R-CNN
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The document discusses R-CNN, a framework for object detection in images using convolutional neural networks. It introduces R-CNN and its components, including region proposal using selective search, feature extraction from proposed regions using a CNN, and classifying regions using an SVM. Later developments like Fast R-CNN and Faster R-CNN improved upon R-CNN by making object detection faster and joint training end-to-end.
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Object Detection Using R-CNN Deep Learning Framework
Teaching slides covering R-CNN deep learning framework for object detection. I tried to cover R-CNN, Fast R-CNN, Faster R-CNN and Mask R-CNN.
Faster R-CNN: Towards real-time object detection with region proposal network...
Faster R-CNN: Towards real-time object detection with region proposal network...Universitat Politècnica de Catalunya
Slides by Amaia Salvador at the UPC Computer Vision Reading Group.
Source document on GDocs with clickable links:
https://docs.google.com/presentation/d/1jDTyKTNfZBfMl8OHANZJaYxsXTqGCHMVeMeBe5o1EL0/edit?usp=sharing
Based on the original work:
Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. "Faster R-CNN: Towards real-time object detection with region proposal networks." In Advances in Neural Information Processing Systems, pp. 91-99. 2015.Faster R-CNN - PR012
Faster R-CNN improves object detection by introducing a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. The RPN slides over feature maps and predicts object bounds and objectness at each position. During training, anchors are assigned positive or negative labels based on Intersection over Union with ground truth boxes. Faster R-CNN runs the RPN in parallel with Fast R-CNN for detection, end-to-end in a single network and stage. This achieves state-of-the-art object detection speed and accuracy while eliminating computationally expensive selective search for proposals.
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I mede this slide for the beginners of object detection.
Anchor box was really hard to understand for me, so I wrote about it as easy to understand as I can.
Let's overwhelmingly prosper!!
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Faster R-CNN: Towards real-time object detection with region proposal network...
Faster R-CNN: Towards real-time object detection with region proposal network...Universitat Politècnica de Catalunya
Slides by Amaia Salvador at the UPC Computer Vision Reading Group.
Source document on GDocs with clickable links:
https://docs.google.com/presentation/d/1jDTyKTNfZBfMl8OHANZJaYxsXTqGCHMVeMeBe5o1EL0/edit?usp=sharing
Based on the original work:
Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. "Faster R-CNN: Towards real-time object detection with region proposal networks." In Advances in Neural Information Processing Systems, pp. 91-99. 2015.Faster R-CNN - PR012
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I mede this slide for the beginners of object detection.
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R-CNN
- 1. L B S C E K A S A R A G O D R-CNN Mohamed Rashid
- 2. R - C N N @ r a s h i v k p INTRODUCTION
- 3. R - C N N @ r a s h i v k p COMPUTER VISION TASK
- 4. R - C N N @ r a s h i v k p DATASETS ILSVRC2013
- 5. R - C N N @ r a s h i v k p OVERVIEW
- 6. R - C N N @ r a s h i v k p
- 7. R - C N N @ r a s h i v k p REGION PROPOSAL
- 8. R - C N N @ r a s h i v k p REGION PROPOSAL SELECTIVE SEARCH
- 9. R - C N N @ r a s h i v k p REGION PROPOSAL SELECTIVE SEARCH
- 10. R - C N N @ r a s h i v k p FEATURE EXTRACTION
- 11. R - C N N @ r a s h i v k p SOME BASICS CNN
- 12. SOME BASICS TRANSFER LEARNING R - C N N @ r a s h i v k p
- 13. R - C N N @ r a s h i v k p FEATURE EXTRACTION ARCHITECTURE
- 14. R - C N N @ r a s h i v k p REGION CLASSIFICATION
- 15. R - C N N @ r a s h i v k p REGION CLASSIFICATION LINEAR SVM
- 16. R - C N N @ r a s h i v k p COMPARISON
- 17. R - C N N @ r a s h i v k p R-CNN https://arxiv.org/abs/1311.2524 Fast R-CNN https://arxiv.org/abs/1504.08083 Faster R-CNN https://arxiv.org/abs/1506.01497 A Beginner's Guide To Understanding Convolutional Neural Networks https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural- Networks/ https://cs.stackexchange.com/questions/51387/what-is-the-difference-between-object-detection-semantic-segmentation- and-local http://fastml.com/what-you-wanted-to-know-about-mean-average-precision/ RCNN Implementation https://github.com/rbgirshick/rcnn Selective Search https://www.koen.me/research/selectivesearch/ Bounding Box Regression https://www.quora.com/Convolutional-Neural-Networks-What-are-bounding-box-regressors-doing-in-Fast-RCNN https://cs231n.github.io/transfer-learning/ Feature Extraction from Convolutional Neural Network (CNN) and use this feature to other classification algorithm https://stackoverflow.com/questions/38940192/feature-extraction-from-convolutional-neural-network-cnn-and-use-this- feature#38954700 SVM https://en.wikipedia.org/wiki/Support_vector_machine REFERENCE
- 18. R - C N N @ r a s h i v k p THANK YOU