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🚷 Signfeld in PyTorch

"Give me a sign!" - Britney Spears, Circa October 23, 1998

"Jerry, just remember, it's not a lie if you believe it."- George Costanza (Seinfeld), Circa July 5, 1989

🚳 Synthetic traffic sign detection

This repository collates our efforts on building traffic sign detection model in low (to zero) sample regime (with little to no human annotations). We leverage templates of known traffic signs to train our detector. We married the ideas of synthetic text & object detection for this work to bear fruit. We provide a pre-trained traffic sign detection model trained on 169 German Traffic sign(s). Sample results

🐾 Table of Contents

💻 Installation

You can either install the code in a virtual environment or docker. Use the docker if you want a reproducible environment.

🐍 Conda

First, create a virtual environment:

conda create -n synth-signs
conda activate synth-signs
conda install pip

Then install dependencies and software:

pip install .            # If you only want to generate a dataset.
pip install .[inference] # Include Inference
pip install .[trainer]   # Include Model training

🐳 Docker

The following two commands install and run the docker image:

make docker-install
make docker-run

🎉 Usage

Download the pre-trained model(.pth) from here at resources/models.

Detection

detect-synthetic-signs --images=synthetic_signs/images/test_samples/*.jpg \
                       --label-map=resources/labels/labels-DE-169.yaml \
                       --config=resources/models/DE-signs-169.yaml \
                       --weights=resources/models/DE-signs-169.pth \
                       --output-dir=/tmp/signfeld

Visualisation

visualize-synthetic-sign-detections --images=synthetic_signs/images/test_samples/*.jpg \
                                    --template-dir=synthetic_signs/templates \
                                    --detections=/tmp/signfeld \
                                    --destination=/tmp/signfeld-viz \
                                    --min-confidence=0.5
Disclaimer : The model was trained with zero real samples, and is known to miss signs which are obscure, small and skewed from camera axis.

🐇 German Traffic Signs

Trained models are include in the repository [Calzone]. Evaluated on GTSDB

Name Description GTSDB mAP Remarks
Calzone Detector: RetinaNet, backbone: ResNet 50 67.23 Location : download

👽 Contributors