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

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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

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Real-Time Detection on a Webcam

python demo.py cfg/tiny-yolo-voc.cfg tiny-yolo-voc.weights

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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)

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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

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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));