| lenet |
Gradient-Based Learning Applied to Document Recognition |
link |
| googlenet |
Going Deeper with Convolutions |
link |
| inception_resnet_v2 |
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning |
link |
| inceptionv2 |
Rethinking the Inception Architecture for Computer Vision |
link |
| nasnet |
Learning Transferable Architectures for Scalable Image Recognition |
link |
| densenet100 |
Densely Connected Convolutional Networks |
link |
| densenet121 |
Densely Connected Convolutional Networks |
link |
| dpn |
Dual Path Networks |
link |
| efficientnet-b0 |
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks |
link |
| mobilenetv3_large |
Searching for MobileNetV3 |
link |
| mobilenetV3_small_x1_0 |
Searching for MobileNetV3 |
link |
| squeezenet |
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size |
link |
| ghostnet |
GhostNet: More Features from Cheap Operations |
link |
| ghostnet_d |
GhostNets on Heterogeneous Devices via Cheap Operations |
link |
| tinydarknet |
You Only Look Once: Unified, Real-Time Object Detection |
link |
| sppnet |
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition |
link |
| res2net101 |
Res2Net: A New Multi-scale Backbone Architecture |
link |
| res2net152 |
Res2Net: A New Multi-scale Backbone Architecture |
link |
| res2net50 |
Res2Net: A New Multi-scale Backbone Architecture |
link |
| resnext50 |
Aggregated Residual Transformations for Deep Neural Networks |
link |
| resnest50 |
ResNeSt: Split-Attention Networks |
link |
| resnet50_adv_pruning |
SCOP: Scientific Control for Reliable Neural Network Pruning |
link |
| resnet50_bam |
BAM: Bottleneck Attention Module |
link |
| resnet50-quadruplet |
Beyond triplet loss: A deep quadruplet network for person re-identification |
link |
| se_resnext50 |
Squeeze-and-Excitation Networks |
link |
| senet_resnet101 |
Squeeze-and-Excitation Networks |
link |
| senet_resnet50 |
Squeeze-and-Excitation Networks |
link |
| se-res2net50 |
Res2Net: A New Multi-scale Backbone Architecture |
link |
| se_resnext50 |
Squeeze-and-Excitation Networks |
link |
| s-ghostnet |
Greedy Network Enlarging |
link |
| sknet |
Selective Kernel Networks |
link |
| resnetv2_101 |
Identity Mappings in Deep Residual Networks |
link |
| resnetv2_152 |
Identity Mappings in Deep Residual Networks |
link |
| resnetv2_50 |
Identity Mappings in Deep Residual Networks |
link |
| resnetv2_50_frn |
Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks |
link |
| resnext152_64x4d |
Aggregated Residual Transformations for Deep Neural Networks |
link |
| hrnet_w48_cls |
Deep High-Resolution Representation Learning for Visual Recognition |
link |
| simclr |
A Simple Framework for Contrastive Learning of Visual Representations |
link |
| augvit |
Augmented Shortcuts for Vision Transformers |
link |
| autoaugment |
AutoAugment: Learning Augmentation Policies from Data |
link |
| ava_cifar |
AVA: Adversarial Vignetting Attack against Visual Recognition |
link |
| ava_hpa |
AVA: Adversarial Vignetting Attack against Visual Recognition |
link |
| pdarts |
Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild |
link |
| pnasnet |
Progressive Neural Architecture Search |
link |
| poseestnet |
PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data |
link |
| protonet |
Prototypical Networks for Few-shot Learning |
link |
| proxylessnas |
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware |
link |
| ntsnet |
Learning to Navigate for Fine-grained Classification |
link |
| nfnet |
High-Performance Large-Scale Image Recognition Without Normalization |
link |
| mnasnet |
MnasNet: Platform-Aware Neural Architecture Search for Mobile |
link |
| hour-nas |
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens |
link |
| single_path_nas |
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours |
link |
| multitasknet |
PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data |
link |
| tcn |
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling |
link |
| vig |
Vision GNN: An Image is Worth Graph of Nodes |
link |
| vit_base |
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale |
link |
| convnext |
A ConvNet for the 2020s |
link |
| wave_mlp |
An Image Patch is a Wave: Phase-Aware Vision MLP |
link |
| wideresnet |
Wide Residual Networks |
link |
| tinynet |
Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets |
link |
| tnt |
TNT: Target-driveN Trajectory Prediction |
link |
| cait |
Going deeper with Image Transformers |
link |
| ssc_resnet50 |
Comatch: Semi-supervised learning with contrastive graph regularization |
link |
| snn_mlp |
Brain-inspired Multilayer Perceptron with Spiking Neurons |
link |
| relationnet |
Learning to Compare: Relation Network for Few-Shot Learning |
link |
| cbam |
CBAM: Convolutional Block Attention Module |
link |
| cct |
Escaping the Big Data Paradigm with Compact Transformers |
link |
| drnet |
Dynamic Resolution Network |
link |
| fda-bnn |
Learning Frequency Domain Approximation for Binary Neural Networks |
link |
| fishnet99 |
FishNet: a versatile backbone for image, region, and pixel level prediction |
link |
| genet_res50 |
Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks |
link |
| glore_res200 |
Graph-Based Global Reasoning Networks |
link |
| glore_res50 |
Graph-Based Global Reasoning Networks |
link |
| hardnet |
HarDNet: A Low Memory Traffic Network |
link |
| ibnnet |
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net |
link |
| isynet |
ISyNet: Convolutional Neural Networks design for AI accelerator |
link |
| ivpf |
iVPF: Numerical Invertible Volume Preserving Flow for Efficient Lossless Compression |
link |
| meta-baseline |
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning |
link |
| maml |
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks |
link |
| leo |
Meta-Learning with Latent Embedding Optimization |
link |
| auto-slim |
AutoSlim: Towards One-Shot Architecture Search for Channel Numbers |
link |
| dem |
Learning a Deep Embedding Model for Zero-Shot Learning |
link |
| triplet_loss_resnet50 |
Beyond triplet loss: A deep quadruplet network for person re-identification |
link |