神经网络的一些网站
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以下是目标检测排行榜网站的列表：
- COCO (Common Objects in Context)：COCO - Common Objects in Context
- PASCAL VOC (Visual Object Classes)：The PASCAL Visual Object Classes Homepage
- ImageNet Large Scale Visual Recognition Challenge (ILSVRC)：ImageNet
- MS COCO Detection：COCO test-dev Benchmark (Object Detection) | Papers With Code
- Open Images：https://paperswithcode.com/sota/object-detection-on-open-images
- KITTI：The KITTI Vision Benchmark Suite
- Cityscapes Dataset：Cityscapes Dataset – Semantic Understanding of Urban Street Scenes
以下是神经网络排行榜网站的列表：
- Papers with Code: Browse the State-of-the-Art in Machine Learning | Papers With Code
- The Stanford DAWN Benchmarks: Stanford DAWN Deep Learning Benchmark (DAWNBench) ·
- The ImageNet Large Scale Visual Recognition Competition (ILSVRC): ImageNet
- The CIFAR-10 and CIFAR-100 Benchmark: CIFAR-10 and CIFAR-100 datasets
- The Deep Learning Benchmarks: AI-Benchmark
- The MNIST Handwritten Digit Database: MNIST handwritten digit database, Yann LeCun, Corinna Cortes and Chris Burges
- The Speech Recognition Benchmark: https://github.com/SeanNaren/deepspeech.pytorch
这些排行榜网站提供了用于评估神经网络性能的标准数据集和基准结果
目标检测算法的精度取决于多种因素，如数据集、模型架构、超参数等。以下是目前在一些常用数据集上表现最好的目标检测算法：
COCO数据集：DetectoRS、GFLv2、ATSS、NAS-FPN
PASCAL VOC数据集：RetinaNet、Faster R-CNN、Mask R-CNN
Open Images数据集：YOLOv4、EfficientDet-D7、DETR
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