diff --git a/README.md b/README.md index 3c7e04031069a02592862de66adb8662607dce65..01f8388f5eeec043a75216d1d2858eb8100cbbab 100644 --- a/README.md +++ b/README.md @@ -9,455 +9,455 @@ DeepSparkHub甄选上百个应用算法和模型，覆盖AI和通用计算各领 #### Classification -| Model | Framework | Dataset | -|-----------------------------------------------------------------------------------------|--------------|----------| -| [ACmix](cv/classification/acmix/pytorch/README.md) | PyTorch | ImageNet | -| [ACNet](cv/classification/acnet/pytorch/README.md) | PyTorch | ImageNet | -| [AlexNet](cv/classification/alexnet/pytorch/README.md) | PyTorch | ImageNet | -| [AlexNet](cv/classification/alexnet/tensorflow/README.md) | TensorFlow | ImageNet | -| [BYOL](cv/classification/byol/pytorch/README.md) | PyTorch | ImageNet | -| [CBAM](cv/classification/cbam/pytorch/README.md) | PyTorch | ImageNet | -| [ConvNext](cv/classification/convnext/pytorch/README.md) | PyTorch | ImageNet | -| [CspDarknet53](cv/classification/cspdarknet53/pytorch/README.md) | PyTorch | ImageNet | -| [DenseNet](cv/classification/densenet/paddlepaddle/README.md) | PaddlePaddle | ImageNet | -| [DenseNet](cv/classification/densenet/pytorch/README.md) | PyTorch | ImageNet | -| [DPN92](cv/classification/dpn92/pytorch/README.md) | PyTorch | ImageNet | -| [DPN107](cv/classification/dpn107/pytorch/README.md) | PyTorch | ImageNet | -| [ECA-MobileNetV2](cv/classification/eca_mobilenet_v2/pytorch/README.md) | PyTorch | ImageNet | -| [ECA-ResNet152](cv/classification/eca_resnet152/pytorch/README.md) | PyTorch | ImageNet | -| [EfficientNetB0](cv/classification/efficientnet_b0/paddlepaddle/README.md) | PaddlePaddle | ImageNet | -| [EfficientNetB4](cv/classification/efficientnet_b4/pytorch/README.md) | PyTorch | ImageNet | -| [FasterNet](cv/classification/fasternet/pytorch/README.md) | PyTorch | ImageNet | -| [GoogLeNet](cv/classification/googlenet/pytorch/README.md) | PyTorch | ImageNet | -| [GoogLeNet](cv/classification/googlenet/paddlepaddle/README.md) | PaddlePaddle | ImageNet | -| [InceptionV3](cv/classification/inceptionv3/mindspore/README.md) | MindSpore | ImageNet | -| [InceptionV3](cv/classification/inceptionv3/pytorch/README.md) | PyTorch | ImageNet | -| [InceptionV3](cv/classification/inceptionv3/tensorflow/README.md) | TensorFlow | ImageNet | -| [InceptionV4](cv/classification/inceptionv4/pytorch/README.md) | PyTorch | ImageNet | -| [InternImage](cv/classification/internimage/pytorch/README.md) | PyTorch | ImageNet | -| [LeNet](cv/classification/lenet/pytorch/README.md) | PyTorch | ImageNet | -| [MobileNetV2](cv/classification/mobilenetv2/pytorch/README.md) | PyTorch | ImageNet | -| [MobileNetV3](cv/classification/mobilenetv3/mindspore/README.md) | MindSpore | ImageNet | -| [MobileNetV3](cv/classification/mobilenetv3/pytorch/README.md) | PyTorch | ImageNet | -| [MobileNetV3](cv/classification/mobilenetv3/paddlepaddle/README.md) | PaddlePaddle | ImageNet | -| [MobileNetV3_Large1.0](cv/classification/mobilenetv3_large_x1_0/paddlepaddle/README.md) | PaddlePaddle | ImageNet | -| [MobileOne](cv/classification/mobileone/pytorch/README.md) | PyTorch | ImageNet | -| [MoCoV2](cv/classification/mocov2/pytorch/README.md) | PyTorch | ImageNet | -| [PP-LCNet](cv/classification/pp-lcnet/paddlepaddle/README.md) | PaddlePaddle | ImageNet | -| [RepMLP](cv/classification/repmlp/pytorch/README.md) | PyTorch | ImageNet | -| [RepVGG](cv/classification/repvgg/pytorch/README.md) | PyTorch | ImageNet | -| [RepVGG](cv/classification/repvgg/paddlepaddle/README.md) | PaddlePaddle | ImageNet | -| [RepViT](cv/classification/repvit/pytorch/README.md) | PyTorch | ImageNet | -| [Res2Net50_14w_8s](cv/classification/Res2Net50_14w_8s/paddlepaddle/README.md) | PaddlePaddle | ImageNet | -| [ResNeSt14](cv/classification/resnest14/pytorch/README.md) | PyTorch | ImageNet | -| [ResNeSt50](cv/classification/resnest50/pytorch/README.md) | PyTorch | ImageNet | -| [ResNeSt50](cv/classification/resnest50/paddlepaddle/README.md) | PaddlePaddle | ImageNet | -| [ResNeSt101](cv/classification/resnest101/pytorch/README.md) | PyTorch | ImageNet | -| [ResNeSt269](cv/classification/resnest269/pytorch/README.md) | PyTorch | ImageNet | -| [ResNet18](cv/classification/resnet18/pytorch/README.md) | PyTorch | ImageNet | -| [ResNet50](cv/classification/resnet50/pytorch/README.md) | PyTorch | ImageNet | -| [ResNet50](cv/classification/resnet50/paddlepaddle/README.md) | PaddlePaddle | ImageNet | -| [ResNet50](cv/classification/resnet50/tensorflow/README.md) | TensorFlow | ImageNet | -| [ResNet101](cv/classification/resnet101/pytorch/README.md) | PyTorch | ImageNet | -| [ResNet152](cv/classification/resnet152/pytorch/README.md) | PyTorch | ImageNet | -| [ResNeXt50_32x4d](cv/classification/resnext50_32x4d/mindspore/README.md) | MindSpore | ImageNet | -| [ResNeXt50_32x4d](cv/classification/resnext50_32x4d/pytorch/README.md) | PyTorch | ImageNet | -| [ResNeXt101_32x8d](cv/classification/resnext101_32x8d/pytorch/README.md) | PyTorch | ImageNet | -| [SE_ResNet50_vd](cv/classification/se_resnet50_vd/paddlepaddle/README.md) | PaddlePaddle | ImageNet | -| [SEResNeXt](cv/classification/seresnext/pytorch/README.md) | PyTorch | ImageNet | -| [ShuffleNetV2](cv/classification/shufflenetv2/paddlepaddle/README.md) | PaddlePaddle | ImageNet | -| [ShuffleNetV2](cv/classification/shufflenetv2/pytorch/README.md) | PyTorch | ImageNet | -| [SqueezeNet](cv/classification/squeezenet/pytorch/README.md) | PyTorch | ImageNet | -| [Swin Transformer](cv/classification/swin_transformer/paddlepaddle/README.md) | PaddlePaddle | ImageNet | -| [Swin Transformer](cv/classification/swin_transformer/pytorch/README.md) | PyTorch | ImageNet | -| [VGG16](cv/classification/vgg/paddlepaddle/README.md) | PaddlePaddle | ImageNet | -| [VGG16](cv/classification/vgg/pytorch/README.md) | PyTorch | ImageNet | -| [VGG16](cv/classification/vgg/tensorflow/README.md) | TensorFlow | ImageNet | -| [Wave-MLP](cv/classification/wavemlp/pytorch/README.md) | PyTorch | ImageNet | -| [Wide_ResNet101_2](cv/classification/wide_resnet101_2/pytorch/README.md) | PyTorch | ImageNet | -| [Xception](cv/classification/xception/paddlepaddle/README.md) | PaddlePaddle | ImageNet | -| [Xception](cv/classification/xception/pytorch/README.md) | PyTorch | ImageNet | +| Model | Framework | Dataset | +|-------------------------------------------------------------------------------|--------------|----------| +| [ACmix](cv/classification/acmix/pytorch) | PyTorch | ImageNet | +| [ACNet](cv/classification/acnet/pytorch) | PyTorch | ImageNet | +| [AlexNet](cv/classification/alexnet/pytorch) | PyTorch | ImageNet | +| [AlexNet](cv/classification/alexnet/tensorflow) | TensorFlow | ImageNet | +| [BYOL](cv/classification/byol/pytorch) | PyTorch | ImageNet | +| [CBAM](cv/classification/cbam/pytorch) | PyTorch | ImageNet | +| [ConvNext](cv/classification/convnext/pytorch) | PyTorch | ImageNet | +| [CspDarknet53](cv/classification/cspdarknet53/pytorch) | PyTorch | ImageNet | +| [DenseNet](cv/classification/densenet/paddlepaddle) | PaddlePaddle | ImageNet | +| [DenseNet](cv/classification/densenet/pytorch) | PyTorch | ImageNet | +| [DPN92](cv/classification/dpn92/pytorch) | PyTorch | ImageNet | +| [DPN107](cv/classification/dpn107/pytorch) | PyTorch | ImageNet | +| [ECA-MobileNetV2](cv/classification/eca_mobilenet_v2/pytorch) | PyTorch | ImageNet | +| [ECA-ResNet152](cv/classification/eca_resnet152/pytorch) | PyTorch | ImageNet | +| [EfficientNetB0](cv/classification/efficientnet_b0/paddlepaddle) | PaddlePaddle | ImageNet | +| [EfficientNetB4](cv/classification/efficientnet_b4/pytorch) | PyTorch | ImageNet | +| [FasterNet](cv/classification/fasternet/pytorch) | PyTorch | ImageNet | +| [GoogLeNet](cv/classification/googlenet/pytorch) | PyTorch | ImageNet | +| [GoogLeNet](cv/classification/googlenet/paddlepaddle) | PaddlePaddle | ImageNet | +| [InceptionV3](cv/classification/inceptionv3/mindspore) | MindSpore | ImageNet | +| [InceptionV3](cv/classification/inceptionv3/pytorch) | PyTorch | ImageNet | +| [InceptionV3](cv/classification/inceptionv3/tensorflow) | TensorFlow | ImageNet | +| [InceptionV4](cv/classification/inceptionv4/pytorch) | PyTorch | ImageNet | +| [InternImage](cv/classification/internimage/pytorch) | PyTorch | ImageNet | +| [LeNet](cv/classification/lenet/pytorch) | PyTorch | ImageNet | +| [MobileNetV2](cv/classification/mobilenetv2/pytorch) | PyTorch | ImageNet | +| [MobileNetV3](cv/classification/mobilenetv3/mindspore) | MindSpore | ImageNet | +| [MobileNetV3](cv/classification/mobilenetv3/pytorch) | PyTorch | ImageNet | +| [MobileNetV3](cv/classification/mobilenetv3/paddlepaddle) | PaddlePaddle | ImageNet | +| [MobileNetV3_Large1.0](cv/classification/mobilenetv3_large_x1_0/paddlepaddle) | PaddlePaddle | ImageNet | +| [MobileOne](cv/classification/mobileone/pytorch) | PyTorch | ImageNet | +| [MoCoV2](cv/classification/mocov2/pytorch) | PyTorch | ImageNet | +| [PP-LCNet](cv/classification/pp-lcnet/paddlepaddle) | PaddlePaddle | ImageNet | +| [RepMLP](cv/classification/repmlp/pytorch) | PyTorch | ImageNet | +| [RepVGG](cv/classification/repvgg/pytorch) | PyTorch | ImageNet | +| [RepVGG](cv/classification/repvgg/paddlepaddle) | PaddlePaddle | ImageNet | +| [RepViT](cv/classification/repvit/pytorch) | PyTorch | ImageNet | +| [Res2Net50_14w_8s](cv/classification/Res2Net50_14w_8s/paddlepaddle) | PaddlePaddle | ImageNet | +| [ResNeSt14](cv/classification/resnest14/pytorch) | PyTorch | ImageNet | +| [ResNeSt50](cv/classification/resnest50/pytorch) | PyTorch | ImageNet | +| [ResNeSt50](cv/classification/resnest50/paddlepaddle) | PaddlePaddle | ImageNet | +| [ResNeSt101](cv/classification/resnest101/pytorch) | PyTorch | ImageNet | +| [ResNeSt269](cv/classification/resnest269/pytorch) | PyTorch | ImageNet | +| [ResNet18](cv/classification/resnet18/pytorch) | PyTorch | ImageNet | +| [ResNet50](cv/classification/resnet50/pytorch) | PyTorch | ImageNet | +| [ResNet50](cv/classification/resnet50/paddlepaddle) | PaddlePaddle | ImageNet | +| [ResNet50](cv/classification/resnet50/tensorflow) | TensorFlow | ImageNet | +| [ResNet101](cv/classification/resnet101/pytorch) | PyTorch | ImageNet | +| [ResNet152](cv/classification/resnet152/pytorch) | PyTorch | ImageNet | +| [ResNeXt50_32x4d](cv/classification/resnext50_32x4d/mindspore) | MindSpore | ImageNet | +| [ResNeXt50_32x4d](cv/classification/resnext50_32x4d/pytorch) | PyTorch | ImageNet | +| [ResNeXt101_32x8d](cv/classification/resnext101_32x8d/pytorch) | PyTorch | ImageNet | +| [SE_ResNet50_vd](cv/classification/se_resnet50_vd/paddlepaddle) | PaddlePaddle | ImageNet | +| [SEResNeXt](cv/classification/seresnext/pytorch) | PyTorch | ImageNet | +| [ShuffleNetV2](cv/classification/shufflenetv2/paddlepaddle) | PaddlePaddle | ImageNet | +| [ShuffleNetV2](cv/classification/shufflenetv2/pytorch) | PyTorch | ImageNet | +| [SqueezeNet](cv/classification/squeezenet/pytorch) | PyTorch | ImageNet | +| [Swin Transformer](cv/classification/swin_transformer/paddlepaddle) | PaddlePaddle | ImageNet | +| [Swin Transformer](cv/classification/swin_transformer/pytorch) | PyTorch | ImageNet | +| [VGG16](cv/classification/vgg/paddlepaddle) | PaddlePaddle | ImageNet | +| [VGG16](cv/classification/vgg/pytorch) | PyTorch | ImageNet | +| [VGG16](cv/classification/vgg/tensorflow) | TensorFlow | ImageNet | +| [Wave-MLP](cv/classification/wavemlp/pytorch) | PyTorch | ImageNet | +| [Wide_ResNet101_2](cv/classification/wide_resnet101_2/pytorch) | PyTorch | ImageNet | +| [Xception](cv/classification/xception/paddlepaddle) | PaddlePaddle | ImageNet | +| [Xception](cv/classification/xception/pytorch) | PyTorch | ImageNet | #### Face Detection -| Model | Framework | Dataset | -|----------------------------------------------------|-----------|-----------| -| [RetinaFace](cv/face/retinaface/pytorch/README.md) | PyTorch | WiderFace | +| Model | Framework | Dataset | +|------------------------------------------|-----------|------------| +| [RetinaFace](cv/face/retinaface/pytorch) | PyTorch | WIDER FACE | #### Face Recognition -| Model | Framework | Dataset | -|-------------------------------------------------------|--------------|--------------------| -| [ArcFace](cv/face/arcface/pytorch/README.md) | PyTorch | CASIA-WebFaces&LFW | -| [BlazeFace](cv/face/blazeface/paddlepaddle/README.md) | PaddlePaddle | WIDER-FACE | -| [CosFace](cv/face/cosface/pytorch/README.md) | PyTorch | CASIA-WebFaces&LFW | -| [FaceNet](cv/face/facenet/pytorch/README.md) | PyTorch | CASIA-WebFaces&LFW | -| [FaceNet](cv/face/facenet/tensorflow/README.md) | TensorFlow | CASIA-WebFaces&LFW | +| Model | Framework | Dataset | +|---------------------------------------------|--------------|--------------------| +| [ArcFace](cv/face/arcface/pytorch) | PyTorch | CASIA-WebFaces&LFW | +| [BlazeFace](cv/face/blazeface/paddlepaddle) | PaddlePaddle | WIDER FACE | +| [CosFace](cv/face/cosface/pytorch) | PyTorch | CASIA-WebFaces&LFW | +| [FaceNet](cv/face/facenet/pytorch) | PyTorch | CASIA-WebFaces&LFW | +| [FaceNet](cv/face/facenet/tensorflow) | TensorFlow | CASIA-WebFaces&LFW | #### Instance Segmentation -| Model | Framework | Dataset| -|---------------------------------------------------------------|--------------|--------| -| [SOLO](cv/instance_segmentation/SOLO/pytorch/README.md) | PyTorch | COCO | -| [SOLOv2](cv/detection/solov2/paddlepaddle/README.md) | PaddlePaddle | COCO | -| [SOLOv2](cv/instance_segmentation/solov2/pytorch/README.md) | PyTorch | COCO | -| [YOLACT++](cv/instance_segmentation/yolact/pytorch/README.md) | PyTorch | COCO | +| Model | Framework | Dataset | +|-----------------------------------------------------|--------------|---------| +| [SOLO](cv/instance_segmentation/SOLO/pytorch) | PyTorch | COCO | +| [SOLOv2](cv/detection/solov2/paddlepaddle) | PaddlePaddle | COCO | +| [SOLOv2](cv/instance_segmentation/solov2/pytorch) | PyTorch | COCO | +| [YOLACT++](cv/instance_segmentation/yolact/pytorch) | PyTorch | COCO | #### Image Generation -| Model | Framework | Dataset | -|---------------------------------------------------------------|--------------|----------| -| [DCGAN](cv/image_generation/dcgan/mindspore/README.md) | MindSpore | ImageNet | -| [Pix2Pix](cv/image_generation/Pix2pix/paddlepaddle/README.md) | PaddlePaddle | facades | +| Model | Framework | Dataset | +|-----------------------------------------------------|--------------|----------| +| [DCGAN](cv/image_generation/dcgan/mindspore) | MindSpore | ImageNet | +| [Pix2Pix](cv/image_generation/Pix2pix/paddlepaddle) | PaddlePaddle | facades | #### Knowledge Distillation -| Model | Framework | Dataset | -|---------------------------------------------|-----------|--------------| -| [CWD](cv/distiller/CWD/pytorch/README.md) | PyTorch | Cityscapes | -| [RKD](cv/distiller/RKD/pytorch/README.md) | PyTorch | CUB-200-2011 | -| [WSLD](cv/distiller/WSLD/pytorch/README.md) | PyTorch | ImageNet | - -#### Network Pruning - -| Model | Framework | Dataset | -|-------------------------------------------------------------------|-----------|--------------| -| [Network Slimming](cv/Pruning/Network-Slimming/pytorch/README.md) | PyTorch | CIFAR-10/100 | +| Model | Framework | Dataset | +|-----------------------------------|-----------|--------------| +| [CWD](cv/distiller/CWD/pytorch) | PyTorch | Cityscapes | +| [RKD](cv/distiller/RKD/pytorch) | PyTorch | CUB-200-2011 | +| [WSLD](cv/distiller/WSLD/pytorch) | PyTorch | ImageNet | #### Object Detection -| Model | Framework | Dataset| -|-------------------------------------------------------------------------|-----------------------|--------| -| [ATSS](cv/detection/atss_mmdet/pytorch/README.md) | PyTorch (MMDetection) | COCO | -| [AutoAssign](cv/detection/autoassign/pytorch/README.md) | PyTorch | COCO | -| [Cascade R-CNN](cv/detection/cascade_rcnn_mmdet/pytorch/README.md) | PyTorch (MMDetection) | COCO | -| [CenterMask2](cv/detection/centermask2/pytorch/README.md) | PyTorch | COCO | -| [CenterNet](cv/detection/centernet/pytorch/README.md) | PyTorch | COCO | -| [CenterNet](cv/detection/centernet/paddlepaddle/README.md) | PaddlePaddle | COCO | -| [Co-DETR](cv/detection/co-detr/pytorch/README.md) | PyTorch | COCO | -| [CornerNet](cv/detection/cornernet_mmdet/pytorch/README.md) | PyTorch (MMDetection) | COCO | -| [DCNV2](cv/detection/dcnv2_mmdet/pytorch/README.md) | PyTorch (MMDetection) | COCO | -| [DETR](cv/detection/detr/paddlepaddle/README.md) | PaddlePaddle | COCO | -| [Faster R-CNN](cv/detection/fasterrcnn/pytorch/README.md) | PyTorch | COCO | -| [FCOS](cv/detection/fcos/paddlepaddle/README.md) | PaddlePaddle | COCO | -| [FCOS](cv/detection/fcos/pytorch/README.md) | PyTorch | COCO | -| [Mamba-YOLO](cv/detection/mamba_yolo/pytorch/README.md) | PyTorch | COCO | -| [Mask R-CNN](cv/detection/maskrcnn/pytorch/README.md) | PyTorch | COCO | -| [Mask R-CNN](cv/detection/maskrcnn/paddlepaddle/README.md) | PaddlePaddle | COCO | -| [OC_SORT](cv/detection/oc_sort/paddlepaddle/README.md) | PaddlePaddle | MOT17 | -| [Oriented RepPoints](cv/detection/oriented_reppoints/pytorch/README.md) | PyTorch | DOTA | -| [PP-PicoDet](cv/detection/picodet/paddlepaddle/README.md) | PaddlePaddle | COCO | -| [PP-YOLOE](cv/detection/pp-yoloe/paddlepaddle/README.md) | PaddlePaddle | COCO | -| [PP-YOLOE+](cv/detection/pp_yoloe+/paddlepaddle/README.md) | PaddlePaddle | COCO | -| [PVANet](cv/detection/pvanet/pytorch/README.md) | PyTorch | COCO | -| [RepPoints](cv/detection/reppoints_mmdet/pytorch/README.md) | PyTorch (MMDetection) | COCO | -| [RetinaNet](cv/detection/retinanet/pytorch/README.md) | PyTorch | COCO | -| [RetinaNet](cv/detection/retinanet/paddlepaddle/README.md) | PaddlePaddle | COCO | -| [RT-DETR](cv/detection/rt-detr/pytorch/README.md) | PyTorch | COCO | -| [RTMDet](cv/detection/rtmdet/pytorch/README.md) | PyTorch | COCO | -| [SSD](cv/detection/ssd/pytorch/README.md) | PyTorch | COCO | -| [SSD](cv/detection/ssd/paddlepaddle/README.md) | PaddlePaddle | COCO | -| [SSD](cv/detection/ssd/tensorflow/README.md) | TensorFlow | VOC | -| [SSD](cv/detection/ssd/mindspore/README.md) | MindSpore | COCO | -| [YOLOF](cv/detection/yolof/pytorch/README.md) | PyTorch | COCO | -| [YOLOv3](cv/detection/yolov3/pytorch/README.md) | PyTorch | COCO | -| [YOLOv3](cv/detection/yolov3/paddlepaddle/README.md) | PaddlePaddle | COCO | -| [YOLOv3](cv/detection/yolov3/tensorflow/README.md) | TensorFlow | VOC | -| [YOLOv5](cv/detection/yolov5/paddlepaddle/README.md) | PaddlePaddle | COCO | -| [YOLOv5](cv/detection/yolov5/pytorch/README.md) | PyTorch | COCO | -| [YOLOv6](cv/detection/yolov6/pytorch/README.md) | PyTorch | COCO | -| [YOLOv7](cv/detection/yolov7/pytorch/README.md) | PyTorch | COCO | -| [YOLOv8](cv/detection/yolov8/pytorch/README.md) | PyTorch | COCO | -| [YOLOv9](cv/detection/yolov9/pytorch/README.md) | PyTorch | COCO | -| [YOLOv10](cv/detection/yolov10/pytorch/README.md) | PyTorch | COCO | +| Model | Framework | Dataset | +|---------------------------------------------------------------|-----------------------|---------| +| [ATSS](cv/detection/atss_mmdet/pytorch) | PyTorch (MMDetection) | COCO | +| [AutoAssign](cv/detection/autoassign/pytorch) | PyTorch | COCO | +| [Cascade R-CNN](cv/detection/cascade_rcnn_mmdet/pytorch) | PyTorch (MMDetection) | COCO | +| [CenterMask2](cv/detection/centermask2/pytorch) | PyTorch | COCO | +| [CenterNet](cv/detection/centernet/pytorch) | PyTorch | COCO | +| [CenterNet](cv/detection/centernet/paddlepaddle) | PaddlePaddle | COCO | +| [Co-DETR](cv/detection/co-detr/pytorch) | PyTorch | COCO | +| [CornerNet](cv/detection/cornernet_mmdet/pytorch) | PyTorch (MMDetection) | COCO | +| [DCNV2](cv/detection/dcnv2_mmdet/pytorch) | PyTorch (MMDetection) | COCO | +| [DETR](cv/detection/detr/paddlepaddle) | PaddlePaddle | COCO | +| [Faster R-CNN](cv/detection/fasterrcnn/pytorch) | PyTorch | COCO | +| [FCOS](cv/detection/fcos/paddlepaddle) | PaddlePaddle | COCO | +| [FCOS](cv/detection/fcos/pytorch) | PyTorch | COCO | +| [Mamba-YOLO](cv/detection/mamba_yolo/pytorch) | PyTorch | COCO | +| [Mask R-CNN](cv/detection/maskrcnn/pytorch) | PyTorch | COCO | +| [Mask R-CNN](cv/detection/maskrcnn/paddlepaddle) | PaddlePaddle | COCO | +| [OC_SORT](cv/detection/oc_sort/paddlepaddle) | PaddlePaddle | MOT17 | +| [Oriented RepPoints](cv/detection/oriented_reppoints/pytorch) | PyTorch | DOTA | +| [PP-PicoDet](cv/detection/picodet/paddlepaddle) | PaddlePaddle | COCO | +| [PP-YOLOE](cv/detection/pp-yoloe/paddlepaddle) | PaddlePaddle | COCO | +| [PP-YOLOE+](cv/detection/pp_yoloe+/paddlepaddle) | PaddlePaddle | COCO | +| [PVANet](cv/detection/pvanet/pytorch) | PyTorch | COCO | +| [RepPoints](cv/detection/reppoints_mmdet/pytorch) | PyTorch (MMDetection) | COCO | +| [RetinaNet](cv/detection/retinanet/pytorch) | PyTorch | COCO | +| [RetinaNet](cv/detection/retinanet/paddlepaddle) | PaddlePaddle | COCO | +| [RT-DETR](cv/detection/rt-detr/pytorch) | PyTorch | COCO | +| [RTMDet](cv/detection/rtmdet/pytorch) | PyTorch | COCO | +| [SSD](cv/detection/ssd/pytorch) | PyTorch | COCO | +| [SSD](cv/detection/ssd/paddlepaddle) | PaddlePaddle | COCO | +| [SSD](cv/detection/ssd/tensorflow) | TensorFlow | VOC | +| [SSD](cv/detection/ssd/mindspore) | MindSpore | COCO | +| [YOLOF](cv/detection/yolof/pytorch) | PyTorch | COCO | +| [YOLOv3](cv/detection/yolov3/pytorch) | PyTorch | COCO | +| [YOLOv3](cv/detection/yolov3/paddlepaddle) | PaddlePaddle | COCO | +| [YOLOv3](cv/detection/yolov3/tensorflow) | TensorFlow | VOC | +| [YOLOv5](cv/detection/yolov5/paddlepaddle) | PaddlePaddle | COCO | +| [YOLOv5](cv/detection/yolov5/pytorch) | PyTorch | COCO | +| [YOLOv6](cv/detection/yolov6/pytorch) | PyTorch | COCO | +| [YOLOv7](cv/detection/yolov7/pytorch) | PyTorch | COCO | +| [YOLOv8](cv/detection/yolov8/pytorch) | PyTorch | COCO | +| [YOLOv9](cv/detection/yolov9/pytorch) | PyTorch | COCO | +| [YOLOv10](cv/detection/yolov10/pytorch) | PyTorch | COCO | #### 3D Object Detection -| Model | Framework | Dataset | -|-------------------------------------------------------------------------|-----------|------------------| -| [BEVFormer](cv/3d_detection/BEVFormer/pytorch/README.md) | PyTorch | nuScenes&CAN bus | -| [CenterPoint](cv/3d_detection/centerpoint/pytorch/README.md) | PyTorch | nuScenes | -| [PAConv](cv/3d_detection/PAConv/pytorch/README.md) | PyTorch | S3DIS | -| [Part-A2-Anchor](cv/3d_detection/part_a2_anchor/pytorch/README.md) | PyTorch | KITTI | -| [Part-A2-Free](cv/3d_detection/part_a2_free/pytorch/README.md) | PyTorch | KITTI | -| [PointNet++](cv/3d_detection/pointnet2/pytorch/mmdetection3d/README.md) | PyTorch | S3DIS | -| [PointPillars](cv/3d_detection/pointpillars/pytorch/README.md) | PyTorch | KITTI | -| [PointRCNN](cv/3d_detection/pointrcnn/pytorch/README.md) | PyTorch | KITTI | -| [PointRCNN-IoU](cv/3d_detection/pointrcnn_iou/pytorch/README.md) | PyTorch | KITTI | -| [SECOND](cv/3d_detection/second/pytorch/README.md) | PyTorch | KITTI | -| [SECOND-IoU](cv/3d_detection/second_iou/pytorch/README.md) | PyTorch | KITTI | +| Model | Framework | Dataset | +|---------------------------------------------------------------|-----------|------------------| +| [BEVFormer](cv/3d_detection/BEVFormer/pytorch) | PyTorch | nuScenes&CAN bus | +| [CenterPoint](cv/3d_detection/centerpoint/pytorch) | PyTorch | nuScenes | +| [PAConv](cv/3d_detection/PAConv/pytorch) | PyTorch | S3DIS | +| [Part-A2-Anchor](cv/3d_detection/part_a2_anchor/pytorch) | PyTorch | KITTI | +| [Part-A2-Free](cv/3d_detection/part_a2_free/pytorch) | PyTorch | KITTI | +| [PointNet++](cv/3d_detection/pointnet2/pytorch/mmdetection3d) | PyTorch | S3DIS | +| [PointPillars](cv/3d_detection/pointpillars/pytorch) | PyTorch | KITTI | +| [PointRCNN](cv/3d_detection/pointrcnn/pytorch) | PyTorch | KITTI | +| [PointRCNN-IoU](cv/3d_detection/pointrcnn_iou/pytorch) | PyTorch | KITTI | +| [SECOND](cv/3d_detection/second/pytorch) | PyTorch | KITTI | +| [SECOND-IoU](cv/3d_detection/second_iou/pytorch) | PyTorch | KITTI | #### 3D Reconstruction -| Model | Framework | Dataset | -|----------------------------------------------------------|-----------|---------| -| [HashNeRF](cv/3d-reconstruction/hashnerf/pytorch/README.md) | PyTorch | fox | +| Model | Framework | Dataset | +|---------------------------------------------------|-----------|---------| +| [HashNeRF](cv/3d-reconstruction/hashnerf/pytorch) | PyTorch | fox | #### GNN (Graph Neural Network) -| Model | Framework | Dataset | -|------------------------------------------------------|--------------|--------------------------| -| [GAT](cv/gnn/gat/paddlepaddle/README.md) | PaddlePaddle | CORA | -| [GCN](cv/gnn/GCN/mindspore/README.md) | MindSpore | CORA & Citeseer | -| [GCN](cv/gnn/GCN/paddlepaddle/README.md) | PaddlePaddle | CORA & PubMed & Citeseer | -| [GraphSAGE](cv/gnn/graphsage/paddlepaddle/README.md) | PaddlePaddle | Reddit | +| Model | Framework | Dataset | +|--------------------------------------------|--------------|--------------------------| +| [GAT](cv/gnn/gat/paddlepaddle) | PaddlePaddle | CORA | +| [GCN](cv/gnn/GCN/mindspore) | MindSpore | CORA & Citeseer | +| [GCN](cv/gnn/GCN/paddlepaddle) | PaddlePaddle | CORA & PubMed & Citeseer | +| [GraphSAGE](cv/gnn/graphsage/paddlepaddle) | PaddlePaddle | Reddit | #### OCR -| Model | Framework | Dataset | -|----------------------------------------------------------|--------------|-----------| -| [CRNN](cv/ocr/crnn/mindspore/README.md) | MindSpore | OCR_Recog | -| [CRNN](cv/ocr/crnn/paddlepaddle/README.md) | PaddlePaddle | LMDB | -| [DBNet](cv/ocr/dbnet/pytorch/README.md) | PyTorch | ICDAR2015 | -| [DBNet++](cv/ocr/dbnetpp/paddlepaddle/README.md) | PaddlePaddle | ICDAR2015 | -| [DBNet++](cv/ocr/dbnetpp/pytorch/README.md) | PyTorch | ICDAR2015 | -| [PP-OCR-DB](cv/ocr/pp-ocr-db/paddlepaddle/README.md) | PaddlePaddle | ICDAR2015 | -| [PP-OCR-EAST](cv/ocr/pp-ocr-east/paddlepaddle/README.md) | PaddlePaddle | ICDAR2015 | -| [PSE](cv/ocr/pse/paddlepaddle/README.md) | PaddlePaddle | OCR_Recog | -| [SAR](cv/ocr/sar/pytorch/README.md) | PyTorch | OCR_Recog | -| [SAST](cv/ocr/sast/paddlepaddle/README.md) | PaddlePaddle | ICDAR2015 | -| [SATRN](cv/ocr/satrn/pytorch/base/README.md) | PyTorch | OCR_Recog | +| Model | Framework | Dataset | +|------------------------------------------------|--------------|-----------| +| [CRNN](cv/ocr/crnn/mindspore) | MindSpore | OCR_Recog | +| [CRNN](cv/ocr/crnn/paddlepaddle) | PaddlePaddle | LMDB | +| [DBNet](cv/ocr/dbnet/pytorch) | PyTorch | ICDAR2015 | +| [DBNet++](cv/ocr/dbnetpp/paddlepaddle) | PaddlePaddle | ICDAR2015 | +| [DBNet++](cv/ocr/dbnetpp/pytorch) | PyTorch | ICDAR2015 | +| [PP-OCR-DB](cv/ocr/pp-ocr-db/paddlepaddle) | PaddlePaddle | ICDAR2015 | +| [PP-OCR-EAST](cv/ocr/pp-ocr-east/paddlepaddle) | PaddlePaddle | ICDAR2015 | +| [PSE](cv/ocr/pse/paddlepaddle) | PaddlePaddle | OCR_Recog | +| [SAR](cv/ocr/sar/pytorch) | PyTorch | OCR_Recog | +| [SAST](cv/ocr/sast/paddlepaddle) | PaddlePaddle | ICDAR2015 | +| [SATRN](cv/ocr/satrn/pytorch/base) | PyTorch | OCR_Recog | #### Point Cloud -| Model | Framework | Dataset | -|-----------------------------------------------------------|-----------|---------------------------------| -| [Point-BERT](cv/point_cloud/point-bert/pytorch/README.md) | PyTorch | ShapeNet55 & processed ModelNet | +| Model | Framework | Dataset | +|-------------------------------------------------|-----------|---------------------------------| +| [Point-BERT](cv/point_cloud/point-bert/pytorch) | PyTorch | ShapeNet55 & processed ModelNet | #### Pose Estimation -| Model | Framework | Dataset | -|---------------------------------------------------|--------------|---------| -| [AlphaPose](cv/pose/alphapose/pytorch/README.md) | PyTorch | COCO | -| [HRNet](cv/pose/hrnet/pytorch/README.md) | PyTorch | COCO | -| [HRNet-W32](cv/pose/hrnet/paddlepaddle/README.md) | PaddlePaddle | COCO | -| [OpenPose](cv/pose/openpose/mindspore/README.md) | MindSpore | COCO | +| Model | Framework | Dataset | +|-----------------------------------------|--------------|---------| +| [AlphaPose](cv/pose/alphapose/pytorch) | PyTorch | COCO | +| [HRNet](cv/pose/hrnet/pytorch) | PyTorch | COCO | +| [HRNet-W32](cv/pose/hrnet/paddlepaddle) | PaddlePaddle | COCO | +| [OpenPose](cv/pose/openpose/mindspore) | MindSpore | COCO | #### Self-Supervised Learning -| Model | Framework | Dataset | -|----------------------------------------------------------|-----------|----------| -| [MAE](cv/self_supervised_learning/MAE/pytorch/README.md) | PyTorch | ImageNet | +| Model | Framework | Dataset | +|------------------------------------------------|-----------|----------| +| [MAE](cv/self_supervised_learning/MAE/pytorch) | PyTorch | ImageNet | #### Semantic Segmentation -| Model | Framework | Dataset | -|--------------------------------------------------------------------------------|--------------|----------------| -| [3D-UNet](cv/semantic_segmentation/unet3d/pytorch/README.md) | PyTorch | kits19 | -| [APCNet](cv/semantic_segmentation/apcnet/pytorch/README.md) | PyTorch | Cityscapes | -| [Attention U-net](cv/semantic_segmentation/att_unet/pytorch/README.md) | PyTorch | Cityscapes | -| [BiSeNet](cv/semantic_segmentation/bisenet/pytorch/README.md) | PyTorch | COCO | -| [BiSeNetV2](cv/semantic_segmentation/bisenetv2/paddlepaddle/README.md) | PaddlePaddle | Cityscapes | -| [BiSeNetV2](cv/semantic_segmentation/bisenetv2/pytorch/README.md) | PyTorch | Cityscapes | -| [CGNet](cv/semantic_segmentation/cgnet/pytorch/README.md) | PyTorch | COCO | -| [ContextNet](cv/semantic_segmentation/contextnet/pytorch/README.md) | PyTorch | COCO | -| [DabNet](cv/semantic_segmentation/dabnet/pytorch/README.md) | PyTorch | COCO | -| [DANet](cv/semantic_segmentation/danet/pytorch/README.md) | PyTorch | COCO | -| [DDRnet](cv/semantic_segmentation/ddrnet/pytorch/README.md) | PyTorch | Cityscapes | -| [DeepLabV3](cv/semantic_segmentation/deeplabv3/pytorch/README.md) | PyTorch | COCO | -| [DeepLabV3](cv/semantic_segmentation/deeplabv3/paddlepaddle/README.md) | PaddlePaddle | Cityscapes | -| [DeepLabV3](cv/semantic_segmentation/deeplabv3/mindspore/README.md) | MindSpore | VOC | -| [DeepLabV3+](cv/semantic_segmentation/deeplabv3plus/paddlepaddle/README.md) | PaddlePaddle | Cityscapes | -| [DeepLabV3+](cv/semantic_segmentation/deeplabv3plus/tensorflow/README.md) | TensorFlow | Cityscapes | -| [DenseASPP](cv/semantic_segmentation/denseaspp/pytorch/README.md) | PyTorch | COCO | -| [DFANet](cv/semantic_segmentation/dfanet/pytorch/README.md) | PyTorch | COCO | -| [DNLNet](cv/semantic_segmentation/dnlnet/paddlepaddle/README.md) | PaddlePaddle | Cityscapes | -| [DUNet](cv/semantic_segmentation/dunet/pytorch/README.md) | PyTorch | COCO | -| [EncNet](cv/semantic_segmentation/encnet/pytorch/README.md) | PyTorch | COCO | -| [ENet](cv/semantic_segmentation/enet/pytorch/README.md) | PyTorch | COCO | -| [ERFNet](cv/semantic_segmentation/erfnet/pytorch/README.md) | PyTorch | COCO | -| [ESPNet](cv/semantic_segmentation/espnet/pytorch/README.md) | PyTorch | COCO | -| [FastFCN](cv/semantic_segmentation/fastfcn/paddlepaddle/README.md) | PyTorch | ADE20K | -| [FastSCNN](cv/semantic_segmentation/fastscnn/pytorch/README.md) | PyTorch | COCO | -| [FCN](cv/semantic_segmentation/fcn/pytorch/README.md) | PyTorch | COCO | -| [FPENet](cv/semantic_segmentation/fpenet/pytorch/README.md) | PyTorch | COCO | -| [GCNet](cv/semantic_segmentation/gcnet/pytorch/README.md) | PyTorch | Cityscapes | -| [HardNet](cv/semantic_segmentation/hardnet/pytorch/README.md) | PyTorch | COCO | -| [ICNet](cv/semantic_segmentation/icnet/pytorch/README.md) | PyTorch | COCO | -| [LedNet](cv/semantic_segmentation/lednet/pytorch/README.md) | PyTorch | COCO | -| [LinkNet](cv/semantic_segmentation/linknet/pytorch/README.md) | PyTorch | COCO | -| [Mask2Former](cv/semantic_segmentation/Mask2Former/pytorch/README.md) | PyTorch | Cityscapes | -| [MobileSeg](cv/semantic_segmentation/mobileseg/paddlepaddle/README.md) | PaddlePaddle | Cityscapes | -| [OCNet](cv/semantic_segmentation/ocnet/pytorch/README.md) | PyTorch | COCO | -| [OCRNet](cv/semantic_segmentation/ocrnet/paddlepaddle/README.md) | PaddlePaddle | Cityscapes | -| [OCRNet](cv/semantic_segmentation/ocrnet/pytorch/README.md) | PyTorch | Cityscapes | -| [PP-HumanSegV1](cv/semantic_segmentation/pp_humansegv1/paddlepaddle/README.md) | PaddlePaddle | PP-HumanSeg14K | -| [PP-HumanSegV2](cv/semantic_segmentation/pp_humansegv2/paddlepaddle/README.md) | PaddlePaddle | PP-HumanSeg14K | -| [PP-LiteSeg](cv/semantic_segmentation/pp_liteseg/paddlepaddle/README.md) | PaddlePaddle | Cityscapes | -| [PSANet](cv/semantic_segmentation/psanet/pytorch/README.md) | PyTorch | COCO | -| [RefineNet](cv/semantic_segmentation/refinenet/pytorch/README.md) | PyTorch | COCO | -| [SegNet](cv/semantic_segmentation/segnet/pytorch/README.md) | PyTorch | COCO | -| [STDC](cv/semantic_segmentation/stdc/paddlepaddle/README.md) | PaddlePaddle | Cityscapes | -| [STDC](cv/semantic_segmentation/stdc/pytorch/README.md) | PyTorch | Cityscapes | -| [UNet](cv/semantic_segmentation/unet/pytorch/README.md) | PyTorch | COCO | -| [UNet](cv/semantic_segmentation/unet/paddlepaddle/README.md) | PaddlePaddle | Cityscapes | -| [UNet++](cv/semantic_segmentation/unet++/pytorch/README.md) | PyTorch | DRIVE | -| [VNet](cv/semantic_segmentation/vnet/tensorflow/README.md) | TensorFlow | Hippocampus | +| Model | Framework | Dataset | +|----------------------------------------------------------------------|--------------|----------------| +| [3D-UNet](cv/semantic_segmentation/unet3d/pytorch) | PyTorch | kits19 | +| [APCNet](cv/semantic_segmentation/apcnet/pytorch) | PyTorch | Cityscapes | +| [Attention U-net](cv/semantic_segmentation/att_unet/pytorch) | PyTorch | Cityscapes | +| [BiSeNet](cv/semantic_segmentation/bisenet/pytorch) | PyTorch | COCO | +| [BiSeNetV2](cv/semantic_segmentation/bisenetv2/paddlepaddle) | PaddlePaddle | Cityscapes | +| [BiSeNetV2](cv/semantic_segmentation/bisenetv2/pytorch) | PyTorch | Cityscapes | +| [CGNet](cv/semantic_segmentation/cgnet/pytorch) | PyTorch | COCO | +| [ContextNet](cv/semantic_segmentation/contextnet/pytorch) | PyTorch | COCO | +| [DabNet](cv/semantic_segmentation/dabnet/pytorch) | PyTorch | COCO | +| [DANet](cv/semantic_segmentation/danet/pytorch) | PyTorch | COCO | +| [DDRnet](cv/semantic_segmentation/ddrnet/pytorch) | PyTorch | Cityscapes | +| [DeepLabV3](cv/semantic_segmentation/deeplabv3/pytorch) | PyTorch | COCO | +| [DeepLabV3](cv/semantic_segmentation/deeplabv3/paddlepaddle) | PaddlePaddle | Cityscapes | +| [DeepLabV3](cv/semantic_segmentation/deeplabv3/mindspore) | MindSpore | VOC | +| [DeepLabV3+](cv/semantic_segmentation/deeplabv3plus/paddlepaddle) | PaddlePaddle | Cityscapes | +| [DeepLabV3+](cv/semantic_segmentation/deeplabv3plus/tensorflow) | TensorFlow | Cityscapes | +| [DenseASPP](cv/semantic_segmentation/denseaspp/pytorch) | PyTorch | COCO | +| [DFANet](cv/semantic_segmentation/dfanet/pytorch) | PyTorch | COCO | +| [DNLNet](cv/semantic_segmentation/dnlnet/paddlepaddle) | PaddlePaddle | Cityscapes | +| [DUNet](cv/semantic_segmentation/dunet/pytorch) | PyTorch | COCO | +| [EncNet](cv/semantic_segmentation/encnet/pytorch) | PyTorch | COCO | +| [ENet](cv/semantic_segmentation/enet/pytorch) | PyTorch | COCO | +| [ERFNet](cv/semantic_segmentation/erfnet/pytorch) | PyTorch | COCO | +| [ESPNet](cv/semantic_segmentation/espnet/pytorch) | PyTorch | COCO | +| [FastFCN](cv/semantic_segmentation/fastfcn/paddlepaddle) | PyTorch | ADE20K | +| [FastSCNN](cv/semantic_segmentation/fastscnn/pytorch) | PyTorch | COCO | +| [FCN](cv/semantic_segmentation/fcn/pytorch) | PyTorch | COCO | +| [FPENet](cv/semantic_segmentation/fpenet/pytorch) | PyTorch | COCO | +| [GCNet](cv/semantic_segmentation/gcnet/pytorch) | PyTorch | Cityscapes | +| [HardNet](cv/semantic_segmentation/hardnet/pytorch) | PyTorch | COCO | +| [ICNet](cv/semantic_segmentation/icnet/pytorch) | PyTorch | COCO | +| [LedNet](cv/semantic_segmentation/lednet/pytorch) | PyTorch | COCO | +| [LinkNet](cv/semantic_segmentation/linknet/pytorch) | PyTorch | COCO | +| [Mask2Former](cv/semantic_segmentation/Mask2Former/pytorch) | PyTorch | Cityscapes | +| [MobileSeg](cv/semantic_segmentation/mobileseg/paddlepaddle) | PaddlePaddle | Cityscapes | +| [OCNet](cv/semantic_segmentation/ocnet/pytorch) | PyTorch | COCO | +| [OCRNet](cv/semantic_segmentation/ocrnet/paddlepaddle) | PaddlePaddle | Cityscapes | +| [OCRNet](cv/semantic_segmentation/ocrnet/pytorch) | PyTorch | Cityscapes | +| [PP-HumanSegV1](cv/semantic_segmentation/pp_humansegv1/paddlepaddle) | PaddlePaddle | PP-HumanSeg14K | +| [PP-HumanSegV2](cv/semantic_segmentation/pp_humansegv2/paddlepaddle) | PaddlePaddle | PP-HumanSeg14K | +| [PP-LiteSeg](cv/semantic_segmentation/pp_liteseg/paddlepaddle) | PaddlePaddle | Cityscapes | +| [PSANet](cv/semantic_segmentation/psanet/pytorch) | PyTorch | COCO | +| [RefineNet](cv/semantic_segmentation/refinenet/pytorch) | PyTorch | COCO | +| [SegNet](cv/semantic_segmentation/segnet/pytorch) | PyTorch | COCO | +| [STDC](cv/semantic_segmentation/stdc/paddlepaddle) | PaddlePaddle | Cityscapes | +| [STDC](cv/semantic_segmentation/stdc/pytorch) | PyTorch | Cityscapes | +| [UNet](cv/semantic_segmentation/unet/pytorch) | PyTorch | COCO | +| [UNet](cv/semantic_segmentation/unet/paddlepaddle) | PaddlePaddle | Cityscapes | +| [UNet++](cv/semantic_segmentation/unet++/pytorch) | PyTorch | DRIVE | +| [VNet](cv/semantic_segmentation/vnet/tensorflow) | TensorFlow | Hippocampus | #### Super Resolution -| Model | Framework | Dataset | -|---------------------------------------------------------------------|-----------|---------| -| [basicVSR++](cv/super_resolution/basicVSR++/pytorch/README.md) | PyTorch | REDS | -| [basicVSR](cv/super_resolution/basicVSR/pytorch/README.md) | PyTorch | REDS | -| [ESRGAN](cv/super_resolution/esrgan/pytorch/README.md) | PyTorch | DIV2K | -| [LIIF](cv/super_resolution/liif/pytorch/README.md) | PyTorch | DIV2K | -| [RealBasicVSR](cv/super_resolution/real_basicVSR/pytorch/README.md) | PyTorch | REDS | -| [TTSR](cv/super_resolution/ttsr/pytorch/README.md) | PyTorch | CUFED | -| [TTVSR](cv/super_resolution/ttvsr/pytorch/README.md) | PyTorch | REDS | +| Model | Framework | Dataset | +|-----------------------------------------------------------|-----------|---------| +| [basicVSR++](cv/super_resolution/basicVSR++/pytorch) | PyTorch | REDS | +| [basicVSR](cv/super_resolution/basicVSR/pytorch) | PyTorch | REDS | +| [ESRGAN](cv/super_resolution/esrgan/pytorch) | PyTorch | DIV2K | +| [LIIF](cv/super_resolution/liif/pytorch) | PyTorch | DIV2K | +| [RealBasicVSR](cv/super_resolution/real_basicVSR/pytorch) | PyTorch | REDS | +| [TTSR](cv/super_resolution/ttsr/pytorch) | PyTorch | CUFED | +| [TTVSR](cv/super_resolution/ttvsr/pytorch) | PyTorch | REDS | #### Multi-Object Tracking -| Model | Framework | Dataset | -|-----------------------------------------------------------|--------------|-------------| -| [ByteTrack](cv/tracking/bytetrack/paddlepaddle/README.md) | PaddlePaddle | MOT17 | -| [DeepSORT](cv/tracking/deep_sort/pytorch/README.md) | PyTorch | Market-1501 | -| [FairMOT](cv/tracking/fairmot/pytorch/README.md) | PyTorch | MOT17 | - -#### Traffic Forecast - -| Model | Framework | Dataset | -|----------------------------------------------------------------------|-----------|--------------------| -| [Graph WaveNet](cv/traffic_forecast/graph_wavenet/pytorch/README.md) | PyTorch | METR-LA & PEMS-BAY | +| Model | Framework | Dataset | +|-------------------------------------------------|--------------|-------------| +| [ByteTrack](cv/tracking/bytetrack/paddlepaddle) | PaddlePaddle | MOT17 | +| [DeepSORT](cv/tracking/deep_sort/pytorch) | PyTorch | Market-1501 | +| [FairMOT](cv/tracking/fairmot/pytorch) | PyTorch | MOT17 | ### LLM (Large Language Model) -| Model | Framework | ToolBox | Dataset/Weight | -|-----------------------------------------------------------------------|-----------|--------------------|-----------------------| -| [Aquila2-34B](nlp/llm/aquila2-34b/megatron-deepspeed/README.md) | PyTorch | Megatron-DeepSpeed | Bookcorpus | -| [Baichuan2-7B](nlp/llm/baichuan2-7b/Baichuan2/README.md) | PyTorch | DeepSpeed | baichuan2-7b-base | -| [Bloom-7B1](nlp/llm/bloom-7b1/firefly/README.md) | PyTorch | Firefly | school_math_0.25M | -| [ChatGLM-6B](nlp/llm/chatglm-6b/deepspeed/README.md) | PyTorch | DeepSpeed | ADGEN & chatglm-6b | -| [ChatGLM2-6B SFT](nlp/llm/ChatGLM2-6b-sft/README.md) | PyTorch | DeepSpeed | ADGEN & chatglm2-6b | -| [ChatGLM3-6B](nlp/llm/chatglm3-6b/deepspeed/finetune_demo/README.md) | PyTorch | DeepSpeed | ADGEN & chatglm3-6b | -| [DeepSeekMoE 7B](nlp/llm/deepseek_moe_7b/colossalai/README.md) | PyTorch | ColossalAI | deepseek-moe-16b-base | -| [Llama-7B](nlp/llm/llama-7b/colossalai/README.md) | PyTorch | ColossalAI | llama-7b-hf | -| [Llama2-7B](nlp/llm/llama2-7b/megatron-deepspeed/README.md) | PyTorch | Megatron-DeepSpeed | Bookcorpus | -| [Llama2-7B RMF](nlp/llm/llama2-7b_reward_sft/deepspeed/README.md) | PyTorch | DeepSpeed | Dahoas/rm-static | -| [Llama2-7B RLHF](nlp/llm/llama2-7b_rlhf/megatron-deepspeed/README.md) | PyTorch | Megatron-DeepSpeed | llama2-7b&tiny-llama | -| [Llama2-7B SFT](nlp/llm/llama2-7b_sft/megatron-deepspeed/README.md) | PyTorch | Megatron-DeepSpeed | GPT Small-117M | -| [Llama2-13B](nlp/llm/llama2-13b/megatron-deepspeed/README.md) | PyTorch | Megatron-DeepSpeed | Bookcorpus | -| [Llama2-34B](nlp/llm/llama2-34b/megatron-deepspeed/README.md) | PyTorch | Megatron-DeepSpeed | Bookcorpus | -| [Llama3-8B](nlp/llm/llama3_8b/megatron-deepspeed/README.md) | PyTorch | Megatron-DeepSpeed | Bookcorpus | -| [Llama3-8B SFT](nlp/llm/llama3_8b/colossalai/README.md) | PyTorch | ColossalAI | school_math_0.25M | -| [Mamba-2](nlp/llm/mamba-2/megatron-lm/README.md) | PyTorch | Megatron-LM | GPT Small-117M | -| [Mixtral 8x7B](nlp/llm/mixtral/megatron-lm/README.md) | PyTorch | Megatron-LM | GPT Small-117M | -| [QWen-7B](nlp/llm/qwen-7b/firefly/README.md) | PyTorch | Firefly | qwen-7b | -| [QWen1.5-7B](nlp/llm/qwen1.5-7b/firefly/README.md) | PyTorch | Firefly | school_math | -| [QWen1.5-14B](nlp/llm/qwen1.5-14b/firefly/README.md) | PyTorch | Firefly | school_math | -| [Qwen2.5-7B SFT](nlp/llm/qwen2.5-7b/LLaMA-Factory/README.md) | PyTorch | LLaMA-Factory | qwen2.5-7b | +| Model | Framework | ToolBox | Dataset/Weight | +|-------------------------------------------------------------|-----------|--------------------|-----------------------| +| [Aquila2-34B](nlp/llm/aquila2-34b/megatron-deepspeed) | PyTorch | Megatron-DeepSpeed | Bookcorpus | +| [Baichuan2-7B](nlp/llm/baichuan2-7b/Baichuan2) | PyTorch | DeepSpeed | baichuan2-7b-base | +| [Bloom-7B1](nlp/llm/bloom-7b1/firefly) | PyTorch | Firefly | school_math_0.25M | +| [ChatGLM-6B](nlp/llm/chatglm-6b/deepspeed) | PyTorch | DeepSpeed | ADGEN & chatglm-6b | +| [ChatGLM2-6B SFT](nlp/llm/ChatGLM2-6b-sft) | PyTorch | DeepSpeed | ADGEN & chatglm2-6b | +| [ChatGLM3-6B](nlp/llm/chatglm3-6b/deepspeed/finetune_demo) | PyTorch | DeepSpeed | ADGEN & chatglm3-6b | +| [DeepSeekMoE 7B](nlp/llm/deepseek_moe_7b/colossalai) | PyTorch | ColossalAI | deepseek-moe-16b-base | +| [Llama-7B](nlp/llm/llama-7b/colossalai) | PyTorch | ColossalAI | llama-7b-hf | +| [Llama2-7B](nlp/llm/llama2-7b/megatron-deepspeed) | PyTorch | Megatron-DeepSpeed | Bookcorpus | +| [Llama2-7B RMF](nlp/llm/llama2-7b_reward_sft/deepspeed) | PyTorch | DeepSpeed | Dahoas/rm-static | +| [Llama2-7B RLHF](nlp/llm/llama2-7b_rlhf/megatron-deepspeed) | PyTorch | Megatron-DeepSpeed | llama2-7b&tiny-llama | +| [Llama2-7B SFT](nlp/llm/llama2-7b_sft/megatron-deepspeed) | PyTorch | Megatron-DeepSpeed | GPT Small-117M | +| [Llama2-13B](nlp/llm/llama2-13b/megatron-deepspeed) | PyTorch | Megatron-DeepSpeed | Bookcorpus | +| [Llama2-34B](nlp/llm/llama2-34b/megatron-deepspeed) | PyTorch | Megatron-DeepSpeed | Bookcorpus | +| [Llama3-8B](nlp/llm/llama3_8b/megatron-deepspeed) | PyTorch | Megatron-DeepSpeed | Bookcorpus | +| [Llama3-8B SFT](nlp/llm/llama3_8b/colossalai) | PyTorch | ColossalAI | school_math_0.25M | +| [Mamba-2](nlp/llm/mamba-2/megatron-lm) | PyTorch | Megatron-LM | GPT Small-117M | +| [Mixtral 8x7B](nlp/llm/mixtral/megatron-lm) | PyTorch | Megatron-LM | GPT Small-117M | +| [QWen-7B](nlp/llm/qwen-7b/firefly) | PyTorch | Firefly | qwen-7b | +| [QWen1.5-7B](nlp/llm/qwen1.5-7b/firefly) | PyTorch | Firefly | school_math | +| [QWen1.5-14B](nlp/llm/qwen1.5-14b/firefly) | PyTorch | Firefly | school_math | +| [Qwen2.5-7B SFT](nlp/llm/qwen2.5-7b/LLaMA-Factory) | PyTorch | LLaMA-Factory | qwen2.5-7b | ### Multimodal -| Model | Framework | Dataset | -|----------------------------------------------------------------------------------|-----------|----------------| -| [BLIP](multimodal/BLIP/pytorch/README.md) | PyTorch | COCO | -| [CLIP](multimodal/Language-Image_Pre-Training/clip/pytorch/README.md) | PyTorch | CIFAR100 | -| [ControlNet](multimodal/diffusion/ControlNet/README.md) | PyTorch | Fill50K | -| [DDPM](multimodal/diffusion/ddpm/README.md) | PyTorch | CIFAR-10 | -| [LLaVA 1.5](multimodal/llava/pytorch/README.md) | PyTorch | LLaVA-Pretrain | -| [L-Verse](multimodal/Language-Image_Pre-Training/L-Verse/pytorch/README.md) | PyTorch | ImageNet | -| [Stable Diffusion 1.4](multimodal/diffusion/stable-diffusion/training/README.md) | PyTorch | pokemon-images | -| [Stable Diffusion 1.5](multimodal/diffusion/stable-diffusion/sd_1.5/README.md) | PyTorch | pokemon-images | -| [Stable Diffusion 2.1](multimodal/diffusion/stable-diffusion/sd_2.1/README.md) | PyTorch | pokemon-images | -| [Stable Diffusion 3](multimodal/diffusion/stable-diffusion/sd_3/README.md) | PyTorch | dog-example | -| [Stable Diffusion XL](multimodal/diffusion/stable-diffusion/sd_xl/README.md) | PyTorch | pokemon-images | +| Model | Framework | Dataset | +|------------------------------------------------------------------------|-----------|----------------| +| [BLIP](multimodal/BLIP/pytorch) | PyTorch | COCO | +| [CLIP](multimodal/Language-Image_Pre-Training/clip/pytorch) | PyTorch | CIFAR100 | +| [ControlNet](multimodal/diffusion/ControlNet) | PyTorch | Fill50K | +| [DDPM](multimodal/diffusion/ddpm) | PyTorch | CIFAR-10 | +| [LLaVA 1.5](multimodal/llava/pytorch) | PyTorch | LLaVA-Pretrain | +| [L-Verse](multimodal/Language-Image_Pre-Training/L-Verse/pytorch) | PyTorch | ImageNet | +| [Stable Diffusion 1.4](multimodal/diffusion/stable-diffusion/training) | PyTorch | pokemon-images | +| [Stable Diffusion 1.5](multimodal/diffusion/stable-diffusion/sd_1.5) | PyTorch | pokemon-images | +| [Stable Diffusion 2.1](multimodal/diffusion/stable-diffusion/sd_2.1) | PyTorch | pokemon-images | +| [Stable Diffusion 3](multimodal/diffusion/stable-diffusion/sd_3) | PyTorch | dog-example | +| [Stable Diffusion XL](multimodal/diffusion/stable-diffusion/sd_xl) | PyTorch | pokemon-images | ### NLP (Natural Language Processing) #### Cloze Test -| Model | Framework | Dataset | -|-------------------------------------------------------------------|-----------|-----------------------| -| [GLM](nlp/cloze_test/glm/pytorch/GLMForMultiTokenCloze/README.md) | PyTorch | GLMForMultiTokenCloze | +| Model | Framework | Dataset | +|---------------------------------------------------------|-----------|-----------------------| +| [GLM](nlp/cloze_test/glm/pytorch/GLMForMultiTokenCloze) | PyTorch | GLMForMultiTokenCloze | #### Dialogue Generation -| Model | Framework | Dataset | -|------------------------------------------------------|-----------|---------| -| [CPM](nlp/dialogue_generation/cpm/pytorch/README.md) | PyTorch | STC | +| Model | Framework | Dataset | +|--------------------------------------------|-----------|---------| +| [CPM](nlp/dialogue_generation/cpm/pytorch) | PyTorch | STC | #### Language Modeling -| Model | Framework | Dataset | -|----------------------------------------------------------------------------|-------------------|--------------------| -| [BART](nlp/language_model/bart_fairseq/pytorch/README.md) | PyTorch (Fairseq) | RTE | -| [BERT NER](nlp/ner/bert/pytorch/README.md) | PyTorch | CoNLL-2003 | -| [BERT Pretraining](nlp/language_model/bert/pytorch/README.md) | PyTorch | MLCommon Wikipedia | -| [BERT Pretraining](nlp/language_model/bert/paddlepaddle/README.md) | PaddlePaddle | MNLI | -| [BERT Pretraining](nlp/language_model/bert/tensorflow/base/README.md) | TensorFlow | MNLI | -| [BERT Pretraining](nlp/language_model/bert/mindspore/README.md) | MindSpore | SQuAD | -| [BERT Text Classification](nlp/text_classification/bert/pytorch/README.md) | PyTorch | GLUE | -| [BERT Text Summerization](nlp/text_summarisation/bert/pytorch/README.md) | PyTorch | cnn_dailymail | -| [BERT Question Answering](nlp/question_answering/bert/pytorch/README.md) | PyTorch | SQuAD | -| [GPT2-Medium-EN](nlp/llm/gpt2-medium-en/paddlepaddle/README.md) | PaddlePaddle | SST-2 | -| [RoBERTa](nlp/language_model/roberta_fairseq/pytorch/README.md) | PyTorch (Fairseq) | RTE | -| [XLNet](nlp/language_model/xlnet/paddlepaddle/README.md) | PaddlePaddle | SST-2 | +| Model | Framework | Dataset | +|------------------------------------------------------------------|-------------------|--------------------| +| [BART](nlp/language_model/bart_fairseq/pytorch) | PyTorch (Fairseq) | RTE | +| [BERT NER](nlp/ner/bert/pytorch) | PyTorch | CoNLL-2003 | +| [BERT Pretraining](nlp/language_model/bert/pytorch) | PyTorch | MLCommon Wikipedia | +| [BERT Pretraining](nlp/language_model/bert/paddlepaddle) | PaddlePaddle | MNLI | +| [BERT Pretraining](nlp/language_model/bert/tensorflow/base) | TensorFlow | MNLI | +| [BERT Pretraining](nlp/language_model/bert/mindspore) | MindSpore | SQuAD | +| [BERT Text Classification](nlp/text_classification/bert/pytorch) | PyTorch | GLUE | +| [BERT Text Summerization](nlp/text_summarisation/bert/pytorch) | PyTorch | cnn_dailymail | +| [BERT Question Answering](nlp/question_answering/bert/pytorch) | PyTorch | SQuAD | +| [GPT2-Medium-EN](nlp/llm/gpt2-medium-en/paddlepaddle) | PaddlePaddle | SST-2 | +| [RoBERTa](nlp/language_model/roberta_fairseq/pytorch) | PyTorch (Fairseq) | RTE | +| [XLNet](nlp/language_model/xlnet/paddlepaddle) | PaddlePaddle | SST-2 | #### Text Correction -| Model | Framework | Dataset| -|-----------------------------------------------------------|--------------|--------| -| [Ernie](nlp/text_correction/ernie/paddlepaddle/README.md) | PaddlePaddle | corpus | +| Model | Framework | Dataset | +|-------------------------------------------------|--------------|---------| +| [ERNIE](nlp/text_correction/ernie/paddlepaddle) | PaddlePaddle | corpus | #### Translation -| Model | Framework | Dataset | -|--------------------------------------------------------------------------|-------------------|---------| -| [Convolutional](nlp/translation/convolutional_fairseq/pytorch/README.md) | PyTorch (Fairseq) | WMT14 | -| [T5](nlp/translation/t5/pytorch/README.md) | PyTorch | WMT14 | -| [Transformer](nlp/translation/transformer/paddlepaddle/README.md) | PaddlePaddle | WMT14 | -| [Transformer](nlp/translation/transformer_fairseq/pytorch/README.md) | PyTorch (Fairseq) | IWSLT14 | +| Model | Framework | Dataset | +|----------------------------------------------------------------|-------------------|---------| +| [Convolutional](nlp/translation/convolutional_fairseq/pytorch) | PyTorch (Fairseq) | WMT14 | +| [T5](nlp/translation/t5/pytorch) | PyTorch | WMT14 | +| [Transformer](nlp/translation/transformer/paddlepaddle) | PaddlePaddle | WMT14 | +| [Transformer](nlp/translation/transformer_fairseq/pytorch) | PyTorch (Fairseq) | IWSLT14 | ### Reinforcement Learning -| Model | Framework | Dataset | -|------------------------------------------------------------------------------|--------------|-------------| -| [DQN](reinforcement_learning/q-learning-networks/dqn/paddlepaddle/README.md) | PaddlePaddle | CartPole-v0 | +| Model | Framework | Dataset | +|--------------------------------------------------------------------|--------------|-------------| +| [DQN](reinforcement_learning/q-learning-networks/dqn/paddlepaddle) | PaddlePaddle | CartPole-v0 | ### Audio #### Speech Recognition -| Model | Framework | Dataset | -|----------------------------------------------------------------------------------------------------|-----------------|----------| -| [Conformer](audio/speech_recognition/conformer_wenet/pytorch/README.md) | PyTorch (WeNet) | AISHELL | -| [Efficient Conformer v2](audio/speech_recognition/efficient_conformer_v2_wenet/pytorch/README.md) | PyTorch (WeNet) | AISHELL | -| [PP-ASR-Conformer](audio/speech_recognition/conformer/paddlepaddle/README.md) | PaddlePaddle | AISHELL | -| [RNN-T](audio/speech_recognition/rnnt/pytorch/README.md) | PyTorch | LJSpeech | -| [Transformer](audio/speech_recognition/transformer_wenet/pytorch/README.md) | PyTorch (WeNet) | AISHELL | -| [U2++ Conformer](audio/speech_recognition/u2++_conformer_wenet/pytorch/README.md) | PyTorch (WeNet) | AISHELL | -| [Unified Conformer](audio/speech_recognition/unified_conformer_wenet/pytorch/README.md) | PyTorch (WeNet) | AISHELL | +| Model | Framework | Dataset | +|-----------------------------------------------------------------------------------------|-----------------|----------| +| [Conformer](audio/speech_recognition/conformer_wenet/pytorch) | PyTorch (WeNet) | AISHELL | +| [Efficient Conformer v2](audio/speech_recognition/efficient_conformer_v2_wenet/pytorch) | PyTorch (WeNet) | AISHELL | +| [PP-ASR-Conformer](audio/speech_recognition/conformer/paddlepaddle) | PaddlePaddle | AISHELL | +| [RNN-T](audio/speech_recognition/rnnt/pytorch) | PyTorch | LJSpeech | +| [Transformer](audio/speech_recognition/transformer_wenet/pytorch) | PyTorch (WeNet) | AISHELL | +| [U2++ Conformer](audio/speech_recognition/u2++_conformer_wenet/pytorch) | PyTorch (WeNet) | AISHELL | +| [Unified Conformer](audio/speech_recognition/unified_conformer_wenet/pytorch) | PyTorch (WeNet) | AISHELL | #### Speech Synthesis -| Model | Framework | Dataset | -|----------------------------------------------------------------------------------|--------------|-------------| -| [PP-TTS-FastSpeech2](audio/speech_synthesis/fastspeech2/paddlepaddle/README.md) | PaddlePaddle | CSMSC | -| [PP-TTS-HiFiGAN](audio/speech_synthesis/hifigan/paddlepaddle/README.md) | PaddlePaddle | CSMSC | -| [Tacotron2](audio/speech_synthesis/tacotron2/pytorch/README.md) | PyTorch | LJSpeech | -| [VQMIVC](audio/speech_synthesis/vqmivc/pytorch/README.md) | PyTorch | VCTK-Corpus | -| [WaveGlow](audio/speech_synthesis/waveglow/pytorch/README.md) | PyTorch | LJSpeech | +| Model | Framework | Dataset | +|-----------------------------------------------------------------------|--------------|-------------| +| [PP-TTS-FastSpeech2](audio/speech_synthesis/fastspeech2/paddlepaddle) | PaddlePaddle | CSMSC | +| [PP-TTS-HiFiGAN](audio/speech_synthesis/hifigan/paddlepaddle) | PaddlePaddle | CSMSC | +| [Tacotron2](audio/speech_synthesis/tacotron2/pytorch) | PyTorch | LJSpeech | +| [VQMIVC](audio/speech_synthesis/vqmivc/pytorch) | PyTorch | VCTK-Corpus | +| [WaveGlow](audio/speech_synthesis/waveglow/pytorch) | PyTorch | LJSpeech | ### Others +#### Graph Machine Learning + +| Model | Framework | Dataset | +|----------------------------------------------------------------------|-----------|--------------------| +| [Graph WaveNet](others/graph_machine_learning/graph_wavenet/pytorch) | PyTorch | METR-LA & PEMS-BAY | + #### Kolmogorov-Arnold Networks -| Model | Framework | Dataset | -|-----------------------------------------------------------------------|-----------|---------| -| [KAN](others/kolmogorov_arnold_networks/kan/pytorch/README.md) | PyTorch | - | +| Model | Framework | Dataset | +|------------------------------------------------------|-----------|---------| +| [KAN](others/kolmogorov_arnold_networks/kan/pytorch) | PyTorch | - | + +#### Model Pruning + +| Model | Framework | Dataset | +|-------------------------------------------------------------------|-----------|--------------| +| [Network Slimming](others/model_pruning/network-slimming/pytorch) | PyTorch | CIFAR-10/100 | #### Recommendation Systems -| Model | Framework | Dataset | -|------------------------------------------------------------------------------------|--------------|-----------------| -| [DeepFM](others/recommendation_systems/deepfm/paddlepaddle/README.md) | PaddlePaddle | Criteo_Terabyte | -| [DLRM](others/recommendation_systems/dlrm/pytorch/README.md) | PyTorch | Criteo_Terabyte | -| [DLRM](others/recommendation_systems/dlrm/paddlepaddle/README.md) | PaddlePaddle | Criteo_Terabyte | -| [FFM](others/recommendation_systems/ffm/paddlepaddle/README.md) | PaddlePaddle | Criteo_Terabyte | -| [NCF](others/recommendation_systems/ncf/pytorch/README.md) | PyTorch | movielens | -| [Wide&Deep](others/recommendation_systems/wide_deep/paddlepaddle/README.md) | PaddlePaddle | Criteo_Terabyte | -| [xDeepFM](others/recommendation_systems/xdeepfm/paddlepaddle/README.md) | PaddlePaddle | Criteo_Terabyte | +| Model | Framework | Dataset | +|-------------------------------------------------------------------|--------------|-----------------| +| [DeepFM](others/recommendation_systems/deepfm/paddlepaddle) | PaddlePaddle | Criteo_Terabyte | +| [DLRM](others/recommendation_systems/dlrm/pytorch) | PyTorch | Criteo_Terabyte | +| [DLRM](others/recommendation_systems/dlrm/paddlepaddle) | PaddlePaddle | Criteo_Terabyte | +| [FFM](others/recommendation_systems/ffm/paddlepaddle) | PaddlePaddle | Criteo_Terabyte | +| [NCF](others/recommendation_systems/ncf/pytorch) | PyTorch | movielens | +| [Wide&Deep](others/recommendation_systems/wide_deep/paddlepaddle) | PaddlePaddle | Criteo_Terabyte | +| [xDeepFM](others/recommendation_systems/xdeepfm/paddlepaddle) | PaddlePaddle | Criteo_Terabyte | -------- diff --git a/cv/traffic_forecast/README.md b/cv/traffic_forecast/README.md deleted file mode 100644 index eeebbf0bc94759766806e550efaacdf27cac91e7..0000000000000000000000000000000000000000 --- a/cv/traffic_forecast/README.md +++ /dev/null @@ -1 +0,0 @@ -# Traffic Forecase diff --git a/nlp/cloze_test/glm/pytorch/GLMForMultiTokenCloze/README.md b/nlp/cloze_test/glm/pytorch/GLMForMultiTokenCloze/README.md index 847b992680cf943f950d939b697c8d7f2a67292f..de238822adc88394cd212cb0a6ad147e5b8249b2 100644 --- a/nlp/cloze_test/glm/pytorch/GLMForMultiTokenCloze/README.md +++ b/nlp/cloze_test/glm/pytorch/GLMForMultiTokenCloze/README.md @@ -2,7 +2,16 @@ ## Model description -There have been various types of pretraining architectures including autoencoding models (e.g., BERT), autoregressive models (e.g., GPT), and encoder-decoder models (e.g., T5). However, none of the pretraining frameworks performs the best for all tasks of three main categories including natural language understanding (NLU), unconditional generation, and conditional generation. We propose a General Language Model (GLM) based on autoregressive blank infilling to address this challenge. GLM improves blank filling pretraining by adding 2D positional encodings and allowing an arbitrary order to predict spans, which results in performance gains over BERT and T5 on NLU tasks. Meanwhile, GLM can be pretrained for different types of tasks by varying the number and lengths of blanks. On a wide range of tasks across NLU, conditional and unconditional generation, GLM outperforms BERT, T5, and GPT given the same model sizes and data, and achieves the best performance from a single pretrained model with 1.25x parameters of BERT Large , demonstrating its generalizability to different downstream tasks. +There have been various types of pretraining architectures including autoencoding models (e.g., BERT), autoregressive +models (e.g., GPT), and encoder-decoder models (e.g., T5). However, none of the pretraining frameworks performs the best +for all tasks of three main categories including natural language understanding (NLU), unconditional generation, and +conditional generation. We propose a General Language Model (GLM) based on autoregressive blank infilling to address +this challenge. GLM improves blank filling pretraining by adding 2D positional encodings and allowing an arbitrary order +to predict spans, which results in performance gains over BERT and T5 on NLU tasks. Meanwhile, GLM can be pretrained for +different types of tasks by varying the number and lengths of blanks. On a wide range of tasks across NLU, conditional +and unconditional generation, GLM outperforms BERT, T5, and GPT given the same model sizes and data, and achieves the +best performance from a single pretrained model with 1.25x parameters of BERT Large , demonstrating its generalizability +to different downstream tasks. ## Step 1: Installing packages @@ -19,8 +28,7 @@ bash preparedata.sh /home/data/perf/glm ## Step 3: Preparing data prepare pretrained weights -download from [glm github](https://github.com/THUDM/GLM) -[model release page](https://mailstsinghuaeducn-my.sharepoint.com/personal/duzx16_mails_tsinghua_edu_cn/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fduzx16%5Fmails%5Ftsinghua%5Fedu%5Fcn%2FDocuments%2Fmodels&ga=1) download `glm-large-blank.tar.bz2` +Download `glm-large-blank.tar.bz2` from [Tsinghua-Cloud](https://cloud.tsinghua.edu.cn/d/13f5b03da9594e5490c4/). ```bash mkdir -p /home/data/perf/glm @@ -29,9 +37,8 @@ tar -jxvf glm-large-blank.tar.bz2 popd ``` - ## Step 4: Training - + ### Multiple GPUs on one machine ```bash @@ -40,10 +47,10 @@ bash run.sh ## Results on BI-V100 -| GPUs | Batch Size | FPS | Accuracy | -|------|------------| --- | ------------ | -| 1x8 | 8 | 9.43 | 0.81 | - +| GPUs | Batch Size | FPS | Accuracy | +|------|------------|------|----------| +| 1x8 | 8 | 9.43 | 0.81 | ## Reference -https://github.com/THUDM/GLM + +- [GLM](https://github.com/THUDM/GLM) diff --git a/nlp/dialogue_generation/cpm/pytorch/README.md b/nlp/dialogue_generation/cpm/pytorch/README.md index b708cf3eef472f72cbe89023027fd9b12e470ec6..289325d8e1c23e67f8e4100cbe6623fdb810d458 100644 --- a/nlp/dialogue_generation/cpm/pytorch/README.md +++ b/nlp/dialogue_generation/cpm/pytorch/README.md @@ -2,63 +2,69 @@ ## Model description -Pre-trained Language Models (PLMs) have proven to be beneficial for various downstream NLP tasks. Recently, GPT-3, with 175 billion parameters and 570GB training data, drew a lot of attention due to the capacity of few-shot (even zero-shot) learning. However, applying GPT-3 to address Chinese NLP tasks is still challenging, as the training corpus of GPT-3 is primarily English, and the parameters are not publicly available. In this technical report, we release the Chinese Pre-trained Language Model (CPM) with generative pre-training on large-scale Chinese training data. To the best of our knowledge, CPM, with 2.6 billion parameters and 100GB Chinese training data, is the largest Chinese pre-trained language model, which could facilitate several downstream Chinese NLP tasks, such as conversation, essay generation, cloze test, and language understanding. Extensive experiments demonstrate that CPM achieves strong performance on many NLP tasks in the settings of few-shot (even zero-shot) learning. The code and parameters are available at https://github.com/TsinghuaAI/CPM-Generate. +Pre-trained Language Models (PLMs) have proven to be beneficial for various downstream NLP tasks. Recently, GPT-3, with +175 billion parameters and 570GB training data, drew a lot of attention due to the capacity of few-shot (even zero-shot) +learning. However, applying GPT-3 to address Chinese NLP tasks is still challenging, as the training corpus of GPT-3 is +primarily English, and the parameters are not publicly available. In this technical report, we release the Chinese +Pre-trained Language Model (CPM) with generative pre-training on large-scale Chinese training data. To the best of our +knowledge, CPM, with 2.6 billion parameters and 100GB Chinese training data, is the largest Chinese pre-trained language +model, which could facilitate several downstream Chinese NLP tasks, such as conversation, essay generation, cloze test, +and language understanding. Extensive experiments demonstrate that CPM achieves strong performance on many NLP tasks in +the settings of few-shot (even zero-shot) learning. The code and parameters are available at +. ## Step 1: Installing packages ### Install packages in container ```shell -$ apt install -y numactl -or -$ yum install -y numactl +# Ubuntu +apt install -y numactl +# CentOS +yum install -y numactl ``` ## Step 2: Download dataset and model ```shell -$ pip install gdown -$ mkdir -p /home/data/perf/cpm -$ cd /home/data/perf/cpm -$ gdown -O "STC.json" --fuzzy https://drive.google.com/uc?id=19VyP6e7pS4pYed87yfvO2hAO7dd0dL9K +pip install gdown +mkdir -p /home/data/perf/cpm +cd /home/data/perf/cpm +gdown -O "STC.json" --fuzzy https://drive.google.com/uc?id=19VyP6e7pS4pYed87yfvO2hAO7dd0dL9K ``` -Download pretrained model file "model-v2.tar.gz": https://wudaoai.cn/model/download?resourceId=1420992356135514112&filename=CPM-1-2.6B-zh.tar.gz +Download pretrained model file "model-v2.tar.gz": ```shell -$ tar -xvf model-v2.tar.gz +tar -xvf model-v2.tar.gz ``` - - ## Step 3: Training ### On single GPU ```shell -$ cd modelzoo-benchmark/nlp/dialogue_generation/cpm/pytorch/base/ -$ python3 prepare.py --name iluvatar --data_dir /home/data/perf/cpm -$ bash run_training.sh --name iluvatar --config V100x1x8 --data_dir /home/data/perf/cpm +cd modelzoo-benchmark/nlp/dialogue_generation/cpm/pytorch/base/ +python3 prepare.py --name iluvatar --data_dir /home/data/perf/cpm +bash run_training.sh --name iluvatar --config V100x1x8 --data_dir /home/data/perf/cpm ``` ### On single GPU (AMP) ```shell -$ bash run_training.sh --name iluvatar --config V100x1x1 --data_dir /home/data/perf/cpm +bash run_training.sh --name iluvatar --config V100x1x1 --data_dir /home/data/perf/cpm ``` ## Results on BI-V100 -| GPUs | FPS | E2E | Accuracy | -|------| --- |----------| -------- | +| GPUs | FPS | E2E | Accuracy | +|------|--------|----------|----------| | 1x8 | 152.86 | 3558.36s | 0.91 | - -| Convergence criteria | Configuration (x denotes number of GPUs) | Performance | Accuracy | Power（W） | Scalability | Memory utilization（G） | Stability | -|----------------------|------------------------------------------|-------------|----------|------------|-------------|-------------------------|-----------| -| 0.91 | SDK V2.2,bs:128,8x,AMP | 357 | 0.91 | 156\*8 | 0.93 | 20.6\*8 | 1 | - - +| Convergence criteria | Configuration | Performance | Accuracy | Power (W) | Scalability | Memory utilization (G) | Stability | +|----------------------|------------------------|-------------|----------|-----------|-------------|------------------------|-----------| +| 0.91 | SDK V2.2,bs:128,8x,AMP | 357 | 0.91 | 156\*8 | 0.93 | 20.6\*8 | 1 | ## Reference -https://github.com/TsinghuaAI/CPM-1-Finetune \ No newline at end of file + +- [CPM-1-Finetune](https://github.com/TsinghuaAI/CPM-1-Finetune) diff --git a/nlp/language_model/bart_fairseq/pytorch/README.md b/nlp/language_model/bart_fairseq/pytorch/README.md