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--- |
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license: apache-2.0 |
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tags: |
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- object-detection |
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- computer-vision |
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- object-classification |
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language: |
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- en |
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--- |
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### Model Description |
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Aggregating Nested Transformers - https://arxiv.org/abs/2105.12723 |
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BEiT - https://arxiv.org/abs/2106.08254 |
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Big Transfer ResNetV2 (BiT) - https://arxiv.org/abs/1912.11370 |
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Bottleneck Transformers - https://arxiv.org/abs/2101.11605 |
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CaiT (Class-Attention in Image Transformers) - https://arxiv.org/abs/2103.17239 |
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CoaT (Co-Scale Conv-Attentional Image Transformers) - https://arxiv.org/abs/2104.06399 |
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CoAtNet (Convolution and Attention) - https://arxiv.org/abs/2106.04803 |
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ConvNeXt - https://arxiv.org/abs/2201.03545 |
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ConvNeXt-V2 - http://arxiv.org/abs/2301.00808 |
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ConViT (Soft Convolutional Inductive Biases Vision Transformers)- https://arxiv.org/abs/2103.10697 |
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CspNet (Cross-Stage Partial Networks) - https://arxiv.org/abs/1911.11929 |
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DeiT - https://arxiv.org/abs/2012.12877 |
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DeiT-III - https://arxiv.org/pdf/2204.07118.pdf |
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DenseNet - https://arxiv.org/abs/1608.06993 |
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DLA - https://arxiv.org/abs/1707.06484 |
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DPN (Dual-Path Network) - https://arxiv.org/abs/1707.01629 |
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EdgeNeXt - https://arxiv.org/abs/2206.10589 |
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EfficientFormer - https://arxiv.org/abs/2206.01191 |
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EfficientNet (MBConvNet Family) |
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EfficientNet NoisyStudent (B0-B7, L2) - https://arxiv.org/abs/1911.04252 |
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EfficientNet AdvProp (B0-B8) - https://arxiv.org/abs/1911.09665 |
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EfficientNet (B0-B7) - https://arxiv.org/abs/1905.11946 |
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EfficientNet-EdgeTPU (S, M, L) - https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html |
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EfficientNet V2 - https://arxiv.org/abs/2104.00298 |
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FBNet-C - https://arxiv.org/abs/1812.03443 |
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MixNet - https://arxiv.org/abs/1907.09595 |
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MNASNet B1, A1 (Squeeze-Excite), and Small - https://arxiv.org/abs/1807.11626 |
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MobileNet-V2 - https://arxiv.org/abs/1801.04381 |
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Single-Path NAS - https://arxiv.org/abs/1904.02877 |
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TinyNet - https://arxiv.org/abs/2010.14819 |
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EVA - https://arxiv.org/abs/2211.07636 |
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FlexiViT - https://arxiv.org/abs/2212.08013 |
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GCViT (Global Context Vision Transformer) - https://arxiv.org/abs/2206.09959 |
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GhostNet - https://arxiv.org/abs/1911.11907 |
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gMLP - https://arxiv.org/abs/2105.08050 |
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GPU-Efficient Networks - https://arxiv.org/abs/2006.14090 |
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Halo Nets - https://arxiv.org/abs/2103.12731 |
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HRNet - https://arxiv.org/abs/1908.07919 |
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Inception-V3 - https://arxiv.org/abs/1512.00567 |
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Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261 |
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Lambda Networks - https://arxiv.org/abs/2102.08602 |
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LeViT (Vision Transformer in ConvNet's Clothing) - https://arxiv.org/abs/2104.01136 |
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MaxViT (Multi-Axis Vision Transformer) - https://arxiv.org/abs/2204.01697 |
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MLP-Mixer - https://arxiv.org/abs/2105.01601 |
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MobileNet-V3 (MBConvNet w/ Efficient Head) - https://arxiv.org/abs/1905.02244 |
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FBNet-V3 - https://arxiv.org/abs/2006.02049 |
