MNASNET Tensorflow

An unofficial implementation of MNASNet. Use Tensorflow Slim. MNASNet is a CNN model proposed by Google for mobile devices.It is faster and more accurate than previous models, such as Mobilenet V2 and Shufflenet V2.

References: MnasNet: Platform-Aware Neural Architecture Search for Mobile by Mingxing Tan, et. al.

Object Detection

We tested the replacement of ResNet with MNASNet-b1 as the backbone of RetinaNet.Evaluation on the MSCOCO17 val after training on the MSCOCO17 train dataset.We have achieved a mAP of 33.7.We have achieved a mAP of 33.7, which is slightly better than using MobileNet V2 as the backbone(33.7 vs 33.4).All work on object detection uses the object detection api of tensorflow.We just wrote a feature extractor and related configuration files.

How To Use

The basic implementation of Mnasnet is in the /mnasnet directory. If you want to use it, copy it to the /slim/nets directory, then add mnasnet to nets_factory.py.

networks_map = {
                #......
                'mnasnet_a1':mnasnet.mnasnet_a1,
                'mnasnet_b1':mnasnet.mnasnet_b1
               }

arg_scopes_map = {
                  #......
                  'mnasnet_a1':mnasnet.training_scope,
                  'mnasnet_b1':mnasnet.training_scope,
                 }

If you want to use mnasnet in the object detection api, we provide the implementation of retinanet.Copy the files in the /object_detection/models directory to where the object detection api is located, then add the model to model_builder.py.The configuration file is provided in the /object_detection/samples/configs/ directory

SSD_FEATURE_EXTRACTOR_CLASS_MAP = {
    #......
    'ssd_mnasnet_a1_fpn':SSDMNASNetA1FpnFeatureExtractor,
    'ssd_mnasnet_b1_fpn':SSDMNASNetB1FpnFeatureExtractor,
}

Pretrained models

Imagenet Model

Model	Input Size	Depth Multiplier	Top-1 Accuracy	Top-5 Accuracy	Pixel 1 latency (ms)	DownLoad Link
mnasnet-b1	224*224	1.0	74.094	92.002	TBA	mnasnet_b1_1.0_224.tar
mnasnet-a1	224*224	1.0	TBA	TBA	TBA	TBA

MSCOCO17 Model

Model	Input Size	Depth Multiplier	mAP(IOU 0.05:0.95)	mAP.L	mAP.M	mAP.S	Pixel 1 latency (ms)	DownLoad Link
ssd-mnasnet-b1-fpn	640*640	1.0	33.7	47.7	30.5	11.7	TBA	ssd_mnasnet_b1_fpn_shared_box_predictor_640x640_coco17_2019_03_21.tar
ssd-mobilenet-v1-fpn	640*640	1.0	32.5	46.0	29.2	10.1	TBA	ssd_mobilenet_v1_fpn_shared_box_predictor_640x640_coco17_2019_03_21.tar
ssd-mobilenet-v2-fpn	640*640	1.0	33.4	47.2	30.5	10.7	TBA	ssd_mobilenet_v2_fpn_shared_box_predictor_640x640_coco17_2019_03_21.tar

System Requirement

Tensorflow 1.X

TODO List

Upload the code for object detection
Freeze the object detection model and upload it
Training the MNASNet A1 model (in progress, estimated to take 60 days)
Evaluation on mobile devices (I am sorry that I don't have a Google Pixel 1 phone, I might buy a newer phone for evaluation)
Provide more pre-trained models (our limited computing power)
Send PR to tensorflow

License

Apache License 2.0

bhupathyap / mnasnet_tensorflow Goto Github PK