KowalskiWang's repositories
awesome-chatgpt-prompts-zh
ChatGPT 中文调教指南。怎么让它听你的话。
carla
Open-source simulator for autonomous driving research.
darknet
Windows and Linux version of Darknet Yolo v3 & v2 Neural Networks for object detection (Tensor Cores are used)
deep_sort
Simple Online Realtime Tracking with a Deep Association Metric
deep_sort_yolov3
Real-time Multi-person tracker using YOLO v3 and deep_sort with tensorflow
keras-yolo3
A Keras implementation of YOLOv3 (Tensorflow backend)
LeetCodeAnimation
Demonstrate all the questions on LeetCode in the form of animation.（用动画的形式呈现解LeetCode题目的思路）
maskfusion
MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects
self_drive
基于树莓派的自动驾驶小车，利用树莓派和tensorflow实现小车在赛道的自动驾驶。（Self-driving car based on raspberry pi（tensorflow））
TensorMask-Review
Sliding-window object detectors that generate boundingbox object predictions over a dense, regular grid have advanced rapidly and proven popular. In contrast, modern instance segmentation approaches are dominated by methods that first detect object bounding boxes, and then crop and segment these regions, as popularized by Mask R-CNN. In this work, we investigate the paradigm of dense slidingwindow instance segmentation, which is surprisingly underexplored. Our core observation is that this task is fundamentally different than other dense prediction tasks such as semantic segmentation or bounding-box object detection, as the output at every spatial location is itself a geometric structure with its own spatial dimensions. To formalize this, we treat dense instance segmentation as a prediction task over 4D tensors and present a general framework called TensorMask that explicitly captures this geometry and enables novel operators on 4D tensors. We demonstrate that the tensor view leads to large gains over baselines that ignore this structure, and leads to results comparable to Mask R-CNN. These promising results suggest that TensorMask can serve as a foundation for novel advances in dense mask prediction and a more complete understanding of the task. Code will be made available.