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A Simple and Versatile Framework for Object Detection and Instance Recognition
| Date | Stars |
|---|---|
| 2026-07-24 | 3084 |
| 2026-07-25 | 3085 |
| 2026-07-28 | 3085 |
| 2026-07-30 | 3085 |
| 2026-08-06 | 3085 |
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## SimpleDet - A Simple and Versatile Framework for Object Detection and Instance Recognition ### Major Features  - FP16 training for memory saving and up to **2.5X** acceleration - Highly scalable distributed training available **out of box** - Full coverage of state-of-the-art models including FasterRCNN, MaskRCNN, CascadeRCNN, RetinaNet, [DCNv1/v2](./models/dcn), **[TridentNet](./models/tridentnet)**, **[NASFPN](./models/NASFPN)** , **[EfficientNet](./models/efficientnet)**, and **[Knowledge Distillation](./models/KD)** - Extensive feature set including **large batch BN**, **loss synchronization**, **automatic BN fusion**, soft NMS, multi-scale train/test - Modular design for coding-free exploration of new experiment settings - Extensive documentations including [annotated config](./doc/fully_annotated_config.py), [Fintuning Guide](./doc/FINETUNE.md) ### Recent Updates - Add RPN test (2019.05.28) - Add [NASFPN](https://github.com/TuSimple/simpledet/tree/master/models/NASFPN) (2019.06.04) - Add new ResNetV1b baselines from GluonCV (2019.06.07) - Add Cascade R-CNN with FPN backbone (2019.06.11) - Speed up FPN up to 70% (2019.06.16) - Update [NASFPN](https://github.com/TuSimple/simpledet/tree/master/models/NASFPN) to include larger models (2019.07.01) - Automatic BN fusion for fixed BN training, saving up to 50% GPU memory (2019.07.04) - Speed up MaskRCNN by 80% (2019.07.23) - Update MaskRCNN baselines (2019.07.25) - Add EfficientNet and DCN (2019.08.06) - Add python wheel for easy local installation (2019.08.20) - Add FitNet based Knowledge Distill (2019.08.27) - Add SE and train from scratch (2019.08.30) - Add a lot of docs (2019.09.03) - Add support for INT8 training(contributed by Xiaotao Chen & Jingqiu Zhou) (2019.10.24) - Add support for FCOS(contributed by Zhen Wei) (2019.11) - Add support for Mask Scoring RCNN(contributed by Zehui Chen) (2019.12) - Add support for RepPoints(contributed by Bo Ke) (2020.02) - Add support for FreeAnchor (2020.03) - Add support for Feature Pyramid Grids & PAFPN (2020.06) - Add support for CrowdHuman Dataset (2020.06) - Add support for [Double Pred](https://arxiv.org/abs/2003.09163) (2020.06) - Add support for [SEPC](https://github.com/TuSimple/simpledet/tree/master/models/sepc)(contributed by Qiaofei Li) (2020.07) ### Setup #### All-in-one Script We provide a [setup script](./scripts/setup.sh) for install simpledet and preppare the coco dataset. If you use this script, you can skip to the Quick Start. #### Install We provide a conda installation here for Debian/Ubuntu system. To use a pre-built docker or singularity images, please refer to [INSTALL.md](./doc/INSTALL.md) for more information. ```bash # install dependency sudo apt update && sudo apt install -y git wget make python3-dev libglib2.0-0 libsm6 libxext6 libxrender-dev unzip # create conda env conda create -n simpledet python=3.7 conda activate simpledet # fetch CUDA environment conda install cudatoolkit=10.1 # install python dependency pip install 'matplotlib<3.1' opencv-python pytz # download and intall pre-built wheel for CUDA 10.1 pip install https://1dv.aflat.top/mxnet_cu101-1.6.0b20191214-py2.py3-none-manylinux1_x86_64.whl # install pycocotools pip install 'git+https://github.com/RogerChern/cocoapi.git#subdirectory=PythonAPI' # install mxnext, a wrapper around MXNet symbolic API pip install 'git+https://github.com/RogerChern/mxnext#egg=mxnext' # get simpledet git clone https://github.com/tusimple/simpledet cd simpledet make # test simpledet installation mkdir -p experiments/faster_r50v1_fpn_1x python detection_infer_speed.py --config config/faster_r50v1_fpn_1x.py --shape 800 1333 ``` If the last line execute successfully, the average running speed of Faster R-CNN R-50 FPN will be reported. And you have successfuly setup SimpleDet. Now you can head up to the next section to prepare your dataset. #### Preparing Data We provide a step by step preparation for the COCO dataset below. `
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:50332234c66f5d28, topic:object-detection, desc:object detection, readme:object detection