repo_id stringlengths 15 86 | file_path stringlengths 27 180 | content stringlengths 1 1.75M | __index_level_0__ int64 0 0 |
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hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/inception-resnet-v2.mdx | # Inception ResNet v2
**Inception-ResNet-v2** is a convolutional neural architecture that builds on the Inception family of architectures but incorporates [residual connections](https://paperswithcode.com/method/residual-connection) (replacing the filter concatenation stage of the Inception architecture).
## How do I... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/inception-v3.mdx | # Inception v3
**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswithcode.co... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/inception-v4.mdx | # Inception v4
**Inception-v4** is a convolutional neural network architecture that builds on previous iterations of the Inception family by simplifying the architecture and using more inception modules than [Inception-v3](https://paperswithcode.com/method/inception-v3).
## How do I use this model on an image?
To loa... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/legacy-se-resnet.mdx | # (Legacy) SE-ResNet
**SE ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
## How do I use this mod... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/legacy-se-resnext.mdx | # (Legacy) SE-ResNeXt
**SE ResNeXt** is a variant of a [ResNeXt](https://www.paperswithcode.com/method/resnext) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
## How do I use this... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/legacy-senet.mdx | # (Legacy) SENet
A **SENet** is a convolutional neural network architecture that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
The weights from this model were ported from Gluon.
## ... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/mixnet.mdx | # MixNet
**MixNet** is a type of convolutional neural network discovered via AutoML that utilises [MixConvs](https://paperswithcode.com/method/mixconv) instead of regular [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution).
## How do I use this model on an image?
To load a pretrained mo... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/mnasnet.mdx | # MnasNet
**MnasNet** is a type of convolutional neural network optimized for mobile devices that is discovered through mobile neural architecture search, which explicitly incorporates model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and late... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/mobilenet-v2.mdx | # MobileNet v2
**MobileNetV2** is a convolutional neural network architecture that seeks to perform well on mobile devices. It is based on an [inverted residual structure](https://paperswithcode.com/method/inverted-residual-block) where the residual connections are between the bottleneck layers. The intermediate expa... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/mobilenet-v3.mdx | # MobileNet v3
**MobileNetV3** is a convolutional neural network that is designed for mobile phone CPUs. The network design includes the use of a [hard swish activation](https://paperswithcode.com/method/hard-swish) and [squeeze-and-excitation](https://paperswithcode.com/method/squeeze-and-excitation-block) modules in... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/nasnet.mdx | # NASNet
**NASNet** is a type of convolutional neural network discovered through neural architecture search. The building blocks consist of normal and reduction cells.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('nasnetalarge', pretrained=T... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/noisy-student.mdx | # Noisy Student (EfficientNet)
**Noisy Student Training** is a semi-supervised learning approach. It extends the idea of self-training
and distillation with the use of equal-or-larger student models and noise added to the student during learning. It has three main steps:
1. train a teacher model on labeled images
2.... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/pnasnet.mdx | # PNASNet
**Progressive Neural Architecture Search**, or **PNAS**, is a method for learning the structure of convolutional neural networks (CNNs). It uses a sequential model-based optimization (SMBO) strategy, where we search the space of cell structures, starting with simple (shallow) models and progressing to comple... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/regnetx.mdx | # RegNetX
**RegNetX** is a convolutional network design space with simple, regular models with parameters: depth $d$, initial width $w\_{0} > 0$, and slope $w\_{a} > 0$, and generates a different block width $u\_{j}$ for each block $j < d$. The key restriction for the RegNet types of model is that there is a linear pa... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/regnety.mdx | # RegNetY
**RegNetY** is a convolutional network design space with simple, regular models with parameters: depth $d$, initial width $w\_{0} > 0$, and slope $w\_{a} > 0$, and generates a different block width $u\_{j}$ for each block $j < d$. The key restriction for the RegNet types of model is that there is a linear pa... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/res2net.mdx | # Res2Net
**Res2Net** is an image model that employs a variation on bottleneck residual blocks, [Res2Net Blocks](https://paperswithcode.com/method/res2net-block). The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for CNNs that constructs hierarchical... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/res2next.mdx | # Res2NeXt
**Res2NeXt** is an image model that employs a variation on [ResNeXt](https://paperswithcode.com/method/resnext) bottleneck residual blocks. The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for CNNs that constructs hierarchical residual-li... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/resnest.mdx | # ResNeSt
A **ResNeSt** is a variant on a [ResNet](https://paperswithcode.com/method/resnet), which instead stacks [Split-Attention blocks](https://paperswithcode.com/method/split-attention). The cardinal group representations are then concatenated along the channel dimension: $V = \text{Concat}${$V^{1},V^{2},\cdots{V... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/resnet-d.mdx | # ResNet-D
