README.md

metadata

title: ScribblBot
emoji: ✏️
colorFrom: green
colorTo: gray
sdk: gradio
sdk_version: 5.23.0
app_file: app.py
pinned: false

ScribblBot

Real time sketch recognition powered by a lightweight CNN trained on Google's Quick Draw dataset. Draw anything in the browser and ScribblBot identifies it instantly.

Live app: https://huggingface.co/spaces/Jog-sama/ScribbleBot_Huggingface

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Results

Model	Architecture	Test Accuracy
Majority Class Baseline	Always predicts most frequent class	6.67%
Random Forest	HOG features, 200 trees	85.10%
ScribblNet	3-layer CNN	94.42%

ScribblNet was trained on 30,000 samples across 15 classes (2,000 per class) for 15 epochs using Adam with cosine annealing. Training took under 5 minutes on Apple M-series hardware with MPS acceleration. The Random Forest operates on 1,296-dimensional HOG feature vectors extracted from 28×28 grayscale bitmaps.

Classes: cat · dog · pizza · bicycle · house · sun · tree · car · fish · butterfly · guitar · hamburger · airplane · banana · star

Per-class performance (ScribblNet)

Class	Precision	Recall	F1
cat	0.88	0.83	0.85
dog	0.81	0.82	0.82
pizza	0.94	0.97	0.96
bicycle	0.96	0.98	0.97
house	0.99	0.98	0.98
sun	0.95	0.96	0.96
tree	0.96	0.97	0.97
car	0.97	0.96	0.96
fish	0.97	0.95	0.96
butterfly	0.97	0.97	0.97
guitar	0.95	0.98	0.96
hamburger	0.99	0.97	0.98
airplane	0.91	0.89	0.90
banana	0.97	0.98	0.97
star	0.94	0.94	0.94

Cat and dog are the hardest classes, which is expected given their visual similarity in quick sketches. Airplane also underperforms, likely due to style variation in how people draw wings and fuselage.

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Experiment: Training Size Sensitivity

Both ScribblNet and Random Forest were trained at 10%, 25%, 50%, 75%, and 100% of available training data.

Fraction	Samples	ScribblNet	Random Forest
10%	3,000	86.12%	77.92%
25%	7,500	90.37%	80.35%
50%	15,000	92.70%	81.57%
75%	22,500	93.00%	82.88%
100%	30,000	94.02%	83.03%

The CNN scales more steeply with data volume than the Random Forest. At 10% of training data the gap is about 8 points; at 100% it grows to 11 points. The Random Forest plateaus around 83% while ScribblNet continues improving, suggesting the CNN would benefit further from additional data.

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Dataset

Quick Draw by Google — 50 million drawings across 345 categories, collected from players of the Quick Draw game. Each drawing is a 28×28 grayscale bitmap stored as a flat 784-element uint8 vector. The dataset is publicly available via Google Cloud Storage.

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Setup

pip install -r requirements.txt
python setup.py
python app.py

setup.py runs the full pipeline: downloads the raw .npy files, extracts HOG features, trains all three models, and runs the experiment.

Individual steps:

python scripts/make_dataset.py
python scripts/build_features.py
python scripts/model.py

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Repository Structure

scribblbot/
├── README.md
├── requirements.txt
├── Makefile
├── setup.py
├── app.py
├── config.py
├── scripts/
│   ├── make_dataset.py
│   ├── build_features.py
│   └── model.py
├── models/
├── data/
│   ├── raw/
│   ├── processed/
│   └── outputs/
└── notebooks/

Component	Location
Naive baseline	scripts/model.py — MajorityClassifier, saved to models/naive_model.pkl
Random Forest	scripts/model.py — train_classical(), saved to models/classical_model.pkl
ScribblNet CNN	scripts/model.py — ScribblNet, train_deep(), saved to models/deep_model.pth
Inference app	app.py
Config	config.py

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Deployment

1. python setup.py to train and generate models/deep_model.pth
2. Create a new Space on HuggingFace (SDK: Gradio)
3. Push the full repo including models/deep_model.pth

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Git Workflow

Working branches: develop for integration, feature/* for individual changes. All work branches into develop via pull requests. develop merges into main for releases. No direct commits to main.