Links.txt

Image classification using Pytorch:
https://medium.com/@14prakash/almost-any-image-classification-problem-using-pytorch-i-am-in-love-with-pytorch-26c7aa979ec4

Autoencoders in Computer Vision:
https://www.kaggle.com/shivamb/how-autoencoders-work-intro-and-usecases/

Developing an OCR using Deep learning:
https://blogs.dropbox.com/tech/2017/04/creating-a-modern-ocr-pipeline-using-computer-vision-and-deep-learning/

checklist/steps for building a CNN:
https://khanna.cc/blog/structuring-deep-learning-projects/

Common architectures of CNN:
https://www.jeremyjordan.me/convnet-architectures/

Object detection and segmentation (Mask RCNN & U-Net)
https://medium.com/@keremturgutlu/semantic-segmentation-u-net-part-1-d8d6f6005066
https://github.com/matterport/Mask_RCNN/tree/master

Practical Guide to Effectively Train ConvNets by Andrej Kalparthy
http://karpathy.github.io/2019/04/25/recipe/

Distributed Training Imagenet in 1 hour by FAIR using Large Batch size:
https://arxiv.org/pdf/1706.02677.pdf

Open-Sourcing BiT: Exploring Large-Scale Pre-training for Computer Vision
https://ai.googleblog.com/2020/05/open-sourcing-bit-exploring-large-scale.html

Weight Standardization used together with Batch Normalization for effective training in case of micro batch size.
https://arxiv.org/pdf/1903.10520.pdf
https://github.com/joe-siyuan-qiao/WeightStandardization