index e87f30d52fae45e62adae58b52805be6d1e642ab..524cc3dccac33123479f714594295c67b5d19434 100644 --- a/nlp/language_model/bart_fairseq/pytorch/README.md +++ b/nlp/language_model/bart_fairseq/pytorch/README.md @@ -2,17 +2,14 @@ ## Model description -BART is sequence-to-sequence model trained with denoising as pretraining -objective. We show that this pretraining objective is more generic and -show that we can match RoBERTa results on SQuAD and GLUE and gain -state-of-the-art results on summarization (XSum, CNN dataset), -long form generative question answering (ELI5) and dialog response -genration (ConvAI2). +BART is sequence-to-sequence model trained with denoising as pretraining objective. We show that this pretraining +objective is more generic and show that we can match RoBERTa results on SQuAD and GLUE and gain state-of-the-art results +on summarization (XSum, CNN dataset), long form generative question answering (ELI5) and dialog response genration +(ConvAI2). ## Step 1: Installation -Bart model is using Fairseq toolbox. Before you run this model, -you need to setup Fairseq first. +Bart model is using Fairseq toolbox. Before you run this model, you need to setup Fairseq first. ```bash # Go to "toolbox/Fairseq" directory in root path @@ -52,9 +49,8 @@ python3 bart.py ## Results - | GPUs | QPS | Train Epochs | Accuracy | -| ------------ | -------- | -------------- | ---------- | +|------------|--------|--------------|----------| | BI-v100 x8 | 113.18 | 10 | 83.8 | ## Reference diff --git a/nlp/language_model/bert/mindspore/README.md b/nlp/language_model/bert/mindspore/README.md index ad49d268befb40bb420b3db0cca0ae282356b2b4..da434b22da93ca35355fc377fc0d9cfc5c015fa3 100644 --- a/nlp/language_model/bert/mindspore/README.md +++ b/nlp/language_model/bert/mindspore/README.md @@ -1,49 +1,76 @@ # BERT ## Model description -The BERT network was proposed by Google in 2018. The network has made a breakthrough in the field of NLP. The network uses pre-training to achieve a large network structure without modifying, and only by adding an output layer to achieve multiple text-based tasks in fine-tuning. The backbone code of BERT adopts the Encoder structure of Transformer. The attention mechanism is introduced to enable the output layer to capture high-latitude global semantic information. The pre-training uses denoising and self-encoding tasks, namely MLM(Masked Language Model) and NSP(Next Sentence Prediction). No need to label data, pre-training can be performed on massive text data, and only a small amount of data to fine-tuning downstream tasks to obtain good results. The pre-training plus fune-tuning mode created by BERT is widely adopted by subsequent NLP networks. -[Paper](https://arxiv.org/abs/1810.04805): Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova. [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding]((https://arxiv.org/abs/1810.04805)). arXiv preprint arXiv:1810.04805. +The BERT network was proposed by Google in 2018. The network has made a breakthrough in the field of NLP. The network +uses pre-training to achieve a large network structure without modifying, and only by adding an output layer to achieve +multiple text-based tasks in fine-tuning. The backbone code of BERT adopts the Encoder structure of Transformer. The +attention mechanism is introduced to enable the output layer to capture high-latitude global semantic information. The +pre-training uses denoising and self-encoding tasks, namely MLM(Masked Language Model) and NSP(Next Sentence +Prediction). No need to label data, pre-training can be performed on massive text data, and only a small amount of data +to fine-tuning downstream tasks to obtain good results. The pre-training plus fune-tuning mode created by BERT is widely +adopted by subsequent NLP networks. + +[Paper](https://arxiv.org/abs/1810.04805): Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova. [BERT: +Pre-training of Deep Bidirectional Transformers for Language Understanding]((https://arxiv.org/abs/1810.04805)). arXiv +preprint arXiv:1810.04805. + +[Paper](https://arxiv.org/abs/1909.00204): Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, +Jiashu Lin, Xin Jiang, Xiao Chen, Qun Liu. [NEZHA: Neural Contextualized Representation for Chinese Language +Understanding](https://arxiv.org/abs/1909.00204). arXiv preprint arXiv:1909.00204. -[Paper](https://arxiv.org/abs/1909.00204): Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen, Qun Liu. [NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://arxiv.org/abs/1909.00204). arXiv preprint arXiv:1909.00204. ## Step 1: Installing -``` + +```sh pip3 install -r requirements.txt ``` + ## Step 2: Prepare Datasets -# 1. Download training dataset(.tf_record), eval dataset(.json), vocab.txt and checkpoint：bert_large_ascend_v130_enwiki_official_nlp_bs768_loss1.1.ckpt -``` + +1. Download training dataset(.tf_record), eval dataset(.json), vocab.txt and checkpoint：bert_large_ascend_v130_enwiki_official_nlp_bs768_loss1.1.ckpt + +```sh cd scripts mkdir -p squad ``` + Please [BERT](https://github.com/google-research/bert#pre-training-with-bert) download vocab.txt here - Create fine-tune dataset - - Download dataset for fine-tuning and evaluation such as Chinese Named Entity Recognition[CLUENER](https://github.com/CLUEbenchmark/CLUENER2020), Chinese sentences classification[TNEWS](https://github.com/CLUEbenchmark/CLUE), Chinese Named Entity Recognition[ChineseNER](https://github.com/zjy-ucas/ChineseNER), English question and answering[SQuAD v1.1 train dataset](https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json), [SQuAD v1.1 eval dataset](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json), package of English sentences classification[GLUE](https://gluebenchmark.com/tasks). - - We haven't provide the scripts to create tfrecord yet, while converting dataset files from JSON format to TFRECORD format, please refer to run_classifier.py or run_squad.py file in [BERT](https://github.com/google-research/bert) repository or the CLUE official repository [CLUE](https://github.com/CLUEbenchmark/CLUE/blob/master/baselines/models/bert/run_classifier.py) and [CLUENER](https://github.com/CLUEbenchmark/CLUENER2020/tree/master/tf_version) + - Download dataset for fine-tuning and evaluation such as Chinese Named Entity + Recognition[CLUENER](https://github.com/CLUEbenchmark/CLUENER2020), Chinese sentences + classification[TNEWS](https://github.com/CLUEbenchmark/CLUE), Chinese Named Entity + Recognition[ChineseNER](https://github.com/zjy-ucas/ChineseNER), English question and answering[SQuAD v1.1 train + dataset](https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json), [SQuAD v1.1 eval + dataset](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json), package of English sentences + classification[GLUE](https://gluebenchmark.com/tasks). + - We haven't provide the scripts to create tfrecord yet, while converting dataset files from JSON format to TFRECORD + format, please refer to run_classifier.py or run_squad.py file in [BERT](https://github.com/google-research/bert) + repository or the CLUE official repository + [CLUE](https://github.com/CLUEbenchmark/CLUE/blob/master/baselines/models/bert/run_classifier.py) and + [CLUENER](https://github.com/CLUEbenchmark/CLUENER2020/tree/master/tf_version) -# [Pretrained models](#contents) +### Pretrained models We have provided several kinds of pretrained checkpoint. -- [Bert-base-zh](https://download.mindspore.cn/model_zoo/r1.3/bert_base_ascend_v130_zhwiki_official_nlp_bs256_acc91.72_recall95.06_F1score93.36/), trained on zh-wiki datasets with 128 length. -- [Bert-large-zh](https://download.mindspore.cn/model_zoo/r1.3/bert_large_ascend_v130_zhwiki_official_nlp_bs3072_loss0.8/), trained on zh-wiki datasets with 128 length. -- [Bert-large-en](https://download.mindspore.cn/model_zoo/r1.3/bert_large_ascend_v130_enwiki_official_nlp_bs768_loss1.1/), tarined on en-wiki datasets with 512 length. +- [Bert-base-zh](https://download.mindspore.cn/model_zoo/r1.3/bert_base_ascend_v130_zhwiki_official_nlp_bs256_acc91.72_recall95.06_F1score93.36/), + trained on zh-wiki datasets with 128 length. +- [Bert-large-zh](https://download.mindspore.cn/model_zoo/r1.3/bert_large_ascend_v130_zhwiki_official_nlp_bs3072_loss0.8/), + trained on zh-wiki datasets with 128 length. +- [Bert-large-en](https://download.mindspore.cn/model_zoo/r1.3/bert_large_ascend_v130_enwiki_official_nlp_bs768_loss1.1/), + tarined on en-wiki datasets with 512 length. ## Step 3: Training -``` + +```sh bash scripts/run_squad_gpu_distribute.sh 8 ``` -### [Evaluation result] -## Results on BI-V100 -| GPUs | per step time | exact_match | F1 | -|------|-------------- |---------------|------| -| 1*8 | 1.898s | 71.9678 |81.422| -### 性能数据：NV -## Results on NV-V100s +### [Evaluation result] -| GPUs | per step time | exact_match | F1 | -|------|-------------- |---------------|------| -| 1*8 | 1.877s | 71.9678 |81.422| +## Results +| GPUs | per step time | exact_match | F1 | +|------|---------------|-------------|--------| +| 1x8 | 1.898s | 71.9678 | 81.422 | diff --git a/nlp/language_model/bert/paddlepaddle/README.md b/nlp/language_model/bert/paddlepaddle/README.md index 3359131bf885281464f674bec3061bc952d58b8e..001984d4c2840f7e01ed53e26f26f7022c2fab69 100644 --- a/nlp/language_model/bert/paddlepaddle/README.md +++ b/nlp/language_model/bert/paddlepaddle/README.md @@ -2,7 +2,13 @@ ## Model description -BERT, or Bidirectional Encoder Representations from Transformers, improves upon standard Transformers by removing the unidirectionality constraint by using a masked language model (MLM) pre-training objective. The masked language model randomly masks some of the tokens from the input, and the objective is to predict the original vocabulary id of the masked word based only on its context. Unlike left-to-right language model pre-training, the MLM objective enables the representation to fuse the left and the right context, which allows us to pre-train a deep bidirectional Transformer. In addition to the masked language model, BERT uses a next sentence prediction task that jointly pre-trains text-pair representations. +BERT, or Bidirectional Encoder Representations from Transformers, improves upon standard Transformers by removing the +unidirectionality constraint by using a masked language model (MLM) pre-training objective. The masked language model +randomly masks some of the tokens from the input, and the objective is to predict the original vocabulary id of the +masked word based only on its context. Unlike left-to-right language model pre-training, the MLM objective enables the +representation to fuse the left and the right context, which allows us to pre-train a deep bidirectional Transformer. In +addition to the masked language model, BERT uses a next sentence prediction task that jointly pre-trains text-pair +representations. ## Step 1: Installing @@ -16,17 +22,15 @@ pip3 install -r requirements.txt Download the [MNLI Dataset](http://www.nyu.edu/projects/bowman/multinli/) - -## Step 3: Run BERT +## Step 3: Training ```bash # Make sure your dataset path is the same as above bash train_bert.sh ``` - ## Results on BI-V100 -| GPU | FP32 | -| ----------- | ------------------------------------ | -| 1 card | Acc@1=84.5,FPS=5.1,BatchSize=32 | +| GPU | FP32 | +|--------|---------------------------------| +| 1 card | Acc@1=84.5,FPS=5.1,BatchSize=32 | diff --git a/nlp/language_model/bert/tensorflow/base/README.md b/nlp/language_model/bert/tensorflow/base/README.md index 42ae860eb7efefe402a4340c77fcf4a0b3d281ab..27c6ab51a23f19270fcbddc4c11e6f93ba754a54 100644 --- a/nlp/language_model/bert/tensorflow/base/README.md +++ b/nlp/language_model/bert/tensorflow/base/README.md @@ -1,7 +1,14 @@ - # BERT Pretraining +# BERT Pretraining ## Model description -BERT, or Bidirectional Encoder Representations from Transformers, improves upon standard Transformers by removing the unidirectionality constraint by using a masked language model (MLM) pre-training objective. The masked language model randomly masks some of the tokens from the input, and the objective is to predict the original vocabulary id of the masked word based only on its context. Unlike left-to-right language model pre-training, the MLM objective enables the representation to fuse the left and the right context, which allows us to pre-train a deep bidirectional Transformer. In addition to the masked language model, BERT uses a next sentence prediction task that jointly pre-trains text-pair representations. + +BERT, or Bidirectional Encoder Representations from Transformers, improves upon standard Transformers by removing the +unidirectionality constraint by using a masked language model (MLM) pre-training objective. The masked language model +randomly masks some of the tokens from the input, and the objective is to predict the original vocabulary id of the +masked word based only on its context. Unlike left-to-right language model pre-training, the MLM objective enables the +representation to fuse the left and the right context, which allows us to pre-train a deep bidirectional Transformer. In +addition to the masked language model, BERT uses a next sentence prediction task that jointly pre-trains text-pair +representations. ## Prepare @@ -19,10 +26,11 @@ export LD_LIBRARY_PATH=/usr/local/lib/:$LD_LIBRARY_PATH ### Download datasets -This [Google Drive location](https://drive.google.com/drive/folders/1oQF4diVHNPCclykwdvQJw8n_VIWwV0PT) contains the following. +This [Google Drive location](https://drive.google.com/drive/folders/1oQF4diVHNPCclykwdvQJw8n_VIWwV0PT) contains the +following. You need to download tf1_ckpt folde , vocab.txt and bert_config.json into one file named bert_pretrain_ckpt_tf -``` +```sh bert_pretrain_ckpt_tf: contains checkpoint files model.ckpt-28252.data-00000-of-00001 model.ckpt-28252.index @@ -30,11 +38,11 @@ bert_pretrain_ckpt_tf: contains checkpoint files vocab.txt bert_config.json ``` + [Download and preprocess datasets](https://github.com/mlcommons/training/tree/master/language_model/tensorflow/bert#generate-the-tfrecords-for-wiki-dataset) You need to make a file named bert_pretrain_tf_records and store the results above. tips: you can git clone this repo in other place ,we need the bert_pretrain_tf_records results here. - ## Training ### Training on single card @@ -48,6 +56,7 @@ bash run_1card_FPS.sh --input_files_dir=/path/to/bert_pretrain_tf_records/train_ ``` ### Training on mutil-cards + ```shell export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 export IX_NUM_CUDA_VISIBLE_DEVICES=8 @@ -57,9 +66,9 @@ bash run_multi_card_FPS.sh --input_files_dir=/path/to/bert_pretrain_tf_records/t --train_batch_size=6 \ --bert_config_file=/path/to/bert_pretrain_ckpt_tf/bert_config.json ``` - + ## Result -| | acc | fps | -| --- | --- | --- | -| multi_card | 0.424126 | 0.267241| \ No newline at end of file +| GPUs | acc | fps | +|------------|----------|----------| +| multi_card | 0.424126 | 0.267241 | \ No newline at end of file diff --git a/nlp/language_model/roberta_fairseq/pytorch/README.md b/nlp/language_model/roberta_fairseq/pytorch/README.md index b28c5ef937c9fda1ef387477d7504d4cbc24a91f..b4f2a1945395d4bca2a11563512a234c5268e1df 100644 --- a/nlp/language_model/roberta_fairseq/pytorch/README.md +++ b/nlp/language_model/roberta_fairseq/pytorch/README.md @@ -1,14 +1,14 @@ # RoBERTa ## Model description -RoBERTa iterates on BERT's pretraining procedure, including training the model -longer, with bigger batches over more data; removing the next sentence prediction -objective; training on longer sequences; and dynamically changing the masking -pattern applied to the training data. + +RoBERTa iterates on BERT's pretraining procedure, including training the model longer, with bigger batches over more +data; removing the next sentence prediction objective; training on longer sequences; and dynamically changing the +masking pattern applied to the training data. ## Step 1: Installation -RoBERTa model is using Fairseq toolbox. Before you run this model, -you need to setup Fairseq first. + +RoBERTa model is using Fairseq toolbox. Before you run this model, you need to setup Fairseq first. ```bash # Go to "toolbox/Fairseq" directory in root path @@ -48,9 +48,10 @@ python3 roberta.py ## Results -| GPUs | QPS | Train Epochs | Accuracy | -|------|-----|--------------|------| -| BI-v100 x8 | 207.5 | 10 | 86.3 | +| GPUs | QPS | Train Epochs | Accuracy | +|------------|-------|--------------|----------| +| BI-v100 x8 | 207.5 | 10 | 86.3 | ## Reference + - [Fairseq](https://github.com/facebookresearch/fairseq/tree/v0.10.2) diff --git a/nlp/language_model/xlnet/paddlepaddle/README.md b/nlp/language_model/xlnet/paddlepaddle/README.md index a9d8f4435fe2ab5c744dfa271d4dc6ed58fa56c9..0e34f31130578f05f00fe992812ffd16ff9c7f22 100644 --- a/nlp/language_model/xlnet/paddlepaddle/README.md +++ b/nlp/language_model/xlnet/paddlepaddle/README.md @@ -2,7 +2,13 @@ ## Model description -[XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) is an unsupervised auto-regressive pre-trained language model. Different from traditional one-way auto-regressive models, XLNet performs language modeling by maximizing the expectation of all permutations of the input sequence, which allows it to pay attention to contextual information at the same time. In addition, XLNet integrates the [Transformer-XL](https://arxiv.org/abs/1901.02860) model in the pre-training stage, the Segment Recurrent Mechanism and Relative Positional Encoding mechanism in Transformer-XL can support XLNet to accept longer input sequences, which makes XLNet have excellent performance in language tasks with long text sequences. +[XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) is an +unsupervised auto-regressive pre-trained language model. Different from traditional one-way auto-regressive models, +XLNet performs language modeling by maximizing the expectation of all permutations of the input sequence, which allows +it to pay attention to contextual information at the same time. In addition, XLNet integrates the +[Transformer-XL](https://arxiv.org/abs/1901.02860) model in the pre-training stage, the Segment Recurrent Mechanism and +Relative Positional Encoding mechanism in Transformer-XL can support XLNet to accept longer input sequences, which makes +XLNet have excellent performance in language tasks with long text sequences. ## Step 1: Installation @@ -14,7 +20,8 @@ pip3 install paddlenlp==2.4.1 ### Step 2: Preparing datasets -The dataset included in the GLUE evaluation task has been provided in the form of API in PaddleNLP, no preparation is required in advance. It will be automatically downloaded when executing using `run_glue.py`. +The dataset included in the GLUE evaluation task has been provided in the form of API in PaddleNLP, no preparation is +required in advance. It will be automatically downloaded when executing using `run_glue.py`. ### Step 3: Training @@ -35,11 +42,15 @@ python3 -m paddle.distributed.launch --gpus "0,1,2,3,4,5,6,7" ./run_glue.py \ ``` The parameters are explained as follows: -- `model_name_or_path` indicates a model with a specific configuration, corresponding to its pre-trained model and the tokenizer used in pre-training. If the model-related content is saved locally, the corresponding directory address can also be provided here. + +- `model_name_or_path` indicates a model with a specific configuration, corresponding to its pre-trained model and the + tokenizer used in pre-training. If the model-related content is saved locally, the corresponding directory address can + also be provided here. - `task_name` indicates the task of Fine-tuning. - `max_seq_length` indicates the maximum sentence length, beyond which it will be truncated. - `batch_size` represents the number of samples per card per iteration. -- `learning_rate` indicates the size of the basic learning rate, which is multiplied with the value generated by the learning rate scheduler as the current learning rate. +- `learning_rate` indicates the size of the basic learning rate, which is multiplied with the value generated by the + learning rate scheduler as the current learning rate. - `num_train_epochs` indicates the number of training rounds. - `logging_steps` indicates the log printing interval. - `save_steps` indicates the model saving and evaluation interval. @@ -47,11 +58,11 @@ The parameters are explained as follows: ## Results -| GPUs | FPS | ACC | -|:-----:|:----------------------------:|:------------------:| -| BI-V100 x8 | 743.7 | 0.9450 | +| GPUs | FPS | ACC | +|------------|-------|--------| +| BI-V100 x8 | 743.7 | 0.9450 | ## Reference -- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) -- [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/xlnet +- [XLNet](https://arxiv.org/abs/1906.08237) +- [PaddleNLP]( diff --git a/nlp/llm/gpt2-medium-en/paddlepaddle/README.md b/nlp/llm/gpt2-medium-en/paddlepaddle/README.md index c276b3a98ff999213aa379b2bb0f8dbe123a10a4..cd292f3b11f8031c5bfea0c0879ba8867f5dd605 100644 --- a/nlp/llm/gpt2-medium-en/paddlepaddle/README.md +++ b/nlp/llm/gpt2-medium-en/paddlepaddle/README.md @@ -1,7 +1,10 @@ # GPT2-Medium-EN ## Model introduction -GPT-[2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)/[3] is a language generation model obtained by pre-training on a large-scale unlabeled text corpus using the [Transformer](https://arxiv.org/abs/1706.03762) decoder as the basic component of the network. + +GPT-[2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)/[3] is a +language generation model obtained by pre-training on a large-scale unlabeled text corpus using the +[Transformer](https://arxiv.org/abs/1706.03762) decoder as the basic component of the network. ## Step 1: Installation @@ -13,14 +16,23 @@ pip3 install paddlenlp ``` ## Step 2: Preparing dataset -Take the SST-2 task in GLUE as an example, SST-2 (The Stanford Sentiment Treebank, Stanford Sentiment Treebank), a single sentence classification task, contains human annotations of sentences in movie reviews and their emotions. This task is to give the emotion of the sentence, the category is divided into two types of positive emotion (positive, the sample label corresponds to 1) and negative emotion (negative, the sample label corresponds to 0), and only sentence-level labels are used. That is, this task is also a binary classification task, which is divided into positive and negative emotions for the sentence level.Number of samples: 67,350 training sets, 873 development sets, and 1,821 test sets. Task: Sentiment Classification, Binary Classification of Positive and Negative Sentiments. -``` +Take the SST-2 task in GLUE as an example, SST-2 (The Stanford Sentiment Treebank, Stanford Sentiment Treebank), a +single sentence classification task, contains human annotations of sentences in movie reviews and their emotions. This +task is to give the emotion of the sentence, the category is divided into two types of positive emotion (positive, the +sample label corresponds to 1) and negative emotion (negative, the sample label corresponds to 0), and only +sentence-level labels are used. That is, this task is also a binary classification task, which is divided into positive +and negative emotions for the sentence level.Number of samples: 67,350 training sets, 873 development sets, and 1,821 +test sets. Task: Sentiment Classification, Binary Classification of Positive and Negative Sentiments. + +```sh wget https://dl.fbaipublicfiles.com/glue/data/SST-2.zip makdir -p dataset unzip SST-2.zip -d dataset ``` + The SST-2 dataset path structure should look like: + ```bash dataset/SST-2 ├── original @@ -76,22 +88,30 @@ python3 -m paddle.distributed.launch --gpus "0,1,2,3,4,5,6,7" run_glue.py \ ``` The parameters in the configuration file are explained as follows： + - `model_type` Indicates the model type。 -- `model_name_or_path` Indicates a model with a specific configuration, corresponding to its pre-trained model and the tokenizer used for pre-training. If the relevant content of the model is saved locally, the corresponding directory address can also be provided here. +- `model_name_or_path` Indicates a model with a specific configuration, corresponding to its pre-trained model and the + tokenizer used for pre-training. If the relevant content of the model is saved locally, the corresponding directory + address can also be provided here. - `task_name` Indicates the task of Fine-tuning. - `max_seq_length` Indicates the maximum sentence length beyond which it will be truncated. - `batch_size` Indicates the number of samples on each card per iteration. -- `learning_rate` Indicates the size of the basic learning rate, which will be multiplied by the value generated by the learning rate scheduler as the current learning rate. +- `learning_rate` Indicates the size of the basic learning rate, which will be multiplied by the value generated by the + learning rate scheduler as the current learning rate. - `num_train_epochs` Indicates the number of training rounds. - `logging_steps` Indicates the log printing interval. - `save_steps` Indicates the model save and evaluation interval. - `output_dir` Indicates the path to save the model. -- `device` Indicates the equipment used for training, 'gpu' means to use GPU, 'xpu' means to use Baidu Kunlun card, 'cpu' means to use CPU, 'npu' means to use Huawei Ascend card. +- `device` Indicates the equipment used for training, 'gpu' means to use GPU, 'xpu' means to use Baidu Kunlun card, + 'cpu' means to use CPU, 'npu' means to use Huawei Ascend card. - `use_amp` Indicates whether automatic mixed-precision training is enabled. ## Step 4: Model Evaluation -1. Add the `run_eval_sst2.py` file and put it in the `PaddleNLP/model_zoo/gpt` folder, `run_eval_sst2.py` is modified based on the `PaddleNLP/model_zoo/gpt/run_eval.py` + +1. Add the `run_eval_sst2.py` file and put it in the `PaddleNLP/model_zoo/gpt` folder, `run_eval_sst2.py` is modified + based on the `PaddleNLP/model_zoo/gpt/run_eval.py` 2. execute the following command to evaluate: + ```shell python3 run_eval_sst2.py --model_name gpt2-medium-en \ --eval_path dataset/SST-2/dev.tsv \ @@ -100,10 +120,13 @@ python3 run_eval_sst2.py --model_name gpt2-medium-en \ --batch_size 1 \ --device gpu ``` + ## Results -| GPUs | FPS | Accuracy | -|-----------|------------|----------| -| BI-V100 | 221 | 0.92087 | +| GPUs | FPS | Accuracy | +|---------|-----|----------| +| BI-V100 | 221 | 0.92087 | + ## Reference + - [PaddleNlp](https://github.com/PaddlePaddle/PaddleNLP) diff --git a/nlp/ner/bert/pytorch/README.md b/nlp/ner/bert/pytorch/README.md index b05c0c55b2b78bfd2d79af8fd3a6d3e2b3b5fb17..0889f2b912be87b1ce4e34e2927047b5dc576964 