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README.md

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 ---
 license: apache-2.0
+tags:
+- object-detection
+- computer-vision
+- timm
+- object-classification
+language:
+- en
+library_name: timm
 ---
+
+### 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/pdf/2104.13840.pdf
+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
+```python
+from classifyhub import Predictor
+
+model = ClassifyPredictor("resnet18")
+model.predict("data/plane.jpg")
+```