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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
This repo is implemented based on detectron2 and centernet
| Date | Stars |
|---|---|
| 2026-07-24 | 556 |
| 2026-07-25 | 556 |
| 2026-07-28 | 556 |
| 2026-07-30 | 556 |
| 2026-08-06 | 556 |
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<img src="cplogo.png" width="300" > ## This repo is implemented based on [detectron2](https://github.com/facebookresearch/detectron2) and [CenterNet](https://github.com/xingyizhou/CenterNet) ## What\'s new - Support [imgaug](https://github.com/aleju/imgaug.git) data augmentation - Support [swa](https://pytorch.org/blog/stochastic-weight-averaging-in-pytorch/) - Support **Knowledge Distill**, teacher-student, designed by myself - Support other LR_SCHEDULER - Support Optimizer [RangerLars](https://github.com/pabloppp/pytorch-tools.git), not convergence in COCO - We provide some examples and scripts to convert centerX to Caffe, ONNX and TensorRT format in [projects/speedup](https://github.com/CPFLAME/centerX/tree/master/projects/speedup) ## What\'s comming - [️✔] Support simple inference - [✔] Support to caffe, onnx, tensorRT - [ ] Support keypoints ## Requirements - Python >= 3.7 - PyTorch >= 1.5 - torchvision that matches the PyTorch installation. - OpenCV - pycocotools ```shell pip install cython; pip install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' ``` - GCC >= 4.9 ```shell gcc --version ``` - detectron2 ```shell pip install -U 'git+https://github.com/CPFLAME/detectron2.git' ``` - [pytorch tools](https://github.com/pabloppp/pytorch-tools.git) ```shell pip install git+https://github.com/pabloppp/pytorch-tools -U ``` ### Data prepare the same as [detectron2](https://detectron2.readthedocs.io/tutorials/builtin_datasets.html) ### Training modify your yamls in run.sh ```shell sh run.sh ``` ### Testing and Evaluation modify your yamls in run.sh, add eval-only and MODEL.WEIGHTS in your setting ```shell sh run.sh ``` ## Performance ### coco This repo use less training time to get a competitive performance compared to other versions Backbone ResNet-50 | Code | mAP | epoch | | ---------------- | ---- | ----- | | centerX | 33.2 | 70 | | centerX | 34.3 | 140 | | centernet-better | 34.9 | 140 | Backbone ResNet-18 centerX_KD means ResNet-50(33.2) as teacher, ResNet-18(27.9) as student, Knowledge Distill for 70 epoch in coco. | Code | mAP | epoch | | ---------------- | ---- | ----- | | centerX | 30.2 | 140 | | centerX | 27.9 | 70 | | centerX_KD | 31.0 | 70 | | centernet-better | 29.8 | 140 | | centernet | 28.1 | 140 | ### crowd human - optim: SGD - lr: 0.02 - scheduler: WarmupMultiStepLR ,drop 0.1 in (50, 62) for 80 epoch; (90 ,120) for 140 epoch - train size: 512 max size - test size: 512 max size - batch size: 64 - woGT: KD only use teacher loss | Backbone | mAP | mAP50 | mAP75 | epoch | teacher | student_pretrain | | ---------------- | ---- | ----- | ----- | ----- | ----- | ------- | | resdcn18 | 31.2 | 56.6 | 30.8 | 80 | - | - | | resdcn18_swa | 31.1 | 56.6 | 30.4 | 80 | - | - | | resdcn18_syncBN | 31.3 | 56.6 | 30.7 | 80 | - | - | | resdcn18_imgaug | 29.6 | 54.7 | 28.9 | 80 | - | - | | resdcn18_KD | 34.5 | 60.2 | 34.3 | 80 | resdcn50| resdcn18| | resdcn18_KD_woGT | 33.0 | 58.3 | 32.7 | 80 | resdcn50| resdcn18| | resdcn18_KD_woGT_scratch | 32.8 | 58.1 | 32.6 | 140 | resdcn50| imagenet| | resdcn50 | 35.1 | 61.2 | 35.3 | 80 | - | - | ## KD exp ### crowd human KD Generalization performance for Knowledge Distill | Backbone | crowd mAP | coco_person mAP | epoch | teacher | student_pretrain | train_set | | ---------------- | ---- | -------------- | ----- | ----- | ------- | ----- | | resdcn50 | **35.1** | 35.7 | 80 | - | - | crowd | | resdcn18(baseline) | 31.2 | 31.2 | 80 | - | - | crowd | | resdcn18_KD | 3
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a27be00bd7f09727, topic:tensorrt, topic:onnx
matched fp:a27be00bd7f09727, topic:deep-learning
matched fp:a27be00bd7f09727, topic:object-detection