Objects as Points + HarDNet

Object detection using center point detection:

Objects as Points

HarDNet: A Low Memory Traffic Network

Highlights

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.
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.
Efficient: CenterNet-HarDNet85 model achieves 44.3 COCO mAP (test-dev) while running at 45 FPS on an NVIDIA GTX-1080Ti GPU.
State of The Art: CenterNet-HarDNet85's is faster than YOLOv4, SpineNet-49, and EfficientDet-D2

Main results

Object Detection on COCO validation

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

Comparison with other state-of-the-art works

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

Installation

Please refer to INSTALL.md for installation instructions.

Use CenterNet

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

Real-time Demo on NVIDIA Jetson nano and AGX Xavier

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

Benchmark Evaluation and Training

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.

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