# Deep Neural Machine Vision

## **TOOLS**

1. [Image deduplication](https://github.com/idealo/imagededup)
2. [Segment anything by Meta](https://segment-anything.com/demo#)

## **SUPER RESOLUTION**

1. [State of the art comparison](http://www.wisdom.weizmann.ac.il/~vision/zssr/)

## **DETECTION**

![](https://lh5.googleusercontent.com/Efe-9nD1W6Hes040DI2Zgm2lzh0vnkYVTB95hnK1rmv3DYtfbPt9Bia0iVnSV49xJRs8JYLggj7KvIRGZDpbz4melmLvp0uLwQ-F6wtCjHYwRKjD4rw7DH8p90Gqo-P4DZNpW8fH)

1. [**Review on DL technique applied to semantic segmentation**](https://arxiv.org/pdf/1704.06857.pdf)
2. [**Mastery on obj detection**](https://machinelearningmastery.com/object-recognition-with-deep-learning/) **- rcnn family and yolo family**
3. **Fair** [**detectron**](https://github.com/facebookresearch/Detectron)
4. [**Maskrcnn benchmark**](https://github.com/facebookresearch/maskrcnn-benchmark)**,** [**paper**](https://arxiv.org/abs/1703.06870)
5. [**Simpledet - obj detection and instance recognition**](https://github.com/TuSimple/simpledet)
6. [**Mmdetection**](https://github.com/open-mmlab/mmdetection?fbclid=IwAR1W0G-mhiNcCJk1YdnnFFozWY_j9QUNQo9Qevfdj6_PnnODfk-5iSWbMd0)
7. [**Blind image separation**](https://www.researchgate.net/publication/3938186_Blind_image_separation_through_kurtosis_maximization)
8. [**UNET**](https://heartbeat.fritz.ai/deep-learning-for-image-segmentation-u-net-architecture-ff17f6e4c1cf)
9. [**U^2 Net - using a detection network for pencil drawing generation and segmentation**](https://github.com/NathanUA/U-2-Net)
10. [**FastAI image segmentation**](https://gilberttanner.com/blog/fastai-image-segmentation)
11. ![](https://lh6.googleusercontent.com/0gWJVORnNeoeKD6j3fwo1HrA9W8SN2ZHUBkX8YdhLUomtniJ8tlattamydryookCJrL3Pu35a3xZUfOpkc3jXYBsm0gAkMZl5IxCg5nijzRSX80vwvethJRbWGK662LnMfLw4lcZ)
12. ![](https://lh5.googleusercontent.com/kn9eEm1IltsrjvpNUJsS9iZ0zgFynCyqA2kk4OCN9EjFRXKqeUrKlvv7UbfbvwPfQ-kz0fOn3kpUqnE3liGs71m9945BLBPmpeFtOdzCyp6FUhA-7_AEjvzYnaDTXUnz-JEsbWHS)
13. [**You Only Look Once: Unified, Real-Time Object Detection**](https://arxiv.org/abs/1506.02640)**, 2015.**
14. [**YOLO9000: Better, Faster, Stronger**](https://arxiv.org/abs/1612.08242)**, 2016.**
15. [**YOLOv3: An Incremental Improvement**](https://arxiv.org/abs/1804.02767)**, 2018**
16. [**R-CNN: Regions with Convolutional Neural Network Features, GitHub**](https://github.com/rbgirshick/rcnn)**.**
17. [**Fast R-CNN, GitHub**](https://github.com/rbgirshick/fast-rcnn)**.**
18. [**Faster R-CNN Python Code, GitHub**](https://github.com/rbgirshick/py-faster-rcnn)**.**
19. [**YOLO, GitHub**](https://github.com/pjreddie/darknet/wiki/YOLO:-Real-Time-Object-Detection)**.**
20. [**Rich feature hierarchies for accurate object detection and semantic segmentation**](https://arxiv.org/abs/1311.2524)**, 2013.**
21. [**Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition**](https://arxiv.org/abs/1406.4729)**, 2014.**
22. [**Fast R-CNN**](https://arxiv.org/abs/1504.08083)**, 2015.**
23. [**Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks**](https://arxiv.org/abs/1506.01497)**, 2016.**
24. [**Mask R-CNN**](https://arxiv.org/abs/1703.06870)**, 2017.**
25. [**A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN**](https://blog.athelas.com/a-brief-history-of-cnns-in-image-segmentation-from-r-cnn-to-mask-r-cnn-34ea83205de4)**, 2017.**
26. [**Object Detection for Dummies Part 3: R-CNN Family**](https://lilianweng.github.io/lil-log/2017/12/31/object-recognition-for-dummies-part-3.html)**, 2017.**
27. [**Object Detection Part 4: Fast Detection Models**](https://lilianweng.github.io/lil-log/2018/12/27/object-detection-part-4.html)**, 2018.**
28. [**Ikea ASM**](https://ikeaasm.github.io/)
29.

## **RECOGNITION**

1. [**Using image hashtags**](https://engineering.fb.com/ml-applications/advancing-state-of-the-art-image-recognition-with-deep-learning-on-hashtags/)

## Segmentation

1. [Vit](https://dino-vit-features.github.io/)


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