Initial ImageNet pretrained weights

Initial ImageNet pretrained weights for 1.5m, 3m, 5m and 9m variants can now be downloaded from the assets below.

ImageNet Result:

Method	#Params	ImageNet	ImageNet-Real-Labels
SimpleNetV1_imagenet(36.23 MB)	9.5m	74.17/91.614	81.24/94.63
SimpleNetV1_imagenet(21.91 MB)	5.7m	71.936/90.3	79.12/93.68
SimpleNetV1_imagenet(12.52 MB)	3m	68.15/87.762	75.66/91.80
SimpleNetV1_imagenet(5.73 MB)	1.5m	61.524/83.43	69.11/88.10

Note 1

These models are converted from their Pytorch counterparts through onnx runtime.
The respective models can be accessed from our Official Pytorch repository.

Note 2

Please note that since models are converted from onnx to caffe, the mean, std and crop ratio used are as follows:

DEFAULT_CROP_PCT = 0.875
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)

Also note that images were not channel swapped during training so you dont need to do any channel swap either.
You also DO NOT need to rescale the input to [0-255].

Initial ImageNet Models

Initial ImageNet models -1.5m, 3m and 5m models.

ImageNet pretrained weights

Initial ImageNet pretrained weights for 1.5m, 3m, 5m and 9m variants can now be downloaded from the assets below.

m2 variants:

Method	#Params	ImageNet	ImageNet-Real-Labels
simplenetv1_9m_m2(36 MB)	9.5m	74.23/91.748	81.22/94.756
simplenetv1_5m_m2(22 MB)	5.7m	72.03/90.324	79.328/93.714
simplenetv1_small_m2_075(12 MB)	3m	68.506/88.15	76.283/92.02
simplenetv1_small_m2_05(5 MB)	1.5m	61.67/83.488	69.31/ 88.195

m1 variants:

Method	#Params	ImageNet	ImageNet-Real-Labels
simplenetv1_9m_m1(36 MB)	9.5m	73.792/91.486	81.196/94.512
simplenetv1_5m_m1(21 MB)	5.7m	71.548/89.94	79.076/93.36
simplenetv1_small_m1_075(12 MB)	3m	67.784/87.718	75.448/91.69
simplenetv1_small_m1_05(5 MB)	1.5m	61.122/82.988	68.58/87.64

Note 1

These models are converted from their Pytorch counterparts through onnx runtime.
The respective models can be accessed from our Official Pytorch repository.

Note 2

Please note that since models are converted from onnx to caffe, the mean, std and crop ratio used are as follows:

DEFAULT_CROP_PCT = 0.875
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)

Also note that images were not channel swapped during training so you dont need to do any channel swap either.
You also DO NOT need to rescale the input to [0-255].