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yolov3 with mobilenetv2 and efficientnet
| Date | Stars |
|---|---|
| 2026-07-24 | 295 |
| 2026-07-25 | 295 |
| 2026-07-28 | 295 |
| 2026-07-30 | 295 |
| 2026-08-06 | 295 |
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# 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] <br> Example: <br> ``` voc_train_3998.txt ``` <br> 2. If you are using txt dataset, please format records like [image_path] [,[xmin ymin xmax ymax class]] <br>(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 <br> ``` bash python voc_annotation.py ``` to parse your data to tfrecords. <br> Example: <br> ``` /image/path 179 66 272 290 14 172 38 317 349 14 276 2 426 252 14 1 32 498 365 13 ``` <br> 3. Run: <br> ``` bash python main.py --mode=TRAIN --train_dataset_glob=<your dataset glob> --epochs=50 --epochs=50 --mode=TRAIN ``` ### Predict: ``` bash python main.py --mode=IMAGE --model=<your_model_path> ``` ### MAP: ``` bash python main.py --mode=MAP --model=<your_model_path> --test_dataset_glob=<your dataset glob> ``` ### Export serving model: ``` bash python main.py --mode=SERVING --model=<your_model_path> ``` ### 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 <br> 2. Open browser and enter [your_url:your_port]/tfjs <br> --- # 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 <br> Input size: 416*416 <br> Train Dataset: VOC2007 <br> Test Dataset: VOC2007 <br> mAP: <br> ``` 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 <br> CPU inference time (i7-8550U): 112ms <br> Model size: 37M <br> <br> Network: Efficientnet+Yolov3 <br> Input size: 380*380 <br> Train Dataset: VOC2007 <br> Test Dataset: VOC2007 <br> mAP: <br> ``` 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
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matched fp:48df889bf4a85730, topic:tensorflow