# mobilenetv2-yolov3 **Repository Path**: KeRi/mobilenetv2-yolov3 ## Basic Information - **Project Name**: mobilenetv2-yolov3 - **Description**: No description available - **Primary Language**: Python - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 2 - **Created**: 2021-01-19 - **Last Updated**: 2021-01-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## 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 Train: - [x] Multi scale image size - [x] Cosine learning rate Tensorflow: - [x] Tensorflow2 Ready - [x] Use tf.data to load dataset - [x] Use tfds to load dataset - [x] Remove image shape input when use session - [ ] Convert model to tensorflow lite model - [x] Multi GPU training - [ ] TPU support Serving: - [x] Tensorflow Serving warm up request - [x] Tensorflow Serving JAVA Client - [x] Tensorflow Serving Python Client - [x] Tensorflow Serving Service Control Client - [ ] 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= ``` ### Predict: ``` bash python main.py --mode=IMAGE --model= ``` ### 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: 0.585270123970341 bicycle ap: 0.7311717479746895 bird ap: 0.6228634475289679 boat ap: 0.44729361226611786 bottle ap: 0.3524265151288485 bus ap: 0.7260233058709467 car ap: 0.7572503412774444 cat ap: 0.8443930169586521 chair ap: 0.3530240979604032 cow ap: 0.5680746465428056 diningtable ap: 0.6046673143934721 dog ap: 0.8096497542858805 horse ap: 0.785358647511358 motorbike ap: 0.7299038925396009 person ap: 0.6926967393665762 pottedplant ap: 0.2960290730045794 sheep ap: 0.5569735405574012 sofa ap: 0.6053534702293342 train ap: 0.7304618425853895 tvmonitor ap: 0.5983913977616169 mAP: 0.6198638263857212 ``` 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: 0.6492260838166934 bicycle ap: 0.8010712280076165 bird ap: 0.7013865117634108 boat ap: 0.5557173155813903 bottle ap: 0.4353563564340365 bus ap: 0.753804699972881 car ap: 0.7878183961387922 cat ap: 0.8632726491920759 chair ap: 0.4090719340574334 cow ap: 0.6657089830054761 diningtable ap: 0.6513494390619038 dog ap: 0.8466486584164448 horse ap: 0.8328765157511936 motorbike ap: 0.7607912651726462 person ap: 0.7089970516297166 pottedplant ap: 0.32875322571854027 sheep ap: 0.6372370950276296 sofa ap: 0.675301446703759 train ap: 0.7734685594308568 tvmonitor ap: 0.6505409659737674 mAP: 0.6744199190428132 ``` 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: 0.8186380791530123 bicycle ap: 0.778370501901752 bird ap: 0.8040658409051149 boat ap: 0.6606796907615438 bottle ap: 0.5338128542328597 bus ap: 0.8516086793836817 car ap: 0.8247881435224634 cat ap: 0.9271784386863242 chair ap: 0.5344565229671414 cow ap: 0.7724057669182698 diningtable ap: 0.701598520527006 dog ap: 0.9052246177009002 horse ap: 0.8477206181813397 motorbike ap: 0.8275932123398402 person ap: 0.7605203479510053 pottedplant ap: 0.45979410517062475 sheep ap: 0.8301611044152797 sofa ap: 0.7393617389123919 train ap: 0.8817430073959469 tvmonitor ap: 0.6981047903116634 mAP: 0.757891329066908 ``` 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)