clxia12's Projects
[NeurIPS 2022] Implementation of "AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition"
Effortless AI-assisted data labeling with AI support from YOLO, Segment Anything, MobileSAM!!
Config files for my GitHub profile.
Code release for ConvNeXt model
This is a collection of our NAS and Vision Transformer work.
The open-source tool for building high-quality datasets and computer vision models
Unofficial implementation of inference and training for mobilenetv1, mobilenetV2, mobilenetv3, shuffllenet, shuffllenetv2, ghostnet, and repghost, etc.
⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥
Object detection and instance segmentation toolkit based on PaddlePaddle.
🚀🚀🚀 YOLO series of PaddlePaddle implementation, PP-YOLOE+, RT-DETR, YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv10, YOLOX, YOLOv5u, YOLOv7u, YOLOv6Lite, RTMDet and so on. 🚀🚀🚀
here are some code for preprocessing data before you start to train a model
95.47% on CIFAR10 with PyTorch
Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
[CVPR 2024] Official RT-DETR (RTDETR paddle pytorch), Real-Time DEtection TRansformer, DETRs Beat YOLOs on Real-time Object Detection. 🔥 🔥 🔥
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
天宫一号图像分类比赛线上B榜第一
Official implementation of the CVPR 2024 paper ViT-CoMer: Vision Transformer with Convolutional Multi-scale Feature Interaction for Dense Predictions.