Single Shot Detection

Build flexible object detection pipelines with declarative configuration

You are being provided with the following set of features:

Supporting latest PyTorch release
Train SSD, M2Det, RetinaNet or some custom architecture
Available backbones: torchvision + pretrainedmodels + custom MobileNet, MobileNetV2
Data augmentations
AdamW and SGDW optimizers, some custom learning rate schedulers
Weight pruning for efficient inference
Export to ONNX or OpenVINO
Tensorboard integration
Training callbacks

Download PASCAL VOC or COCO dataset and start training using one of the provided sample configs:

python3 main.py --config samples/ssd_mb2_voc.py

^{^{(don't forget to adjust paths in the config first!)}}

To see the list of parameters:

python3 main.py --help

Some places that may be useful to look into:

bf - provides common reusable parts for building a deep learning pipeline
- bf.base - custom backbone network implementation (e.g. MobileNetV2)
- bf.datasets - dataset handling
- bf.preprocessing - data augmentations and preprocessing
- bf.training - callbacks; custom optimizers and learning rate schedulers; weight prunner
- ...
detection - parts of code which are used to build object detection pipelines on top of bf
samples - contains sample configuration files for popular network architectures
main.py - the entry point

Tensorflow Object Detection API (https://github.com/tensorflow/models/tree/master/research/object_detection)
Gluon CV (https://github.com/dmlc/gluon-cv)
mmdetection (https://github.com/open-mmlab/mmdetection)

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