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awesome-computer-vision-models
A list of popular deep learning models related to classification, segmentation and detection problems
https://github.com/gmalivenko/awesome-computer-vision-models
- 'One weird trick for parallelizing convolutional neural networks'
- 'Very Deep Convolutional Networks for Large-Scale Image Recognition'
- 'Deep Residual Learning for Image Recognition'
- 'Deep Residual Learning for Image Recognition'
- 'Deep Residual Learning for Image Recognition'
- 'Deep Residual Learning for Image Recognition'
- 'Rethinking the Inception Architecture for Computer Vision'
- 'Identity Mappings in Deep Residual Networks'
- 'Identity Mappings in Deep Residual Networks'
- 'Identity Mappings in Deep Residual Networks'
- 'Densely Connected Convolutional Networks'
- 'Densely Connected Convolutional Networks'
- 'Deep Pyramidal Residual Networks'
- 'Aggregated Residual Transformations for Deep Neural Networks'
- 'Aggregated Residual Transformations for Deep Neural Networks'
- 'Wide Residual Networks'
- 'Xception: Deep Learning with Depthwise Separable Convolutions'
- 'Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning'
- 'Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning'
- 'PolyNet: A Pursuit of Structural Diversity in Very Deep Networks'
- 'Darknet: Open source neural networks in C'
- 'Darknet: Open source neural networks in C'
- 'Darknet: Open source neural networks in C'
- 'SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size'
- 'SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size'
- 'Residual Attention Network for Image Classification'
- 'CondenseNet: An Efficient DenseNet using Learned Group Convolutions'
- 'Dual Path Networks'
- 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices'
- 'DiracNets: Training Very Deep Neural Networks Without Skip-Connections'
- 'DiracNets: Training Very Deep Neural Networks Without Skip-Connections'
- 'Squeeze-and-Excitation Networks'
- 'Squeeze-and-Excitation Networks'
- 'MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications'
- 'Learning Transferable Architectures for Scalable Image Recognition'
- 'Learning Transferable Architectures for Scalable Image Recognition'
- 'Deep Layer Aggregation'
- 'Attention Inspiring Receptive-Fields Network for Learning Invariant Representations'
- 'BAM: Bottleneck Attention Module'
- 'CBAM: Convolutional Block Attention Module'
- 'SqueezeNext: Hardware-Aware Neural Network Design'
- 'SqueezeNext: Hardware-Aware Neural Network Design'
- 'SqueezeNext: Hardware-Aware Neural Network Design'
- 'ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design'
- 'Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications'
- 'FD-MobileNet: Improved MobileNet with A Fast Downsampling Strategy'
- 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
- 'IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks'
- 'DARTS: Differentiable Architecture Search'
- 'Progressive Neural Architecture Search'
- 'Regularized Evolution for Image Classifier Architecture Search'
- 'MnasNet: Platform-Aware Neural Architecture Search for Mobile'
- 'Two at Once: Enhancing Learning andGeneralization Capacities via IBN-Net'
- 'Large Margin Deep Networks for Classification'
- 'A^2-Nets: Double Attention Networks'
- 'FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction'
- 'IMAGENET-TRAINED CNNS ARE BIASED TOWARDS TEXTURE; INCREASING SHAPE BIAS IMPROVES ACCURACY AND ROBUSTNESS'
- 'Greedy Layerwise Learning Can Scale to ImageNet'
- 'Selective Kernel Networks'
- 'SRM : A Style-based Recalibration Module for Convolutional Neural Networks'
- 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks'
- 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks'
- 'PROXYLESSNAS: DIRECT NEURAL ARCHITECTURE SEARCH ON TARGET TASK AND HARDWARE'
- 'MixNet: Mixed Depthwise Convolutional Kernels'
- 'ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks'
- 'ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks'
- 'ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks'
- 'LIP: Local Importance-based Pooling'
- 'LIP: Local Importance-based Pooling'
- 'LIP: Local Importance-based Pooling'
- 'MuffNet: Multi-Layer Feature Federation for Mobile Deep Learning'
- 'MuffNet: Multi-Layer Feature Federation for Mobile Deep Learning'
- 'Making Convolutional Networks Shift-Invariant Again'
- 'Making Convolutional Networks Shift-Invariant Again'
- 'Making Convolutional Networks Shift-Invariant Again'
- 'Fixing the train-test resolution discrepancy'
- 'Self-training with Noisy Student improves ImageNet classification'
- 'TResNet: High Performance GPU-Dedicated Architecture'
- 'DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search'
- 'ResNeSt: Split-Attention Networks'
- 'ResNeSt: Split-Attention Networks'
- 'Funnel Activation for Visual Recognition'
- 'Funnel Activation for Visual Recognition'
- 'MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks'
- 'MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks'
- 'MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks'
- 'MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks'
- 'Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet'
- 'Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet'
- 'Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet'
- 'High-Performance Large-Scale Image Recognition Without Normalization'
- 'High-Performance Large-Scale Image Recognition Without Normalization'
- 'High-Performance Large-Scale Image Recognition Without Normalization'
- 'EfficientNetV2: Smaller Models and Faster Training'
