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https://github.com/fsx950223/mobilenetv2-yolov3

yolov3 with mobilenetv2 and efficientnet
https://github.com/fsx950223/mobilenetv2-yolov3

darknet53 efficientnet keras mobilenetv2 tensorflow tf2 yolov3

Last synced: 5 days ago
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yolov3 with mobilenetv2 and efficientnet

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README 

        
# Mobilenetv2-Yolov3

Tensorflow implementation mobilenetv2-yolov3 and efficientnet-yolov3 inspired by [keras-yolo3](https://github.com/qqwweee/keras-yolo3.git)



---

# Update

Backend:

- [x] MobilenetV2

- [x] Efficientnet

- [x] Darknet53



Callback:

- [x] mAP

- [ ] Tensorboard extern callback



Loss:

- [x] MSE

- [x] GIOU

- [x] Adversarial loss



Train:

- [x] Cosine learning rate

- [x] Auto augment



Tensorflow:

- [x] Tensorflow2 Ready

- [x] tf.data pipeline

- [ ] Convert model to tensorflow lite model

- [x] Multi GPU training

- [ ] TPU support

- [x] TensorRT support



Serving:

- [x] Tensorflow Serving warm up request

- [x] Tensorflow Serving JAVA Client

- [x] Tensorflow Serving Python Client

- [x] Tensorflow Serving Service Control Client

- [x] Tensorflow Serving Server Build and Plugins develop 



---



# Usage

### Install:

``` bash

pip install -r requirements.txt

```

### Get help info:

``` bash

python main.py --help

```

### Train:

1. Format file name like [name]_[number].[extension] 


Example: 


```

voc_train_3998.txt

```




2. If you are using txt dataset, please format records like [image_path] [,[xmin ymin xmax ymax class]] 
(for convenience, you can modify voc_text.py to parse your data to specific data format), else you should modify voc_annotation.py, then run 



``` bash

python voc_annotation.py

``` 

to parse your data to tfrecords. 


Example: 



```

/image/path 179 66 272 290 14 172 38 317 349 14 276 2 426 252 14 1 32 498 365 13

```




3. Run: 



``` bash

python main.py --mode=TRAIN --train_dataset_glob= --epochs=50 --epochs=50 --mode=TRAIN

```



### Predict:

``` bash

python main.py --mode=IMAGE --model=

```



### MAP:

``` bash

python main.py --mode=MAP --model= --test_dataset_glob=

```



### Export serving model:

``` bash

python main.py --mode=SERVING --model=

```



### Use custom config file:

``` bash

python main.py --config=mobilenetv2.yaml

```



---

# Set up tensorflow.js model (Live Demo: https://fsx950223.github.io/mobilenetv2-yolov3/tfjs/)

1. Create a web server on project folder 


2. Open browser and enter [your_url:your_port]/tfjs 



---

# Resources

* Download pascal tfrecords from [here](https://drive.google.com/drive/folders/172sH75LPeUd2yyzAnrce0LLe2UR_kFqF).

* Download pre-trained mobilenetv2-yolov3 model(VOC2007) [here](https://drive.google.com/open?id=1B0vVQsuWY-zfuyol38-R5XJs1mntIwqZ)

* Download pre-trained efficientnet-yolov3 model(VOC2007) [here](https://drive.google.com/open?id=10A2BqNrQp5_hIcBzGXu6Xiv4mCQzga2q)

* Download pre-trained efficientnet-yolov3 model(VOC2007+2012) [here](https://drive.google.com/open?id=1dYfi1z5EeNsXMLACwoeR4jGj7RWyCcZp)



---



# Performance

Network: Mobilenetv2+Yolov3 


Input size: 416*416 


Train Dataset: VOC2007 


Test Dataset: VOC2007 


mAP: 



```

aeroplane ap:  0.6721874861775297

bicycle ap:  0.7844226664948993

bird ap:  0.6863393529648882

boat ap:  0.5102715372530052

bottle ap:  0.4098093697072679

bus ap:  0.7646277543282962

car ap:  0.8000339732789448

cat ap:  0.8681120849855787

chair ap:  0.4021823009684314

cow ap:  0.6768311030872428

diningtable ap:  0.626045232887253

dog ap:  0.8293983813984888

horse ap:  0.8315961581768014

motorbike ap:  0.771283337747543

person ap:  0.7298645793931624

pottedplant ap:  0.3081565644702266

sheep ap:  0.6510012751038824

sofa ap:  0.6442699680945367

train ap:  0.8025086962000969

tvmonitor ap:  0.6239227675451299

mAP:  0.6696432295131602

```

GPU inference time (GTX1080Ti): 19ms 


CPU inference time (i7-8550U): 112ms 


Model size: 37M 





Network: Efficientnet+Yolov3 


Input size: 380*380 


Train Dataset: VOC2007 


Test Dataset: VOC2007 


mAP: 



```

aeroplane ap:  0.7770436248733187

bicycle ap:  0.822183784348553

bird ap:  0.7346967323068865

boat ap:  0.6142903989882571

bottle ap:  0.4518063126765959

bus ap:  0.782237197681936

car ap:  0.8138978890046222

cat ap:  0.8800232369515162

chair ap:  0.4531520519719176

cow ap: 0.6992367978932157

diningtable ap:  0.6765065569475968

dog ap:  0.8612118810883834

horse ap:  0.8559580684256001

motorbike ap:  0.8027311717682002

person ap:  0.7280218883512792

pottedplant ap:  0.35520418960051925

sheep ap:  0.6833401035128458

sofa ap:  0.6753841073186044

train ap:  0.8107647793504738

tvmonitor ap:  0.6726791558585905

mAP:  0.7075184964459456

```

GPU inference time (GTX1080Ti): 23ms 


CPU inference time (i7-8550U): 168ms 


Model size: 77M 





Network: Efficientnet+Yolov3 


Input size: 380*380 


Train Dataset: VOC2007+VOC2012 


Test Dataset: VOC2007 


mAP: 



```

aeroplane ap:  0.8572154850266848

bicycle ap:  0.8129962658687486

bird ap:  0.8325678324285539

boat ap:  0.7061501348114156

bottle ap:  0.5603823420846883

bus ap:  0.8536452418769342

car ap:  0.8395446870008888

cat ap:  0.9200504816535645

chair ap:  0.514644868267842

cow ap:  0.8202171886452714

diningtable ap:  0.7370149790284737

dog ap:  0.900374518831019

horse ap:  0.8632567146990895

motorbike ap:  0.8147344820261591

person ap:  0.7690434789031615

pottedplant ap:  0.4576271726152926

sheep ap:  0.8006580581981677

sofa ap:  0.7478146395952494

train ap:  0.8783508559769437

tvmonitor ap:  0.6923886096918628

mAP:  0.7689339018615006

```

GPU inference time (GTX1080Ti): 23ms 


CPU inference time (i7-8550U): 168ms 


Model size: 77M 



---



# Reference

paper: 


- [YOLOv3: An Incremental Improvement](https://arxiv.org/abs/1804.02767)


- [An Analysis of Scale Invariance in Object Detection - SNIP](https://arxiv.org/abs/1711.08189)


- [Scale-Aware Trident Networks for Object Detection](https://arxiv.org/abs/1901.01892)


- [Understanding the Effective Receptive Field in Deep Convolutional Neural Networks](https://arxiv.org/abs/1701.04128)


- [Bag of Freebies for Training Object Detection Neural Networks](https://arxiv.org/pdf/1902.04103.pdf)


- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://arxiv.org/abs/1902.09630)


- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381)