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HardCoRe-NAS - https://arxiv.org/abs/2102.11646 |
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LCNet - https://arxiv.org/abs/2109.15099 |
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MobileViT - https://arxiv.org/abs/2110.02178 |
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MobileViT-V2 - https://arxiv.org/abs/2206.02680 |
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MViT-V2 (Improved Multiscale Vision Transformer) - https://arxiv.org/abs/2112.01526 |
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NASNet-A - https://arxiv.org/abs/1707.07012 |
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NesT - https://arxiv.org/abs/2105.12723 |
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NFNet-F - https://arxiv.org/abs/2102.06171 |
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NF-RegNet / NF-ResNet - https://arxiv.org/abs/2101.08692 |
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PNasNet - https://arxiv.org/abs/1712.00559 |
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PoolFormer (MetaFormer) - https://arxiv.org/abs/2111.11418 |
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Pooling-based Vision Transformer (PiT) - https://arxiv.org/abs/2103.16302 |
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PVT-V2 (Improved Pyramid Vision Transformer) - https://arxiv.org/abs/2106.13797 |
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RegNet - https://arxiv.org/abs/2003.13678 |
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RegNetZ - https://arxiv.org/abs/2103.06877 |
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RepVGG - https://arxiv.org/abs/2101.03697 |
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ResMLP - https://arxiv.org/abs/2105.03404 |
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ResNet/ResNeXt |
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ResNet (v1b/v1.5) - https://arxiv.org/abs/1512.03385 |
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ResNeXt - https://arxiv.org/abs/1611.05431 |
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'Bag of Tricks' / Gluon C, D, E, S variations - https://arxiv.org/abs/1812.01187 |
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Weakly-supervised (WSL) Instagram pretrained / ImageNet tuned ResNeXt101 - https://arxiv.org/abs/1805.00932 |
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Semi-supervised (SSL) / Semi-weakly Supervised (SWSL) ResNet/ResNeXts - https://arxiv.org/abs/1905.00546 |
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ECA-Net (ECAResNet) - https://arxiv.org/abs/1910.03151v4 |
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Squeeze-and-Excitation Networks (SEResNet) - https://arxiv.org/abs/1709.01507 |
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ResNet-RS - https://arxiv.org/abs/2103.07579 |
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Res2Net - https://arxiv.org/abs/1904.01169 |
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ResNeSt - https://arxiv.org/abs/2004.08955 |
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ReXNet - https://arxiv.org/abs/2007.00992 |
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SelecSLS - https://arxiv.org/abs/1907.00837 |
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Selective Kernel Networks - https://arxiv.org/abs/1903.06586 |
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Sequencer2D - https://arxiv.org/abs/2205.01972 |
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Swin S3 (AutoFormerV2) - https://arxiv.org/abs/2111.14725 |
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Swin Transformer - https://arxiv.org/abs/2103.14030 |
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Swin Transformer V2 - https://arxiv.org/abs/2111.09883 |
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Transformer-iN-Transformer (TNT) - https://arxiv.org/abs/2103.00112 |
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TResNet - https://arxiv.org/abs/2003.13630 |
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Twins (Spatial Attention in Vision Transformers) - https://arxiv.org/abs/2104.13840 |
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Visformer - https://arxiv.org/abs/2104.12533 |
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Vision Transformer - https://arxiv.org/abs/2010.11929 |
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VOLO (Vision Outlooker) - https://arxiv.org/abs/2106.13112 |
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VovNet V2 and V1 - https://arxiv.org/abs/1911.06667 |
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Xception - https://arxiv.org/abs/1610.02357 |
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Xception (Modified Aligned, Gluon) - https://arxiv.org/abs/1802.02611 |
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Xception (Modified Aligned, TF) - https://arxiv.org/abs/1802.02611 |
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XCiT (Cross-Covariance Image Transformers) - https://arxiv.org/abs/2106.09681 |
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### Installation |
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``` |
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pip install classifyhub |
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``` |
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### ClassifyHub(Timm) Usage |
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```python |
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from classifyhub import Predictor |
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model = ClassifyPredictor("resnet18") |
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model.predict("data/plane.jpg") |
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``` |
