oddnet (Object Detection Net, the extra d is for branding) 1. Update or install imgaug $ pip uninstall -y imgaug && pip install git+https://github.com/aleju/imgaug.git
$ git clone https://github.com/asceznyk/oddnet.git
3. Change directory to oddnet $ mkdir outputs/ gtruths/model
outputs is for storing model predictions
gtruths is for stroing actual images with the ground truth boxes
5. Download pre-trained weights 6. Download the .cfg file 7. Download pre-trained darknet53.conv.74 weights (for custom object detection) 1. images must be .jpg/.png files
any other format won't work
2. create a .names file to store all the class names like so:
each line in the .names file should be used for exactly 1 class
make sure that it is in a particular order because the model will assign indexes to the classes as follows:
class1 -> 0
class2 -> 1
class3 -> 2
and so on ...
3. labels must be .txt files containing the boxes in follwing format:
id: the index of the class
(x, y, w, h): the xy coordinates and the width and height for the ground truth box (each of these values must be between 0 and 1)
Plain Object Detection
Custom Object Detection
$ python detect.py --testdir test --names yournames.labels --cfg yolov3.cfg --weights yolov3.weights --savedir outputs --boxdir gtruths
testdir: path to the directory with all the test images ONLY
names: path to the .names file
cfg: path to model configuration file (this is for model architecture)
use the yolov3.cfg file you downloaded
weights: path to .pth or a .weights file you can get this from here
use the yolov3.weights file you downloaded
savedir: path to directory to save all the predictions of the model
boxdir (optional): path to directory to save all the actual images wit ground truth boxes
1. Create the custom model with number of classes $ bash createmodel.sh nclasses
here nclasses is the number of classes
running this command will create a file called yolov3custom.cfg (this will be used in the next step)
$ !python3 train.py --traindir train/ --validdir valid/ --cfg yolov3custom.cfg --ptweights darknet53.conv.74 --epochs 100 --ckptpth pathtochekpt.pth --lossfn bboxloss --patience 1000
traindir: path to the directory containing all the training images AND labels
validdir: path to the directory containing all the validation images AND labels
cfg: path to the custom model config file created by createmodel.sh (use the yolov3custom.cfg from the previous step)
ptweights: path to pre-trained weights (use the darknet53.conv.74 weights downloaded, refer step 7 here
epochs: number of epochs to train the model
ckptpth: checkpoint path, the path to save your model while training
lossfn: loss function, you have two loss functions bboxloss and iouloss, it is best to use bboxloss because iouloss is broken (if you can find a creative way to fix, great! please ping me on [email protected]
patience: this is the number of epochs to wait on stagnation of validation loss in order to stop training, the default value is 10 but you can change it to whatever you like
$ python detect.py --testdir test --names yournames.labels --cfg yolov3custom.cfg --weights pathtochekpt.pth --savedir outputs --boxdir gtruths
testdir: path to the directory with all the test images ONLY
names: path to the .names file
cfg: path to model configuration file (use the yolov3custom.cfg from the first step)
weights: checkpoint path, the same checkpoint path from the previous step
savedir: path to directory to save all the predictions of the model
boxdir (optional): path to directory to save all the actual images wit ground truth boxes
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent