pytorch-yolo2
Convert https://pjreddie.com/darknet/yolo/ into pytorch. This repository is trying to achieve the following goals.
- implement RegionLoss, MaxPoolStride1, Reorg, GolbalAvgPool2d
- implement route layer
- detect, partial, valid functions
- load darknet cfg
- load darknet saved weights
- save as darknet weights
- fast evaluation
- pascal voc validation
- train pascal voc
- LMDB data set
- Data augmentation
- load/save caffe prototxt and weights
- reproduce darknet's training results
- convert weight/cfg between pytorch caffe and darknet
- add focal loss
Detection Using A Pre-Trained Model
wget http://pjreddie.com/media/files/yolo.weights
python detect.py cfg/yolo.cfg yolo.weights data/dog.jpg
You will see some output like this:
layer filters size input output
0 conv 32 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 32
1 max 2 x 2 / 2 416 x 416 x 32 -> 208 x 208 x 32
......
30 conv 425 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 425
31 detection
Loading weights from yolo.weights... Done!
data/dog.jpg: Predicted in 0.014079 seconds.
truck: 0.934711
bicycle: 0.998013
dog: 0.990524
Real-Time Detection on a Webcam
python demo.py cfg/tiny-yolo-voc.cfg tiny-yolo-voc.weights
Training YOLO on VOC
Get The Pascal VOC Data
wget https://pjreddie.com/media/files/VOCtrainval_11-May-2012.tar
wget https://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar
wget https://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar
tar xf VOCtrainval_11-May-2012.tar
tar xf VOCtrainval_06-Nov-2007.tar
tar xf VOCtest_06-Nov-2007.tar
Generate Labels for VOC
wget http://pjreddie.com/media/files/voc_label.py
python voc_label.py
cat 2007_train.txt 2007_val.txt 2012_*.txt > voc_train.txt
Modify Cfg for Pascal Data
Change the cfg/voc.data config file
train = train.txt
valid = 2007_test.txt
names = data/voc.names
backup = backup
Download Pretrained Convolutional Weights
Download weights from the convolutional layers
wget http://pjreddie.com/media/files/darknet19_448.conv.23
or run the following command:
python partial.py cfg/darknet19_448.cfg darknet19_448.weights darknet19_448.conv.23 23
Train The Model
python train.py cfg/voc.data cfg/yolo-voc.cfg darknet19_448.conv.23
Evaluate The Model
python valid.py cfg/voc.data cfg/yolo-voc.cfg yolo-voc.weights
python scripts/voc_eval.py results/comp4_det_test_
mAP test on released models
yolo-voc.weights 544 0.7682 (paper: 78.6)
yolo-voc.weights 416 0.7513 (paper: 76.8)
tiny-yolo-voc.weights 416 0.5410 (paper: 57.1)
Focal Loss
A implementation of paper Focal Loss for Dense Object Detection
We get the results by using Focal Loss to replace CrossEntropyLoss in RegionLosss.
| gama | training set | val set | mAP@416 | mAP@544 | Notes |
|---|---|---|---|---|---|
| 0 | VOC2007+2012 | VOC2007 | 73.05 | 74.69 | std-Cross Entropy Loss |
| 1 | VOC2007+2012 | VOC2007 | 73.63 | 75.26 | Focal Loss |
| 2 | VOC2007+2012 | VOC2007 | 74.08 | 75.49 | Focal Loss |
| 3 | VOC2007+2012 | VOC2007 | 73.73 | 75.20 | Focal Loss |
| 4 | VOC2007+2012 | VOC2007 | 73.53 | 74.95 | Focal Loss |
Problems
1. Running variance difference between darknet and pytorch
Change the code in normalize_cpu to make the same result
normalize_cpu:
x[index] = (x[index] - mean[f])/(sqrt(variance[f] + .00001f));
Training on your own data
- Padding your images into square size and produce the corresponding label files.
- Modify the resize strageties in listDataset. Currently the resize scales range from 320 ~ 608, and the resize intervals is 64, which should be equal to batch_size or several times of batch_size.
- Add warm up learning rate (scales=0.1,10,.1,.1)
- Train your model as VOC does.
License
MIT License (see LICENSE file).
Contribution
Thanks for the contributions from @iceflame89 for the image augmentation and @huaijin-chen for focal loss.