Autonomous Vehicles
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Autonomous vehicles is the task of making a vehicle that can guide itself without human conduction.
Many of the state-of-the-art results can be found at more general task pages such as 3D Object Detection and Semantic Segmentation.
<span style="color:grey; opacity: 0.6">( Image credit: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xuanyuzhou98/SqueezeSeg
https://github.com/xuanyuzhou98/SqueezeSegV2
https://github.com/xufanxiong/classification-of-caltech-256
https://github.com/xvyaward/qeft
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/xxnote/stunning-doodle
https://github.com/xy-guo/Learning-Monocular-Depth-by-Stereo
Learning Monocular Depth by Distilling Cross-domain Stereo Networks
https://github.com/xypan1232/iDeepE
https://github.com/xytpai/DetX-FCOS
https://github.com/xytpai/fcos
https://github.com/xytpai/retinanet
https://github.com/xyuan-wyze/darknet
https://github.com/xzxzxzxz/interp
Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning
https://github.com/yahoo/open_nsfw
https://github.com/yakhyo/EfficientNet-PyTorch
https://github.com/yakhyo/gaze-estimation
https://github.com/yakhyo/head-pose-estimation
https://github.com/yakhyo/retinaface-pytorch
https://github.com/yandex-research/shifts
Shifts 2.0: Extending The Dataset of Real Distributional Shifts
https://github.com/yang-0201/YOLOv6_pro
YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
https://github.com/yang-233/waste_detect
https://github.com/yangboz/TransferLearning4Dentist
https://github.com/yangle15/RANet-pytorch
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
https://github.com/yangorwell/NGPlus
https://github.com/yangysc/resinet
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
https://github.com/yangyucheng000/Paper-2/tree/main/JCRNet-mindspore-main
https://github.com/yangyucheng000/Paper-3/tree/main/FPNet-MindSpore-master
https://github.com/yangyucheng000/densenet/blob/main/densenet.py
https://github.com/yangyucheng000/papercode-2/tree/main/JCRNet-mindspore-main
https://github.com/yangyucheng000/papercode-2/tree/main/WS-VAD-mindspore-main
VAD: Vectorized Scene Representation for Efficient Autonomous Driving
https://github.com/yangyucheng000/resnet_Ascend
https://github.com/yangzhaojason/DualDiff
DualDiff: Dual-branch Diffusion Model for Autonomous Driving with Semantic Fusion
https://github.com/yanrucheng/PINet
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
https://github.com/yanrucheng/PINet-demo
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
https://github.com/yanxp/rcnn-discovery
https://github.com/yaoming0712/dalinmask
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.