ssd guillotine project

SSD[2] (Object detection Model) Implementation on Kaist pedestrian multispectral dataset[1]

Goal

• To understand SSD model
• To apply SSD model to Kaist Pedestrain dataset.

Run Now !

Architecture

DataAugmentation/Transforming Method

• phtometric disortation(randomly adjusts 4-disortation methods(Brightness,contrast,saturation,Hue) with a 50% chance)
  • Brightness , contrast,saturation : [0.5, 1.5] (random distribution : Uniform)
• Zoom out (with 50% chance of occurrence)
• Crop the image (50%)
• Flip (50%)

Data Preprocessing

Resize Normalization

train

initialization

• baseline0 initialized with pretrained SSD weights (pre-trained on VOC dataset)
  SSD300 trained on VOC
• baseline1 You can download here

parameter

optimizer : SGD(lr=0.001)
loss : MultiBoxLoss (same as original paper[2])

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Todo (default)

• Make kaist dataset be small size (for working on colab)
• split(train,val,test)
  • tr,val, test (155, 52, 69)
• Dataset&DataLoader
• SSD model
• train

NEXT

• diverse optimizers; AdamP[3], Madgrad[4]
• augmentation in object detection
• Few shot learning in small dataset
• Continual Learning (cl)
  • problem : Catastrophic Forgetting , Semantic Draft
  • Categorization of CL Approach

1. Regularization

• EWC (Elastic Weight Consolidation)[7]

2. Structure

• Progressive Networks[8]

3. Memory

• DGR (Deep Generative Replay)[9]

4. Mixed above

• Dynamically Expandable Network[10]

😁 when i have a time, I wanna think of this topic deeply.

• the ways of solving Class Imbalance problems (In this paper , used Hard Negative method for solving this problem)
  • Online Hard Example Mining [6]
  • Focal loss[5]

ETC

kaist dataset[1]

origin : https://soonminhwang.github.io/rgbt-ped-detection/
download : https://gofile.me/4ce0I/uRhsZ8nnF

kaist dataset annotation

github : https://github.com/luzhang16/AR-CNN
download : https://drive.google.com/open?id=1FLkoJQOGt4PqRtr0j6namAaehrdt_A45

ssd baseline code

baseline0 : https://github.com/amdegroot/ssd.pytorch/tree/5b0b77faa955c1917b0c710d770739ba8fbff9b7 (it has many bugs)
baseline1 : https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection

tools (related to Object detection )

object detection framework : https://github.com/open-mmlab/mmdetection

#reference

• [1] S. Hwang, J. Park, N. Kim, Y. Choi, and I. S. Kweon, “Multispectral Pedestrian Detection: Benchmark Dataset and Baseline.” Accessed: May 07, 2021. [Online]. Available: http://rcv.kaist.ac.kr/multispectral-pedestrian/.
• [2] W. Liu et al., “SSD: Single Shot MultiBox Detector.” Accessed: May 07, 2021. [Online]. Available: https://github.com/weiliu89/caffe/tree/ssd.
• [3] B. Heo et al., “ADAMP: SLOWING DOWN THE SLOWDOWN FOR MO-MENTUM OPTIMIZERS ON SCALE-INVARIANT WEIGHTS Equal contribution * , Works done at Naver AI Lab †.” Accessed: May 07, 2021. [Online]. Available: https://github.com/clovaai/adamp.
• [4] A. Defazio and S. Jelassi, “Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization,” pp. 1–31, 2021, [Online]. Available: http://arxiv.org/abs/2101.11075.
• [5] T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal Loss for Dense Object Detection.” Accessed: May 11, 2021. [Online]. Available: https://github.com/facebookresearch/Detectron.
• [6] A. Shrivastava, A. Gupta, and R. Girshick, “Training Region-based Object Detectors with Online Hard Example Mining.”
• [7] J. Kirkpatrick et al., “Overcoming catastrophic forgetting in neural networks,” Proc. Natl. Acad. Sci. U. S. A., vol. 114, no. 13, pp. 3521–3526, Mar. 2017, doi: 10.1073/pnas.1611835114.
• [8] A. A. Rusu et al., “Progressive Neural Networks,” Jun. 2016, Accessed: May 15, 2021. [Online]. Available: http://arxiv.org/abs/1606.04671.
• [9] H. Shin, J. K. Lee, J. Kim, and J. Kim, “Continual Learning with Deep Generative Replay,” Adv. Neural Inf. Process. Syst., vol. 2017-December, pp. 2991–3000, May 2017, Accessed: May 15, 2021. [Online]. Available: http://arxiv.org/abs/1705.08690.
• [10] J. Yoon, E. Yang, J. Lee, and S. J. Hwang, “Lifelong Learning with Dynamically Expandable Networks,” arXiv, Aug. 2017, Accessed: May 15, 2021. [Online]. Available: http://arxiv.org/abs/1708.01547.