inference
  - se-resnet
datasets:
  - google/quickdraw
metrics:
  - accuracy
library_name: tflite
pipeline_tag: image-classification
model-index:
  - name: quickdraw-345-se-resnet
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: Google Quick Draw
          type: google/quickdraw
        metrics:
          - type: accuracy
            value: 0.7619
            name: Top-1 Accuracy
          - type: accuracy
            value: 0.8951
            name: Top-3 Accuracy
          - type: accuracy
            value: 0.9226
            name: Top-5 Accuracy
          - type: accuracy
            value: 0.9455
            name: Top-10 Accuracy
          - type: accuracy
            value: 0.7640
            name: TFLite Float16 Accuracy
---
# QuickDraw 345 Doodle Classifier — TFLite

A doodle recognition model trained on all **345 categories** from Google's [Quick Draw Dataset](https://quickdraw.withgoogle.com/data), exported as TFLite for **Flutter on-device offline inference**.

## Model Performance

| Metric | Accuracy |
|--------|----------|
| Top-1  | **76.19%** |
| Top-3  | **89.51%** |
| Top-5  | **92.26%** |
| Top-10 | **94.55%** |
| TFLite (float16) | **76.40%** |

> State-of-the-art for 345-class Quick Draw classification is ~73-75% top-1. This model exceeds that.

## Files

| File | Size | Description |
|------|------|-------------|
| `quickdraw_model.tflite` | 8.44 MB | Float16 quantized — recommended for Flutter |
| `quickdraw_model_int8.tflite` | 4.34 MB | Int8 quantized — smallest, fastest |
| `labels.txt` | 2.7 KB | 345 class labels, one per line (alphabetically sorted) |
| `model_metadata.json` | 6.5 KB | Full metadata including accuracy, input shape, Flutter usage |
| `training_history.json` | 5.7 KB | Loss/accuracy per epoch |
| `categories.txt` | 2.7 KB | Raw category list |

## Architecture

- **SE-ResNet** (Squeeze-and-Excitation + ResNet blocks)
- 3 stages: 64 → 128 → 256 filters
- Input: 28×28 grayscale images
- Output: 345-class softmax
- ~3M parameters

## Training

- **Dataset**: Google Quick Draw numpy bitmaps (GCS), 8,000 samples/class × 345 classes = 2.76M images
- **Augmentation**: Random rotation ±8%, translation ±8%, zoom -5%/+10%
- **Optimizer**: Adam + Warmup Cosine Decay
- **Training time**: ~10.9 hours on Kaggle GPU P100

## Flutter Integration

### pubspec.yaml
```yaml
dependencies:
  tflite_flutter: ^0.10.4

flutter:
  assets:
    - assets/quickdraw_model.tflite
    - assets/labels.txt
```

### Dart Usage
```dart
import 'package:tflite_flutter/tflite_flutter.dart';

class QuickDrawClassifier {
  late Interpreter _interpreter;
  late List<String> _labels;

  Future<void> load() async {
    _interpreter = await Interpreter.fromAsset('assets/quickdraw_model.tflite');
    final labelsData = await rootBundle.loadString('assets/labels.txt');
    _labels = labelsData.trim().split('\n');
  }

  /// [pixels] must be a 28x28 Float32List, values in [0.0, 1.0]
  /// where 0.0 = black stroke, 1.0 = white background
  List<MapEntry<String, double>> predict(Float32List pixels, {int topK = 5}) {
    // Reshape to [1, 28, 28, 1]
    var input = pixels.reshape([1, 28, 28, 1]);
    var output = List.filled(1 * 345, 0.0).reshape([1, 345]);

    _interpreter.run(input, output);

    final probs = List<double>.from(output[0]);
    final indexed = probs.asMap().entries.toList()
      ..sort((a, b) => b.value.compareTo(a.value));

    return indexed.take(topK)
      .map((e) => MapEntry(_labels[e.key], e.value))
      .toList();
  }
}
```

### Preprocessing a drawing canvas
```dart
/// Convert your drawing canvas to a 28x28 normalized Float32List
Float32List canvasToInput(ui.Image image) async {
  // Resize to 28x28
  final recorder = ui.PictureRecorder();
  final canvas = Canvas(recorder);
  canvas.drawImageRect(
    image,
    Rect.fromLTWH(0, 0, image.width.toDouble(), image.height.toDouble()),
    Rect.fromLTWH(0, 0, 28, 28),
    Paint(),
  );
  final resized = await recorder.endRecording().toImage(28, 28);
  final bytes = await resized.toByteData(format: ui.ImageByteFormat.rawRgba);

  // Convert RGBA to grayscale float32, normalize to [0,1]
  // white background = 1.0, black strokes = 0.0
  final pixels = Float32List(28 * 28);
  for (int i = 0; i < 28 * 28; i++) {
    final r = bytes!.getUint8(i * 4);
    final g = bytes.getUint8(i * 4 + 1);
    final b = bytes.getUint8(i * 4 + 2);
    pixels[i] = (0.299 * r + 0.587 * g + 0.114 * b) / 255.0;
  }
  return pixels;
}
```

## Input/Output Spec

| Property | Value |
|----------|-------|
| Input shape | `[1, 28, 28, 1]` |
| Input dtype | `float32` |
| Input range | `[0.0, 1.0]` |
| Background | `1.0` (white) |
| Stroke | `0.0` (black) |
| Output shape | `[1, 345]` |
| Output dtype | `float32` |
| Output | Softmax probabilities |

## License

Model weights: Apache 2.0  
Dataset: [Creative Commons Attribution 4.0](https://creativecommons.org/licenses/by/4.0/) (Google Quick Draw)