resnet

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

Implementation of popular deep learning networks with TensorRT network definition API

Tools to Design or Visualize Architecture of Neural Network

Pretrained Pytorch face detection (MTCNN) and facial recognition (InceptionResnet) models

Segmentation models with pretrained backbones. Keras and TensorFlow Keras.

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)

OpenMMLab Pre-training Toolbox and Benchmark

校招、秋招、春招、实习好项目！带你从零实现一个高性能的深度学习推理库，支持大模型 llama2 、Unet、Yolov5、Resnet等模型的推理。Implement a high-performance deep learning inference library step by step

DeepLab v3+ model in PyTorch. Support different backbones.

Integrate deep learning models for image classification | Backbone learning/comparison/magic modification project

A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation

Classification with PyTorch.

Research and Production Oriented Speaker Verification, Recognition and Diarization Toolkit

Proper implementation of ResNet-s for CIFAR10/100 in pytorch that matches description of the original paper.

A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.

Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models

More readable and flexible yolov5 with more backbone(gcn, resnet, shufflenet, moblienet, efficientnet, hrnet, swin-transformer, etc) and (cbam，dcn and so on), and tensorrt

Extract video features from raw videos using multiple GPUs. We support RAFT flow frames as well as S3D, I3D, R(2+1)D, VGGish, CLIP, and TIMM models.

Self-labelling via simultaneous clustering and representation learning. (ICLR 2020)

Code for the Nature Scientific Reports paper "Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks." A sliding window framework for classification of high resolution whole-slide images, often microscopy or histopathology images.