- 'EfficientNetV2: Smaller Models and Faster Training'
- 'EfficientNetV2: Smaller Models and Faster Training'
- 'EfficientNetV2: Smaller Models and Faster Training'
- 'EfficientNetV2: Smaller Models and Faster Training'
- 'EfficientNetV2: Smaller Models and Faster Training'
- 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
- 'Learning Deconvolution Network for Semantic Segmentation'
- 'ParseNet: Looking Wider to See Better'
- 'Efficient piecewise training of deep structured models for semantic segmentation'
- 'SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation'
- 'Fully Convolutional Networks for Semantic Segmentation'
- 'ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation'
- 'MULTI-SCALE CONTEXT AGGREGATION BY DILATED CONVOLUTIONS'
- 'PixelNet: Towards a General Pixel-Level Architecture'
- 'RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation'
- 'Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation'
- 'Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes'
- 'MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving'
- 'DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs'
- 'LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation'
- 'The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation'
- 'ICNet for Real-Time Semantic Segmentation on High-Resolution Images'
- 'Efficient ConvNet for Real-time Semantic Segmentation'
- 'Pyramid Scene Parsing Network'
- 'Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network'
- 'Segmentation-Aware Convolutional Networks Using Local Attention Masks'
- 'PIXEL DECONVOLUTIONAL NETWORKS'
- 'Rethinking Atrous Convolution for Semantic Image Segmentation'
- 'Understanding Convolution for Semantic Segmentation'
- 'SHUFFLESEG: REAL-TIME SEMANTIC SEGMENTATION NETWORK'
- 'Learning to Adapt Structured Output Space for Semantic Segmentation'
- 'Understanding Convolution for Semantic Segmentation'
- 'Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation'
- 'Attention U-Net: Learning Where to Look for the Pancreas'
- 'Dual Attention Network for Scene Segmentation'
- 'Context Encoding for Semantic Segmentation'
- 'ShelfNet for Real-time Semantic Segmentation'
- 'LADDERNET: MULTI-PATH NETWORKS BASED ON U-NET FOR MEDICAL IMAGE SEGMENTATION'
- 'Concentrated-Comprehensive Convolutions for lightweight semantic segmentation'
- 'DifNet: Semantic Segmentation by Diffusion Networks'
- 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation'
- 'ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation'
- 'Semantic Image Synthesis with Spatially-Adaptive Normalization'
- 'Seamless Scene Segmentation'
- 'Expectation-Maximization Attention Networks for Semantic Segmentation'
- 'Rich feature hierarchies for accurate object detection and semantic segmentation'
- 'OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks'
- 'Scalable Object Detection using Deep Neural Networks'
- 'Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition'
- 'Object detection via a multi-region & semantic segmentation-aware CNN model'
- 'AttentionNet: Aggregating Weak Directions for Accurate Object Detection'
- 'Fast R-CNN'
- 'Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks'
- 'You Only Look Once: Unified, Real-Time Object Detection'
- 'G-CNN: an Iterative Grid Based Object Detector'
- 'Adaptive Object Detection Using Adjacency and Zoom Prediction'
- 'Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks'
- 'HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection'
- 'Training Region-based Object Detectors with Online Hard Example Mining'
- 'A MultiPath Network for Object Detection'
- 'SSD: Single Shot MultiBox Detector'
- 'Crafting GBD-Net for Object Detection'
- 'Contextual Priming and Feedback for Faster R-CNN'
- 'A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection'
- 'R-FCN: Object Detection via Region-based Fully Convolutional Networks'
- 'PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection'
- 'DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection'
- 'Object Detection Networks on Convolutional Feature Maps'
- 'DSSD : Deconvolutional Single Shot Detector'
- 'Beyond Skip Connections: Top-Down Modulation for Object Detection'
- 'Feature Pyramid Networks for Object Detection'
- 'YOLO9000: Better, Faster, Stronger'
- 'RON: Reverse Connection with Objectness Prior Networks for Object Detection'
- 'Deformable Convolutional Networks'
- 'DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling'
- 'CoupleNet: Coupling Global Structure with Local Parts for Object Detection'
- 'Focal Loss for Dense Object Detection'
- 'Mask R-CNN'
- 'DSOD: Learning Deeply Supervised Object Detectors from Scratch'
- 'Spatial Memory for Context Reasoning in Object Detection'
- 'YOLOv3: An Incremental Improvement'
- 'Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships'
- 'Scale-Transferrable Object Detection'
- 'Single-Shot Refinement Neural Network for Object Detection'
- 'MegDet: A Large Mini-Batch Object Detector'
- 'Receptive Field Block Net for Accurate and Fast Object Detection'
- 'CornerNet: Detecting Objects as Paired Keypoints'
- 'Libra R-CNN: Towards Balanced Learning for Object Detection'
- 'YOLACT Real-time Instance Segmentation'
- 'DetNAS: Backbone Search for Object Detection'
- 'YOLOv4: Optimal Speed and Accuracy of Object Detection'
- 'SOLO: Segmenting Objects by Locations'
- 'SOLO: Segmenting Objects by Locations'
- 'Scale Normalized Image Pyramids with AutoFocus for Object Detection'
- 'Scale Normalized Image Pyramids with AutoFocus for Object Detection'
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