Pytorch-HarDNet

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

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)

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.

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.

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