mobilenetv2-yolov3
Tensorflow implementation mobilenetv2-yolov3 and efficientnet-yolov3 inspired by keras-yolo3
Backend:
MobilenetV2
Efficientnet
Darknet53
Callback:
mAP
Tensorboard extern callback
Loss:
MSE
GIOU
Adversarial loss
Train:
Cosine learning rate
Auto augment
Tensorflow:
Tensorflow2 Ready
tf.data pipeline
Convert model to tensorflow lite model
Multi GPU training
TPU support
TensorRT support
Serving:
Tensorflow Serving warm up request
Tensorflow Serving JAVA Client
Tensorflow Serving Python Client
Tensorflow Serving Service Control Client
Tensorflow Serving Server Build and Plugins develop
pip install -r requirements.txt
python main.py --help
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
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:
python main.py --mode=TRAIN --train_dataset_glob=<your dataset glob> --epochs=50 --epochs=50 --mode=TRAIN
python main.py --mode=IMAGE --model=<your_model_path>
python main.py --mode=MAP --model=<your_model_path> --test_dataset_glob=<your dataset glob>
python main.py --mode=SERVING --model=<your_model_path>
python main.py --config=mobilenetv2.yaml
Create a web server on project folder
Open browser and enter [your_url:your_port]/tfjs
Download pascal tfrecords from here.
Download pre-trained mobilenetv2-yolov3 model(VOC2007) here
Download pre-trained efficientnet-yolov3 model(VOC2007) here
Download pre-trained efficientnet-yolov3 model(VOC2007+2012) here
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
paper:
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
Bag of Freebies for Training Object Detection Neural Networks
Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression
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