100644 --- a/nlp/ner/bert/pytorch/README.md +++ b/nlp/ner/bert/pytorch/README.md @@ -25,10 +25,9 @@ git lfs install git clone https://huggingface.co/bert-base-uncased ``` - ## Step 3: Training -**Make sure you've got "bert-base-uncased" ready in ./bert-base-uncased** +> Make sure you've got "bert-base-uncased" ready in ./bert-base-uncased. ### On single GPU @@ -45,12 +44,14 @@ bash run.sh ## e.g. --dataset_name /path/to/conll2003 bash run_dist.sh ``` + ## Results on BI-V100 -| GPUs | Samples/s | Loss | -|------|------|----| -| 1x1 | 100 | 0.0696 | -| 1x8 | 252 | 0.0688 | +| GPUs | Samples/s | Loss | +|------|-----------|--------| +| 1x1 | 100 | 0.0696 | +| 1x8 | 252 | 0.0688 | ## Reference -https://github.com/huggingface/ + +- [bert-base-uncased](https://huggingface.co/bert-base-uncased) diff --git a/nlp/question_answering/bert/pytorch/README.md b/nlp/question_answering/bert/pytorch/README.md index 254e1c8d7409615709a1477ef806b39f23e6f112..3325b91adf2dc5ae1fbd734ca79091068c8a0070 100644 --- a/nlp/question_answering/bert/pytorch/README.md +++ b/nlp/question_answering/bert/pytorch/README.md @@ -27,7 +27,7 @@ git clone https://huggingface.co/bert-base-uncased ## Step 3: Training -**Make sure you've got "bert-base-uncased" ready in ./bert-base-uncased** +> Make sure you've got "bert-base-uncased" ready in ./bert-base-uncased ### On single GPU @@ -44,12 +44,14 @@ bash run.sh ## e.g. --dataset_name /path/to/squad bash run_dist.sh ``` -## Results on BI-V100 -| GPUs | Samples/s | F1 | -|------|-----------|--------| -| 1x1 | 128.86 | 87 | -| 1x8 | 208.6 | 78.69 | +## Results + +| GPUs | Samples/s | F1 | +|------|-----------|-------| +| 1x1 | 128.86 | 87 | +| 1x8 | 208.6 | 78.69 | ## Reference -https://github.com/huggingface/ + +- [bert-base-uncased](https://huggingface.co/bert-base-uncased) diff --git a/nlp/text_classification/bert/pytorch/README.md b/nlp/text_classification/bert/pytorch/README.md index f3b8daa758a0425093f9c0bd51996c08e255d315..f099d291faad369bc1d94f050d74aa547ebdf270 100644 --- a/nlp/text_classification/bert/pytorch/README.md +++ b/nlp/text_classification/bert/pytorch/README.md @@ -15,7 +15,6 @@ pip3 install -r requirements.txt ```bash # Get "bert-base-uncased" from [Huggingface](https://huggingface.co/bert-base-uncased) - ## Install lfs wget https://packagecloud.io/github/git-lfs/packages/el/7/git-lfs-2.13.2-1.el7.x86_64.rpm/download -O lfs.rpm rpm -ivh lfs.rpm @@ -27,7 +26,7 @@ git clone https://huggingface.co/bert-base-uncased ## Step 3: Training -**Make sure you've got "bert-base-uncased" ready in ./bert-base-uncased** +> Make sure you've got "bert-base-uncased" ready in ./bert-base-uncased ### On single GPU @@ -40,7 +39,8 @@ bash train.sh ```bash bash train_dist.sh ``` -## Results on BI-V100 + +## Results | GPUs | Samples/s | Loss | |------|-----------|------| @@ -48,4 +48,5 @@ bash train_dist.sh | 1x8 | 322.74 | 0.71 | ## Reference -https://github.com/huggingface/ + +- [bert-base-uncased](https://huggingface.co/bert-base-uncased) diff --git a/nlp/text_correction/ernie/paddlepaddle/README.md b/nlp/text_correction/ernie/paddlepaddle/README.md index 1cb6afd65fc82ccec9ade9d949e9d602b1727f07..c705aca9122c11074ad3c0c479eda3f7bd7f4397 100644 --- a/nlp/text_correction/ernie/paddlepaddle/README.md +++ b/nlp/text_correction/ernie/paddlepaddle/README.md @@ -1,17 +1,27 @@ -# Ernie +# ERNIE +## Introduction + +ERNIE (Enhanced Representation through Knowledge Integration) is a family of large-scale pre-trained language models +developed by Baidu. ERNIE is designed to improve on traditional language models by incorporating knowledge from various +sources, such as structured knowledge graphs, and by integrating multiple linguistic features, including syntax, +semantics, and common sense. The model achieves this by using a knowledge-enhanced pre-training approach, which helps +ERNIE better understand and generate more accurate and contextually aware language representations. ## Step 1: Installing -``` + +```sh git clone https://github.com/PaddlePaddle/PaddleNLP.git ``` -``` + +```sh cd PaddleNLP pip3 install -r requirements.txt ``` ## Step 2: Training -``` + +```sh cd examples/text_correction/ernie-csc pip3 install -r requirements.txt @@ -26,4 +36,5 @@ python3 -u train.py --batch_size 32 --logging_steps 100 --epochs 10 --learning_r ``` ## Reference -- [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP) \ No newline at end of file + +- [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP) diff --git a/nlp/text_summarisation/bert/pytorch/README.md b/nlp/text_summarisation/bert/pytorch/README.md index 6bf4fa7b57ef135dec87e07f463f91256d1d2e3d..d8bc7f226a1db5b5df67713f1c976e0fdc47e4e5 100644 --- a/nlp/text_summarisation/bert/pytorch/README.md +++ b/nlp/text_summarisation/bert/pytorch/README.md @@ -24,12 +24,10 @@ bash train.sh ```shell bash train_dist.sh ``` -## Results on BI-V100 + +## Results | GPUs | Samples/s | Loss | |------|-----------|--------| -| 1x1 | 16.71 | 1.8038 | -| 1x8 | 117.576 | 1.8288 | - -## Reference -https://github.com/huggingface/ +| 1x1 | 16.71 | 1.8038 | +| 1x8 | 117.576 | 1.8288 | diff --git a/nlp/translation/convolutional_fairseq/pytorch/README.md b/nlp/translation/convolutional_fairseq/pytorch/README.md index e9865e0b9439bac039dc0188ee38034139b9b873..9af189f6383014848d84af8d8b586036d0b0a7ce 100644 --- a/nlp/translation/convolutional_fairseq/pytorch/README.md +++ b/nlp/translation/convolutional_fairseq/pytorch/README.md @@ -1,11 +1,12 @@ # Convolutional ## Model description -The following instructions can be used to train a Convolutional translation model on the WMT English to German dataset. + +The following instructions can be used to train a Convolutional translation model on the WMT English to German dataset. ## Step 1: Installation -Convolutional model is using Fairseq toolbox. Before you run this model, -you need to setup Fairseq first. + +Convolutional model is using Fairseq toolbox. Before you run this model, you need to setup Fairseq first. ```bash # Go to "toolbox/Fairseq" directory in root path @@ -53,9 +54,10 @@ fairseq-generate data-bin/wmt17_en_de \ ## Results -| GPUs | QPS | Train Epochs | Evaluate_Bleu | -|------|-----|--------------|------------| -| BI-v100 x8 | 1650.49 | 100 | 25.55 | +| GPUs | QPS | Train Epochs | Evaluate_Bleu | +|------------|---------|--------------|---------------| +| BI-v100 x8 | 1650.49 | 100 | 25.55 | ## Reference -- [Fairseq](https://github.com/facebookresearch/fairseq/tree/v0.10.2) \ No newline at end of file + +- [Fairseq](https://github.com/facebookresearch/fairseq/tree/v0.10.2) diff --git a/nlp/translation/t5/pytorch/README.md b/nlp/translation/t5/pytorch/README.md index 4114b3de2d2a059a027f46eb279e9833279e616f..a6ed40f37830e3e144116ce95945e4e858b4232c 100644 --- a/nlp/translation/t5/pytorch/README.md +++ b/nlp/translation/t5/pytorch/README.md @@ -1,8 +1,11 @@ -# T5 +# T5 ## Model description -T5, or Text-to-Text Transfer Transformer, is a Transformer based architecture that uses a text-to-text approach. Every task – including translation, question answering, and classification – is cast as feeding the model text as input and training it to generate some target text. This allows for the use of the same model, loss function, hyperparameters, etc. across our diverse set of tasks. +T5, or Text-to-Text Transfer Transformer, is a Transformer based architecture that uses a text-to-text approach. Every +task – including translation, question answering, and classification – is cast as feeding the model text as input and +training it to generate some target text. This allows for the use of the same model, loss function, hyperparameters, +etc. across our diverse set of tasks. ## Step 1: Installing packages @@ -32,16 +35,18 @@ bash examples_ix/train_t5_small_dist_torch.sh ``` ### Multiple GPUs on one machine (AMP) + ```shell bash examples_ix/train_t5_small_amp_dist_torch.sh ``` ## Results on BI-V100 -| GUSs | Samples/s | Loss | -|------|------|----| -| 1x1 | 339 | 1.18 | -| 1x8 | 2488 | 1.18 | +| GUSs | Samples/s | Loss | +|------|-----------|------| +| 1x1 | 339 | 1.18 | +| 1x8 | 2488 | 1.18 | ## Reference -https://github.com/huggingface/ \ No newline at end of file + +- [t5-small](https://huggingface.co/google-t5/t5-small) diff --git a/nlp/translation/transformer/paddlepaddle/README.md b/nlp/translation/transformer/paddlepaddle/README.md index f8f0898f10f1e20e6f5dd6811937edb42a68c420..0b6b89db04352af1144ee6f8b1f4dbedad6cf9c8 100644 --- a/nlp/translation/transformer/paddlepaddle/README.md +++ b/nlp/translation/transformer/paddlepaddle/README.md @@ -1,19 +1,31 @@ # Transformer +## Introduction + +The Transformer model, introduced in 2017 by Vaswani et al., revolutionized natural language processing (NLP). Unlike +traditional models like RNNs or LSTMs, the Transformer relies entirely on self-attention mechanisms to process input +data in parallel, rather than sequentially. This allows it to capture long-range dependencies more effectively and scale +efficiently to large datasets. The model consists of an encoder-decoder architecture, where both components use +multi-head attention and position-wise feed-forward networks. Transformers have become the foundation for many +state-of-the-art models like BERT, GPT, and T5, driving advancements in machine translation, text generation, and other +NLP tasks. ## Step 1: Installing -``` + +```sh git clone https://github.com/PaddlePaddle/PaddleNLP.git ``` -``` +```sh cd PaddleNLP pip3 install -r requirements.txt ``` ## Step 2: Training + The training is use AMP model. -``` + +```sh cd PaddleNLP/examples/machine_translation/transformer export FLAGS_cudnn_exhaustive_search=True export FLAGS_cudnn_batchnorm_spatial_persistent=True @@ -23,4 +35,5 @@ python3 -u -m paddle.distributed.launch --gpus=0,1,2,3,4,5,6,7 train.py --config ``` ## Reference -- [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP) \ No newline at end of file + +- [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP) diff --git a/nlp/translation/transformer_fairseq/pytorch/README.md b/nlp/translation/transformer_fairseq/pytorch/README.md index 90b41101e82d6d0f3ffa157790ec4fd26d9683c2..4298270edf5fdeb2954b183f7806a096847cb65a 100644 --- a/nlp/translation/transformer_fairseq/pytorch/README.md +++ b/nlp/translation/transformer_fairseq/pytorch/README.md @@ -1,17 +1,19 @@ # Transformer ## Model description + The following instructions can be used to train a Transformer model on the IWSLT'14 German to English dataset. ## Step 1: Installation -Transformer model is using Fairseq toolbox. Before you run this model, -you need to setup Fairseq first. + +Transformer model is using Fairseq toolbox. Before you run this model, you need to setup Fairseq first. ```bash # Go to "toolbox/Fairseq" directory in root path cd ../../../../toolbox/Fairseq/ bash install_toolbox_fairseq.sh ``` + ## Step 2: Preparing datasets ```bash @@ -57,9 +59,10 @@ fairseq-generate data-bin/iwslt14.tokenized.de-en \ ## Results -| GPUs | QPS | Train Epochs | Bleu | -|------|-----|--------------|------| -| BI-v100 x8 | 3204.78 | 100 | 35.07 | +| GPUs | QPS | Train Epochs | Bleu | +|------------|---------|--------------|-------| +| BI-v100 x8 | 3204.78 | 100 | 35.07 | ## Reference + - [Fairseq](https://github.com/facebookresearch/fairseq/tree/v0.10.2) diff --git a/cv/traffic_forecast/graph_wavenet/pytorch/LICENSE b/others/graph_machine_learning/graph_wavenet/pytorch/LICENSE similarity index 100% rename from cv/traffic_forecast/graph_wavenet/pytorch/LICENSE rename to others/graph_machine_learning/graph_wavenet/pytorch/LICENSE diff --git a/cv/traffic_forecast/graph_wavenet/pytorch/README.md 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a/reinforcement_learning/q-learning-networks/dqn/paddlepaddle/README.md b/reinforcement_learning/q-learning-networks/dqn/paddlepaddle/README.md index 938b5c49166b01f95d0d88890a6d95f81a0f7df2..d9423ebcda369233e6a4f73680aae81eed907bf9 100644 --- a/reinforcement_learning/q-learning-networks/dqn/paddlepaddle/README.md +++ b/reinforcement_learning/q-learning-networks/dqn/paddlepaddle/README.md @@ -1,9 +1,9 @@ # DQN ## Model description -The classic DQN algorithm in reinforcement learning is a value-based rather than a policy-based method. DQN does not learn a policy, but a critic. Critic does not directly take action, but evaluates the quality of the action. -+ Paper: DQN in [Human-level Control Through Deep Reinforcement Learning](http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html) +The classic DQN algorithm in reinforcement learning is a value-based rather than a policy-based method. DQN does not +learn a policy, but a critic. Critic does not directly take action, but evaluates the quality of the action. ## Step 1: Installation @@ -15,29 +15,29 @@ pip3 install matplotlib pip3 install urllib3==1.26.6 ``` - ## Step 2: Training ```bash # 1 GPU python3 train.py ``` + ## Step 3: Evaluating ```bash mv ../../../evaluate.py ./ python3 evaluate.py ``` + ## Result Performance of DQN playing CartPole-v0 -

-result -

-|GPUs|Reward| -|:---:|:---:| -|BI-V100|200.0| +| GPUs | Reward | +|---------|--------| +| BI-V100 | 200.0 | ## Reference -- [PARL](https://github.com/PaddlePaddle/PARL) \ No newline at end of file + +- [PARL](https://github.com/PaddlePaddle/PARL) +- [paper](http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html)