This is an implementation of CVPR 2019: "Multi-task Self-supervised Object Detection via Recycling of Bounding Box Annotations" This is a novel object detection approach that takes advantage of both multi-task learning (MTL) and self-supervised learning (SSL). We propose a set of auxiliary tasks that help improve the accuracy of object detection.
The code is modified from Tensorflow Object Detection API.
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Preparation
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Exporting a trained model for inference
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Quick Start: Jupyter notebook for off-the-shelf inference
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Training and Evaluation
| Model name (baseline/ours) | Detector | Backbone | Training | Eval | Baseline | Ours |
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| model11 / model12 | Faster R-CNN | ResNet101 | VOC 07 trainval | VOC 07 test | 77.0 | 78.7 |
| model21 / model22 | Faster R-CNN | ResNet101 | COCO 2017 train | COCO 2017 val | 32.7 | 34.6 |
| model31 / model32 | Faster R-CNN | ResNet101 | VOC 07+12 trainval | VOC 07 test | 81.7 | 83.7 |
| model41 / model42 | R-FCN | ResNet101 | VOC 07 trainval | VOC 07 test | 73.5 | 74.7 |
| model51 / model52 | Faster R-CNN | MobileNet | VOC 07 trainval | VOC 07 test | 61.2 | 63.8 |
| model61 / model62 | Faster R-CNN | Inception ResNet v2 | VOC 07 trainval | VOC 07 test | 80.7 | 81.8 |
| model71 / model72 | R-FCN | ResNet101 | VOC 07+12 trainval | VOC 07 test | 78.6 | 80.6 |
| model81 / model82 | Faster R-CNN | MobileNet | VOC 07+12 trainval | VOC 07 test | 68.6 | 70.8 |
| model91 / model92 | Faster R-CNN | Inception ResNet v2 | VOC 07+12 trainval | VOC 07 test | 84.3 | 86.0 |