Pytorch-HarDNet

Harmonic DenseNet: A low memory traffic network (ICCV 2019 paper)

See also CenterNet-HarDNet for Object Detection in 44.3 mAP / 45 fps on COCO dataset

and FC-HarDNet for Semantic Segmentation

• Fully utilize your cuda cores!
• Unlike CNN models using a lot of Conv1x1 to reduce model size and number of MACs, HarDNet mainly uses Conv3x3 (with only one Conv1x1 layer for each HarDNet block) to increase the computational density.
• Increased computational density changes a model from Memory-Bound to Compute-Bound

Architecture

HarDNet Block:

• k = growth rate (as in DenseNet)
• m = channel weighting factor (1.6~1.7)
• Conv3x3 for all layers (no bottleneck layer)
• Conv-BN-ReLU for all layers intead of BN-ReLU-Conv used in DenseNet
• See MIPT-Oulu/pytorch_bn_fusion to get rid of BatchNorm for inference.
• No global dense connection (input of a HarDBlk is NOT reused as a part of output)

HarDNet68/85:

• Enhanced local feature extraction to benefit the detection of small objects
• A transitional Conv1x1 layer is employed after each HarDNet block (HarDBlk)

Results

Method	MParam	GMACs	Inference Time*	ImageNet Top-1	COCO mAP with SSD512
HarDNet68	17.6	4.3	22.5 ms	76.5	31.7
ResNet-50	25.6	4.1	31.0 ms	76.2	-
HarDNet85	36.7	9.1	38.0 ms	78.0	35.1
ResNet-101	44.6	7.8	51.2 ms	78.0	31.2
VGG-16	138	15.5	49 ms	73.4	28.8

* Inference time measured on an NVidia 1080ti with pytorch 1.1.0
300 iteraions of random 1024x1024 input images are averaged.

Results of Depthwise Separable (DS) version of HarDNet

Method	MParam	GMACs	Inference Time**	ImageNet Top-1
HarDNet39DS	3.5	0.44	32.5 ms	72.1
MobileNetV2	3.5	0.3	37.9 ms	72.0
HarDNet68DS	4.2	0.8	52.6 ms	74.3
MobileNetV2 1.4x	6.1	0.6	57.8 ms	74.7

** Inference time measured on an NVidia Jetson nano with TensorRT
500 iteraions of random 320x320 input images are averaged.

Train HarDNet models for ImageNet

Training prodedure is branched from https://github.com/pytorch/examples/tree/master/imagenet

Training:

python main.py -a hardnet68 [imagenet-folder with train and val folders]

arch = hardnet39ds | hardnet68ds | hardnet68 | hardnet85

Evaluating:

python main.py -a hardnet68 --pretrained -e [imagenet-folder with train and val folders]

for HarDNet85, please download pretrained weights from here

Hyperparameters

• epochs 150 ~ 250
• initial lr = 0.05
• batch size = 256
• weight decay = 6e-5
• cosine learning rate decay
• nestrov = True