Discover about Computer system Eyesight and Notion for Self Driving Vehicles. This collection focuses on the diverse tasks that a Self Driving Car or truck Notion unit would be necessary to do.
✏️ Training course by Robotics with Sakshay. https://www.youtube.com/channel/UC57lEMTXZzXYu_y0FKdW6xA
⭐️ Course Contents and Inbound links ⭐️
⌨️ (:00:00) Introduction
⌨️ (:02:16) Completely Convolutional Network | Street Segmentation
🔗 Kaggle Dataset: https://www.kaggle.com/sakshaymahna/kittiroadsegmentation
🔗 Kaggle Notebook: https://www.kaggle.com/sakshaymahna/totally-convolutional-community
🔗 KITTI Dataset: http://www.cvlibs.internet/datasets/kitti/
🔗 Thoroughly Convolutional Network Paper: https://arxiv.org/ab muscles/1411.4038
🔗 Hand Crafted Road Segmentation: https://www.youtube.com/watch?v=hrin-qTn4L4
🔗 Deep Discovering and CNNs: https://www.youtube.com/check out?v=aircAruvnKk
⌨️ (:20:45) YOLO | 2D Item Detection
🔗 Kaggle Competitors/Dataset: https://www.kaggle.com/c/3d-item-detection-for-autonomous-autos
🔗 Visualization Notebook: https://www.kaggle.com/sakshaymahna/lyft-3d-object-detection-eda
🔗 YOLO Notebook: https://www.kaggle.com/sakshaymahna/yolov3-keras-2d-object-detection
🔗 Playlist on Fundamentals of Item Detection: https://www.youtube.com/playlist?checklist=PL_IHmaMAvkVxdDOBRg2CbcJBq9SY7ZUvs
🔗 Weblog on YOLO: https://www.section.io/engineering-education and learning/introduction-to-yolo-algorithm-for-item-detection/
🔗 YOLO Paper: https://arxiv.org/stomach muscles/1506.02640
⌨️ (:35:51) Deep Sort | Item Tracking
🔗 Dataset: https://www.kaggle.com/sakshaymahna/kittiroadsegmentation
🔗 Notebook/Code: https://www.kaggle.com/sakshaymahna/deepsort/notebook
🔗 Blog site on Deep Type: https://medium.com/analytics-vidhya/object-monitoring-employing-deepsort-in-tensorflow-2-ec013a2eeb4f
🔗 Deep Type Paper: https://arxiv.org/abs/1703.07402
🔗 Kalman Filter: https://www.youtube.com/playlist?list=PLn8PRpmsu08pzi6EMiYnR-076Mh-q3tWr
🔗 Hungarian Algorithm: https://www.geeksforgeeks.org/hungarian-algorithm-assignment-issue-established-1-introduction/
🔗 Cosine Length Metric: https://www.machinelearningplus.com/nlp/cosine-similarity/
🔗 Mahalanobis Length: https://www.machinelearningplus.com/statistics/mahalanobis-length/
🔗 YOLO Algorithm: https://youtu.be/C3qmhPVUXiE
⌨️ (:52:37) KITTI 3D Data Visualization | Homogenous Transformations
🔗 Dataset: https://www.kaggle.com/garymk/kitti-3d-item-detection-dataset
🔗 Notebook/Code: https://www.kaggle.com/sakshaymahna/lidar-details-visualization/notebook
🔗 LIDAR: https://geoslam.com/what-is-lidar/
🔗 Tesla doesn’t use LIDAR: https://towardsdatascience.com/why-tesla-wont-use-lidar-57c325ae2ed5
⌨️ (1:06:45) Multi Process Focus Community (MTAN) | Multi Undertaking Discovering
🔗 Dataset: https://www.kaggle.com/sakshaymahna/cityscapes-depth-and-segmentation
🔗 Notebook/Code: https://www.kaggle.com/sakshaymahna/mtan-multi-activity-focus-community
🔗 Details Visualization: https://www.kaggle.com/sakshaymahna/exploratory-information-investigation
🔗 MTAN Paper: https://arxiv.org/ab muscles/1803.10704
🔗 Weblog on Multi Process Discovering: https://ruder.io/multi-endeavor/
🔗 Graphic Segmentation and FCN: https://youtu.be/U_v0Tovp4XQ
⌨️ (1:20:58) SFA 3D | 3D Item Detection
🔗 Dataset: https://www.kaggle.com/garymk/kitti-3d-object-detection-dataset
🔗 Notebook/Code: https://www.kaggle.com/sakshaymahna/sfa3d
🔗 Data Visualization: https://www.kaggle.com/sakshaymahna/l…
🔗 Details Visualization Movie: https://youtu.be/tb1H42kE0eE
🔗 SFA3D GitHub Repository: https://github.com/maudzung/SFA3D
🔗 Attribute Pyramid Networks: https://jonathan-hui.medium.com/comprehension-function-pyramid-networks-for-item-detection-fpn-45b227b9106c
🔗 Keypoint Attribute Pyramid Community: https://arxiv.org/pdf/2001.03343.pdf
🔗 Heat Maps: https://en.wikipedia.org/wiki/Heat_map
🔗 Focal Loss: https://medium.com/visionwizard/being familiar with-focal-reduction-a-fast-examine-b914422913e7
🔗 L1 Loss: https://afteracademy.com/weblog/what-are-l1-and-l2-decline-capabilities
🔗 Balanced L1 Loss: https://paperswithcode.com/process/well balanced-l1-loss
🔗 Finding out Rate Decay: https://medium.com/analytics-vidhya/discovering-price-decay-and-techniques-in-deep-discovering-2cee564f910b
🔗 Cosine Annealing: https://paperswithcode.com/method/cosine-annealing
⌨️ (1:40:24) UNetXST | Digicam to Bird’s Eye See
🔗 Dataset: https://www.kaggle.com/sakshaymahna/semantic-segmentation-bev
🔗 Dataset Visualization: https://www.kaggle.com/sakshaymahna/info-visualization
🔗 Notebook/Code: https://www.kaggle.com/sakshaymahna/unetxst
🔗 UNetXST Paper: https://arxiv.org/pdf/2005.04078.pdf
🔗 UNetXST Github Repository: https://github.com/ika-rwth-aachen/Cam2BEV
🔗 UNet: https://towardsdatascience.com/comprehension-semantic-segmentation-with-unet-6be4f42d4b47
🔗 Image Transformations: https://kevinzakka.github.io/2017/01/10/stn-section1/
🔗 Spatial Transformer Networks: https://kevinzakka.github.io/2017/01/18/stn-component2/
