Weekly Reports

I am focusing on reading different papers and try to understand theories for these algorithms.

Rich feature hierarchies for accurate object detection and semantic segmentation | [CVPR' 14]

https://arxiv.org/pdf/1311.2524.pdf

https://github.com/rbgirshick/rcnn

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks | [ICLR' 14]

https://arxiv.org/pdf/1312.6229.pdf

https://github.com/sermanet/OverFeat

Fast R-CNN | [ICCV' 15]

https://arxiv.org/pdf/1504.08083.pdf

https://github.com/rbgirshick/fast-rcnn

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks | [NIPS' 15]

https://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf

https://github.com/rbgirshick/py-faster-rcnn

Mask R-CNN | [ICCV' 17]

http://openaccess.thecvf.com/content_ICCV_2017/papers/He_Mask_R-CNN_ICCV_2017_paper.pdf

https://github.com/facebookresearch/Detectron

You Only Look Once: Unified, Real-Time Object Detection | [CVPR' 16]

https://arxiv.org/pdf/1506.02640.pdf

https://pjreddie.com/darknet/yolo/

YOLO9000: Better, Faster, Stronger | [CVPR' 17]

https://arxiv.org/pdf/1612.08242.pdf

https://pjreddie.com/darknet/yolo/

YOLOv3: An Incremental Improvement | [arXiv' 18]

https://pjreddie.com/media/files/papers/YOLOv3.pdf

https://pjreddie.com/darknet/yolo/

SSD: Single Shot MultiBox Detector | [ECCV' 16]

https://arxiv.org/pdf/1512.02325.pdf

https://github.com/weiliu89/caffe/tree/ssd

Find open source for object detection using tensorflow and openCV

https://github.com/tensorflow/models/tree/master/research/object_detection

Successfully fix and run it in my colab to get picture as they show in the README file.

Fully understand theories and try to start mid-stage report

Continue with mid-stage report and try to finish by next week.

Mid-stage report

Final report