kadirnar / timm_model_list

Model Description

Aggregating Nested Transformers - https://arxiv.org/abs/2105.12723

BEiT - https://arxiv.org/abs/2106.08254

Big Transfer ResNetV2 (BiT) - https://arxiv.org/abs/1912.11370

Bottleneck Transformers - https://arxiv.org/abs/2101.11605

CaiT (Class-Attention in Image Transformers) - https://arxiv.org/abs/2103.17239

CoaT (Co-Scale Conv-Attentional Image Transformers) - https://arxiv.org/abs/2104.06399

CoAtNet (Convolution and Attention) - https://arxiv.org/abs/2106.04803

ConvNeXt - https://arxiv.org/abs/2201.03545

ConvNeXt-V2 - http://arxiv.org/abs/2301.00808

ConViT (Soft Convolutional Inductive Biases Vision Transformers)- https://arxiv.org/abs/2103.10697

CspNet (Cross-Stage Partial Networks) - https://arxiv.org/abs/1911.11929

DeiT - https://arxiv.org/abs/2012.12877

DeiT-III - https://arxiv.org/pdf/2204.07118.pdf

DenseNet - https://arxiv.org/abs/1608.06993

DLA - https://arxiv.org/abs/1707.06484

DPN (Dual-Path Network) - https://arxiv.org/abs/1707.01629

EdgeNeXt - https://arxiv.org/abs/2206.10589

EfficientFormer - https://arxiv.org/abs/2206.01191

EfficientNet (MBConvNet Family)

EfficientNet NoisyStudent (B0-B7, L2) - https://arxiv.org/abs/1911.04252

EfficientNet AdvProp (B0-B8) - https://arxiv.org/abs/1911.09665

EfficientNet (B0-B7) - https://arxiv.org/abs/1905.11946

EfficientNet-EdgeTPU (S, M, L) - https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html

EfficientNet V2 - https://arxiv.org/abs/2104.00298

FBNet-C - https://arxiv.org/abs/1812.03443

MixNet - https://arxiv.org/abs/1907.09595

MNASNet B1, A1 (Squeeze-Excite), and Small - https://arxiv.org/abs/1807.11626

MobileNet-V2 - https://arxiv.org/abs/1801.04381

Single-Path NAS - https://arxiv.org/abs/1904.02877

TinyNet - https://arxiv.org/abs/2010.14819

EVA - https://arxiv.org/abs/2211.07636

FlexiViT - https://arxiv.org/abs/2212.08013

GCViT (Global Context Vision Transformer) - https://arxiv.org/abs/2206.09959

GhostNet - https://arxiv.org/abs/1911.11907

gMLP - https://arxiv.org/abs/2105.08050

GPU-Efficient Networks - https://arxiv.org/abs/2006.14090

Halo Nets - https://arxiv.org/abs/2103.12731

HRNet - https://arxiv.org/abs/1908.07919

Inception-V3 - https://arxiv.org/abs/1512.00567

Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261

Lambda Networks - https://arxiv.org/abs/2102.08602

LeViT (Vision Transformer in ConvNet's Clothing) - https://arxiv.org/abs/2104.01136

MaxViT (Multi-Axis Vision Transformer) - https://arxiv.org/abs/2204.01697

MLP-Mixer - https://arxiv.org/abs/2105.01601

MobileNet-V3 (MBConvNet w/ Efficient Head) - https://arxiv.org/abs/1905.02244

FBNet-V3 - https://arxiv.org/abs/2006.02049

HardCoRe-NAS - https://arxiv.org/abs/2102.11646

LCNet - https://arxiv.org/abs/2109.15099

MobileViT - https://arxiv.org/abs/2110.02178

MobileViT-V2 - https://arxiv.org/abs/2206.02680

MViT-V2 (Improved Multiscale Vision Transformer) - https://arxiv.org/abs/2112.01526

NASNet-A - https://arxiv.org/abs/1707.07012

NesT - https://arxiv.org/abs/2105.12723

NFNet-F - https://arxiv.org/abs/2102.06171

NF-RegNet / NF-ResNet - https://arxiv.org/abs/2101.08692

PNasNet - https://arxiv.org/abs/1712.00559

PoolFormer (MetaFormer) - https://arxiv.org/abs/2111.11418

Pooling-based Vision Transformer (PiT) - https://arxiv.org/abs/2103.16302

PVT-V2 (Improved Pyramid Vision Transformer) - https://arxiv.org/abs/2106.13797

RegNet - https://arxiv.org/abs/2003.13678

RegNetZ - https://arxiv.org/abs/2103.06877

RepVGG - https://arxiv.org/abs/2101.03697

ResMLP - https://arxiv.org/abs/2105.03404

ResNet/ResNeXt

ResNet (v1b/v1.5) - https://arxiv.org/abs/1512.03385

ResNeXt - https://arxiv.org/abs/1611.05431

'Bag of Tricks' / Gluon C, D, E, S variations - https://arxiv.org/abs/1812.01187

Weakly-supervised (WSL) Instagram pretrained / ImageNet tuned ResNeXt101 - https://arxiv.org/abs/1805.00932

Semi-supervised (SSL) / Semi-weakly Supervised (SWSL) ResNet/ResNeXts - https://arxiv.org/abs/1905.00546

ECA-Net (ECAResNet) - https://arxiv.org/abs/1910.03151v4

Squeeze-and-Excitation Networks (SEResNet) - https://arxiv.org/abs/1709.01507

ResNet-RS - https://arxiv.org/abs/2103.07579

Res2Net - https://arxiv.org/abs/1904.01169

ResNeSt - https://arxiv.org/abs/2004.08955

ReXNet - https://arxiv.org/abs/2007.00992

SelecSLS - https://arxiv.org/abs/1907.00837

Selective Kernel Networks - https://arxiv.org/abs/1903.06586

Sequencer2D - https://arxiv.org/abs/2205.01972

Swin S3 (AutoFormerV2) - https://arxiv.org/abs/2111.14725

Swin Transformer - https://arxiv.org/abs/2103.14030

Swin Transformer V2 - https://arxiv.org/abs/2111.09883

Transformer-iN-Transformer (TNT) - https://arxiv.org/abs/2103.00112

TResNet - https://arxiv.org/abs/2003.13630

Twins (Spatial Attention in Vision Transformers) - https://arxiv.org/abs/2104.13840

Visformer - https://arxiv.org/abs/2104.12533

Vision Transformer - https://arxiv.org/abs/2010.11929

VOLO (Vision Outlooker) - https://arxiv.org/abs/2106.13112

VovNet V2 and V1 - https://arxiv.org/abs/1911.06667

Xception - https://arxiv.org/abs/1610.02357

Xception (Modified Aligned, Gluon) - https://arxiv.org/abs/1802.02611

Xception (Modified Aligned, TF) - https://arxiv.org/abs/1802.02611

XCiT (Cross-Covariance Image Transformers) - https://arxiv.org/abs/2106.09681

Installation

pip install classifyhub

ClassifyHub(Timm) Usage

from classifyhub import Predictor

model = ClassifyPredictor("resnet18")
model.predict("data/plane.jpg")