Object detection using center point detection:
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Simple Algorithm: Object as a point is a simple and elegant approach for object detections, it models an object as a single point -- the center point of its bounding box.
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Simple Network: A U-shape HarDNet-85 with Conv3x3, ReLU, bilinear interpolation upsampling, and Sum-to-1 layer normalization comprise the whole network. There is NO dilation/deformable convolution, nor any novel activation function being used.
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Efficient: CenterNet-HarDNet85 model achieves 44.3 COCO mAP (test-dev) while running at 45 FPS on an NVIDIA GTX-1080Ti GPU.
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State of The Art: CenterNet-HarDNet85's is faster than YOLOv4, SpineNet-49, and EfficientDet-D2
| Backbone | #Param | GFLOPs | Train Size |
Input Size | mAP (val) |
mAP (test-dev) |
FPS (1080ti) |
Model |
|---|---|---|---|---|---|---|---|---|
| HarDNet85 | 37.2M | 123.9 | 608 | 608x608 | 44.9 | 45.3 | 32 | Download |
| HarDNet85 | 37.2M | 87.9 | 512 | 512x512 | 44.3 | 44.3 | 45 | Download |
| HarDNet85 | 37.2M | 58.0 | 512 | 416x416 | 42.4 | 42.4 | 53 | as above |
The model was trained with Pytorch 1.5.0 on two V100-32GB GPU for 300 epochs(eight days). Please see experiment for detailed hyperperameters. Using more GPUs may require sync-batchNorm to maintain the accuracy, and the learning rate may also need to adjust. You can also check if your training/val loss is roughly aligned with our log
HarDNet-85 results (no flipping) on COCO test-dev2017:
//512x512:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.443
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.629
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.483
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.235
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.476
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.590
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.355
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.579
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.612
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.381
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.660
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.805
//608x608:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.453
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.638
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.495
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.255
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.485
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.588
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.359
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.591
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.625
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.402
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.669
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.804
| Method | mAP(test-dev) | FPS @ GPU | Training epochs |
|---|---|---|---|
| CenterNet-HarDNet85 | 44.3 | 45 @ 1080Ti | 300 |
| YOLOv4 | 43.5 | 33 @ P100 | 300 |
| SpineNet-49 | 42.8 | 42 @ V100 | 350 |
| EfficientDet-D2 | 43.0 | 26.5 @ 2080Ti | 500 |
Please refer to INSTALL.md for installation instructions.
For object detection on images/ video, run:
python demo.py ctdet --demo /path/to/image/or/folder/or/video --arch hardnet_85 --load_model centernet_hardnet85_coco.pth
We provide example images in CenterNet_ROOT/images/ (from Detectron). If set up correctly, the output should look like
For webcam demo, run
python demo.py ctdet --demo webcam --arch hardnet_85 --load_model centernet_hardnet85_coco.pth
| Train Size | Input Size | COCO AP(val) |
AP-s | AP-m | AP-L | FP16 TRT model: nano (Latency) |
FP16 TRT model: Xavier (Latency) |
|---|---|---|---|---|---|---|---|
| 512x512 | 512x512 | 43.5 | 24.5 | 47.6 | 59.4 | - | Download (49 ms) |
| 512x512 | 416x416 | 41.5 | 20.2 | 45.1 | 59.7 | Download (342 ms) | Download (37 ms) |
| 512x512 | 416x320 | 39.5 | 17.9 | 42.7 | 59.4 | Download (261 ms) | Download (31 ms) |
| 512x512 | 320x320 | 37.3 | 15.1 | 40.4 | 58.4 | Download (210 ms) | Download (25 ms) |
| 512x512 | 256x256 | 33.0 | 11.3 | 34.4 | 56.8 | Download (117 ms) | Download (17 ms) |
| 512x512 | 224x224 | 30.1 | 8.9 | 30.4 | 54.0 | Download (105 ms) | Download (16 ms) |
- Above models are converted from previous 43.6 mAP model (test-dev). For the latest 44.3 mAP model, please convert it from pytorch model
- Install NVIDIA JetPack 4.4 (TensorRT 7.1)
- Install Pytorch > 1.3 for onnx opset 11 and pycuda
- Run following commands with or without the above trt models. It will convert the pytorch model into onnx and TRT model when loading model with --load_model.
- For Jetson nano, please increase swap size to avoid freeze when building your own engines on the target (See instructions)
# Demo
python demo_trt.py ctdet --demo webcam --arch hardnet_85 --load_trt ctdet_hardnet_85_416x320_xavier.trt --input_w 416 --input_h 320
# or run with any size (divided by 32) by converting a new trt model:
python demo_trt.py ctdet --demo webcam --arch hardnet_85 --load_model centernet_hardnet85_coco.pth --input_w 480 --input_h 480
# You can also run test on COCO val set with trt model, which will get ~43.2 mAP for FP16 mode:
python test_trt.py ctdet --arch hardnet_85 --load_trt ctdet_hardnet_85_512x512_xavier.trt
After installation, follow the instructions in DATA.md to setup the datasets. Then check GETTING_STARTED.md to reproduce the results in the paper. We provide scripts for all the experiments in the experiments folder.
Please see original CenterNet repo
For CenterNet Citation, please see original CenterNet repo
For HarDNet Citation, please see HarDNet repo
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