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VoVNet, MobileNet, ShuffleNet, HarDNet, GhostNet, EfficientNet backbone networks and SKU-110K dataset for detectron2

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This repository is based on VoVNet-v2

Faster R-CNN on SKU-110K dataset

Note

We measure the inference time of all models with batch size 1 on the same RTX2080Ti GPU machine.

  • pytorch1.4.0
  • CUDA 10.2
  • cuDNN 7.3

Lightweight with FPNLite

Backbone Param. lr sched inference time AP AP75 AP50 download
MobileNetV2-0.5-64 N/A 1x 0.033 43.31 44.66 78.08 model | metrics
MobileNetV2-0.5 N/A 1x 0.037 42.93 44.27 77.31 model | metrics
MobileNetV2 3.5M 3x 0.031 52.11 58.72 85.98 model | metrics
MobileNetV2 3.5M 1x 0.031 51.20 56.93 85.71 model | metrics
MobileNetV2-FLGC N/A 1x 0.030 50.59 56.05 85.21 model | metrics
ShuffleNetV2-0.5 N/A 1x 0.039 48.24 52.95 82.10 model | metrics
ShuffleNetV2 N/A 1x 0.028 52.60 59.55 86.19 model | metrics
V2-19 11.2M 1x 0.034 41.46 44.97 71.32 model | metrics
V2-19-DW 6.5M 1x N/A N/A N/A N/A model | metrics
V2-19-Slim 3.1M 1x 0.027 47.68 51.47 82.36 model | metrics
V2-19-Slim-DW 1.8M 3x N/A N/A N/A N/A model | metrics
  • 64 FPN.OUT_CHANNELS = 64
  • DW and Slim denote depthwise separable convolution and a thiner model with half the channel size, respectively.

FPN

Backbone Param. lr sched inference time AP AP75 AP50 download
V2-19-FPN 37.6M 3x N/A N/A N/A N/A model | metrics
R-50-FPN 51.2M 3x N/A N/A N/A N/A model | metrics
V2-39-FPN 52.6M 3x 0.071 51.47 57.5 85.5 model | metrics

Using this command with --num-gpus 1

python /path/to/sku110/train_net.py --config-file /path/to/sku110/configs/<config.yaml> --eval-only --num-gpus 1 MODEL.WEIGHTS <model.pth>

Installation

As this repository is implemented as a extension form (detectron2/projects) upon detectron2, you just install detectron2 following INSTALL.md.

Prepare for SKU-110K dataset:

  • To download dataset, please visit here
  • Extract the file downloaded to datasets/sku110/images
  • Extract datasets/sku110/Annotations.zip, there are 2 folders Annotations and ImageSets

Training

To train a model, run

python /path/to/sku110/train_net.py --config-file /path/to/sku110/configs/<config.yaml>

For example, to launch end-to-end Faster R-CNN training with VoVNetV2-39 backbone on 8 GPUs, one should execute:

python /path/to/sku110/train_net.py --config-file /path/to/sku110/configs/faster_rcnn_V_39_FPN_3x.yaml --num-gpus 8

Evaluation

Model evaluation can be done similarly:

python /path/to/sku110/train_net.py --config-file /path/to/sku110/configs/faster_rcnn_V_39_FPN_3x.yaml --eval-only MODEL.WEIGHTS <model.pth>

Visualization

To visual the result, run

python /path/to/sku110/demo.py --config-file /path/to/sku110/configs/faster_rcnn_V_39_FPN_3x.yaml --input image.jpg --output image.jpg MODEL.WEIGHTS <model.pth>

Citing VoVNet

If you use VoVNet, please use the following BibTeX entry.

@inproceedings{lee2019energy,
  title = {An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection},
  author = {Lee, Youngwan and Hwang, Joong-won and Lee, Sangrok and Bae, Yuseok and Park, Jongyoul},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  year = {2019}
}

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VoVNet, MobileNet, ShuffleNet, HarDNet, GhostNet, EfficientNet backbone networks and SKU-110K dataset for detectron2

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