A YOLOv3 based digits detector. Based on the amazing work of qqwweee
! THIS IS A WORK IN PROGRESS, MODEL AVAILABLE SOON
The Street View House Numbers (SVHN) dataset is used for transfer learning.
From the website we see that there are : "73257 digits for training, 26032 digits for testing, and 531131 additional, somewhat less difficult samples, to use as extra training data"
Annotations PASCAL format: https://github.com/penny4860/svhn-voc-annotation-format
Annotations PASCAL format AND txt format (ready for training) http://download939.mediafire.com/ouwinissq9ng/ydg1i5qbm27mdax/annotations.tar.xz
cd <path_to_repo_root>/data/
wget http://download939.mediafire.com/ouwinissq9ng/ydg1i5qbm27mdax/annotations.tar.xz
tar -xf annotations.tar.xz
rm annotations.tar.xz
- 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 N
to 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_file
when using yolo_video.py Remember to modify class path or anchor path, with--classes class_file
and--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
- Convert .mat file to txt for YOLO
- [] Transfer learning on digits
- [] Add mAP to the projects for evaluation
- [] Prune the network
- [] Quantize the network
- [] Convert to tf