A Keras implementation of YOLOv3 (Tensorflow backend) inspired by allanzelener/YAD2K.
- Mobilenet
- VGG16
- ResNet101
- ResNeXt101
| Model name | InputSize | TrainSet | TestSet | mAP | Speed | Ps |
|---|---|---|---|---|---|---|
| YOLOv3-Mobilenet | 320x320 | VOC07 | VOC07 | 64.22% | 29fps | Keras on 1080Ti |
| YOLOv3-Mobilenet | 320x320 | VOC07+12 | VOC07 | 74.56% | 29fps | Keras on 1080Ti |
| YOLOv3-Mobilenet | 416x416 | VOC07+12 | VOC07 | 76.82% | 25fps | Keras on 1080Ti |
| MobileNet-SSD | 300x300 | VOC07+12+coco | VOC07 | 72.7% | (unknown) | |
| MobileNet-SSD | 300x300 | VOC07+12 | VOC07 | 68% | (unknown) | |
| Faster RCNN, VGG-16 | ~1000x600 | VOC07+12 | VOC07 | 73.2% | 151ms | Caffe on Titan X |
| SSD,VGG-16 | 300x300 | VOC07+12 | VOC07 | 77.5% | 39fps | Keras on Titan X |
- Compared with MobileNet-SSD, YOLOv3-Mobilenet is much better on VOC2007 test, even without pre-training on Ms-COCO
- I use the default anchor size that the author cluster on COCO with inputsize of 416*416, whereas the anchors for VOC 320 input should be smaller. The change of anchor size could gain performance improvement.
- Evaluation on https://github.com/Adamdad/Object-Detection-Metrics.git
- I only use the pure model of YOLOv3-Mobilenet with no additional tricks.
1.train_Mobilenet.py
- Code for training
- I change some of the code to read in the annotaions seperately (train.txt and val.txt), remember to change that, and the .txt file are in the same form descibed below
2.yolo3/model_Mobilenet.py
- Model_Mobilenet is the yolo model based on Mobilenet
- If you want to go through the source code,ignore the other function,please see the yolo_body (I extract three layers from the Mobilenet to make the prediction)
3.yolo_Mobilenet.py
- Testing on images
Be sure that you do not load pretrained model when training because I did it on keras_applications,and the keras will load the pretrained model for you
Please use this repo to draw the RP curve calculate the MAP https://github.com/Adamdad/Object-Detection-Metrics.git
[this is the guide for darknet53 not mobilenet]
- Download YOLOv3 weights from YOLO website.
- Convert the Darknet YOLO model to a Keras model.
- Run YOLO detection.
wget https://pjreddie.com/media/files/yolov3.weights
python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5
python yolo_video.py [OPTIONS...] --image, for image detection mode, OR
python yolo_video.py [video_path] [output_path (optional)]
For Tiny YOLOv3, just do in a similar way, just specify model path and anchor path with --model model_file and --anchors anchor_file.
Use --help to see usage of yolo_video.py:
usage: yolo_video.py [-h] [--model MODEL] [--anchors ANCHORS]
[--classes CLASSES] [--gpu_num GPU_NUM] [--image]
[--input] [--output]
positional arguments:
--input Video input path
--output Video output path
optional arguments:
-h, --help show this help message and exit
--model MODEL path to model weight file, default model_data/yolo.h5
--anchors ANCHORS path to anchor definitions, default
model_data/yolo_anchors.txt
--classes CLASSES path to class definitions, default
model_data/coco_classes.txt
--gpu_num GPU_NUM Number of GPU to use, default 1
--image Image detection mode, will ignore all positional arguments
- MultiGPU usage: use
--gpu_num Nto use N GPUs. It is passed to the Keras multi_gpu_model().
-
Generate your own annotation file and class names file.
One row for one image;
Row format:image_file_path box1 box2 ... boxN;
Box format:x_min,y_min,x_max,y_max,class_id(no space).
For VOC dataset, trypython voc_annotation.py
Here is an example:path/to/img1.jpg 50,100,150,200,0 30,50,200,120,3 path/to/img2.jpg 120,300,250,600,2 ... -
Make sure you have run
python convert.py -w yolov3.cfg yolov3.weights model_data/yolo_weights.h5
The file model_data/yolo_weights.h5 is used to load pretrained weights. -
Modify train.py and start training.
python train.py
Use your trained weights or checkpoint weights with command line option--model model_filewhen using yolo_video.py Remember to modify class path or anchor path, with--classes class_fileand--anchors anchor_file.
If you want to use original pretrained weights for YOLOv3:
1. wget https://pjreddie.com/media/files/darknet53.conv.74
2. rename it as darknet53.weights
3. python convert.py -w darknet53.cfg darknet53.weights model_data/darknet53_weights.h5
4. use model_data/darknet53_weights.h5 in train.py
-
The test environment is
- Python 3.5.2
- Keras 2.1.5
- tensorflow 1.6.0
-
Default anchors are used. If you use your own anchors, probably some changes are needed.
-
The inference result is not totally the same as Darknet but the difference is small.
-
The speed is slower than Darknet. Replacing PIL with opencv may help a little.
-
Always load pretrained weights and freeze layers in the first stage of training. Or try Darknet training. It's OK if there is a mismatch warning.
-
The training strategy is for reference only. Adjust it according to your dataset and your goal. And add further strategy if needed.
-
For speeding up the training process with frozen layers train_bottleneck.py can be used. It will compute the bottleneck features of the frozen model first and then only trains the last layers. This makes training on CPU possible in a reasonable time. See this for more information on bottleneck features.
Please cite MobileNet-YOLO in your publications if it helps your research:
@article{MobileNet-Yolov3,
Author = {Adam Yang},
Year = {2018}
}
@article{yolov3,
title={YOLOv3: An Incremental Improvement},
author={Redmon, Joseph and Farhadi, Ali},
journal = {arXiv},
year={2018}
}
@article{mobilenets,
title={MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications},
author={Andrew G. Howard, Menglong Zhu, Bo Chen,Dmitry Kalenichenko,Weijun Wang, Tobias Weyand,Marco Andreetto, Hartwig Adam},
journal = {arXiv},
year = {2017}
}
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