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/minar09/yolov3-pytorch
https://github.com/mindlab-ai/mindcv/blob/main/examples/train_cifar_dynamic.py
https://github.com/mindlab-ai/mindcv/blob/main/mindcv/models/resnet.py
https://github.com/mindspore-ai/models/blob/master/official/cv/retinanet/
https://github.com/mindspore-ai/models/blob/master/research/cv/retinanet_resnet152
https://github.com/mindspore-ai/models/tree/master/official/cv/densenet
https://github.com/mindspore-ai/models/tree/master/official/cv/octsqueeze
OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression
https://github.com/mindspore-ai/models/tree/master/official/cv/resnet
https://github.com/mindspore-ai/models/tree/master/official/cv/resnet50_quant
https://github.com/mindspore-ai/models/tree/master/official/cv/resnet_thor
https://github.com/mindspore-ai/models/tree/master/research/cv/FaceAttribute
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/backbone/resnet.py
https://github.com/mindspore-courses/MindSpore-classification
https://github.com/mindspore-courses/heads-on-mindspore/tree/main/1-best-practice/models
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/densenet.py
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/resnet.py
https://github.com/ming71/UCAS-AOD-benchmark
https://github.com/minggli/DenseNet
https://github.com/minghanz/LaneNet
Towards End-to-End Lane Detection: an Instance Segmentation Approach
https://github.com/mingxingtan/efficientnet
https://github.com/minhcong0891/wheat-detection
https://github.com/minhdua/PHONES
https://github.com/minoot7/Lane-Detection-Transfer-Learning
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
https://github.com/minxuanjun/basalt-mirror
https://github.com/minxuanjun/basalt_class
https://github.com/miraclewkf/densenet
https://github.com/mirkozaff/DeepGTAPrediction
https://github.com/mit-acl/gym-collision-avoidance
Socially Aware Motion Planning with Deep Reinforcement Learning
https://github.com/mit-spark/kimera-multi
Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems
https://github.com/mkang315/rcs-yolo
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/mkocabas/focal-loss-keras
https://github.com/mlvccn/bmtc_transferattackvid
https://github.com/mminamina/Transfer_Learning_NaimishNet---Keypoints_Detection
https://github.com/mminamina/Transfer_Learning_ResNet18---Keypoints_Detection
https://github.com/mnguyen0226/image-augmentation-dnn-performance
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.