codes for pedestrian detection

• Origin repo: https://github.com/polariseee/CSP-pedestrian-pytorch . Codes have been made some changes in this repo
• My best MR^-2 is 5.59%, a little better from the author's(5.84%)
• follow the origin repo to configure, and follow the UseIntro.txt to use my code.

refer to the Readme.md in file F2DNet

refer to the Readme.md in file Pedestron

The structure of CSP:

• add a head branch(head center) with [conv 1×1×256] after "conv 3×3×256"

• add a head branch(head center + head scale + offset) with [conv 3×3×256 --> 3* conv 1×1×256] after feature map

• add a head branch(head center + head scale + offset) with [3* conv 1×1×256] after "conv 3×3×256"

• training and testing different methods using different loss weights
• best result is 5.42%MR^-2 with CSP_HeadCenter_3×3 and the loss weight of head center is 0.005. (a 0.17% improvement compared with the CSP code!)

• need PyQt 5.15.9
• MainWindow
  
• Training: can change configs and print trainging logs

• Test: can choose images and modes to test,score and nms threshold can be changed,test results will be shown and printed

• Track: can choose videos or open camera to track,should firstly detect a frame and then choose one tracking method(tradition methods) to track