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.
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.
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,
}| 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 |
| 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 |
Tensorflow 1.X