https://github.com/2023-MindSpore-4/Code10/tree/main/Efficientnet
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
Exploring Visual Context for Weakly Supervised Person Search
Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes
Flow: A Modular Learning Framework for Mixed Autonomy Traffic
Novel Deep Learning Model for Traffic Sign Detection Using Capsule Networks
Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions
Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systems
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.