**ResNet-D** is a modification on the [ResNet](https://paperswithcode.com/method/resnet) architecture that utilises an [average pooling](https://paperswithcode.com/method/average-pooling) tweak for downsampling. The motivation is that in the unmodified ResNet, the [1×1 convolution](https://paperswithcode.co... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/resnet.mdx | # ResNet
**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual block... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/resnext.mdx | # ResNeXt
A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (the size of the set of transformations) $C$,... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/rexnet.mdx | # RexNet
**Rank Expansion Networks** (ReXNets) follow a set of new design principles for designing bottlenecks in image classification models. Authors refine each layer by 1) expanding the input channel size of the convolution layer and 2) replacing the [ReLU6s](https://www.paperswithcode.com/method/relu6).
## How do... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/se-resnet.mdx | # SE-ResNet
**SE ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
## How do I use this model on an ... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/selecsls.mdx | # SelecSLS
**SelecSLS** uses novel selective long and short range skip connections to improve the information flow allowing for a drastically faster network without compromising accuracy.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('selecsl... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/seresnext.mdx | # SE-ResNeXt
**SE ResNeXt** is a variant of a [ResNext](https://www.paperswithcode.com/method/resneXt) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
## How do I use this model on... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/skresnet.mdx | # SK-ResNet
**SK ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs a [Selective Kernel](https://paperswithcode.com/method/selective-kernel) unit. In general, all the large kernel convolutions in the original bottleneck blocks in ResNet are replaced by the proposed [SK convo... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/skresnext.mdx | # SK-ResNeXt
**SK ResNeXt** is a variant of a [ResNeXt](https://www.paperswithcode.com/method/resnext) that employs a [Selective Kernel](https://paperswithcode.com/method/selective-kernel) unit. In general, all the large kernel convolutions in the original bottleneck blocks in ResNext are replaced by the proposed [SK ... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/spnasnet.mdx | # SPNASNet
**Single-Path NAS** is a novel differentiable NAS method for designing hardware-efficient ConvNets in less than 4 hours.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('spnasnet_100', pretrained=True)
>>> model.eval()
```
To load a... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/ssl-resnet.mdx | # SSL ResNet
**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual b... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/swsl-resnet.mdx | # SWSL ResNet
**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual ... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/swsl-resnext.mdx | # SWSL ResNeXt
A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (the size of the set of transformations)... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/tf-efficientnet-condconv.mdx | # (Tensorflow) EfficientNet CondConv
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method unifo... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/tf-efficientnet-lite.mdx | # (Tensorflow) EfficientNet Lite
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/tf-efficientnet.mdx | # (Tensorflow) EfficientNet
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scal... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/tf-inception-v3.mdx | # (Tensorflow) Inception v3
**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://pape... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/tf-mixnet.mdx | # (Tensorflow) MixNet
**MixNet** is a type of convolutional neural network discovered via AutoML that utilises [MixConvs](https://paperswithcode.com/method/mixconv) instead of regular [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution).
The weights from this model were ported from [Tenso... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/tf-mobilenet-v3.mdx | # (Tensorflow) MobileNet v3
**MobileNetV3** is a convolutional neural network that is designed for mobile phone CPUs. The network design includes the use of a [hard swish activation](https://paperswithcode.com/method/hard-swish) and [squeeze-and-excitation](https://paperswithcode.com/method/squeeze-and-excitation-bloc... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/tresnet.mdx | # TResNet
A **TResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that aim to boost accuracy while maintaining GPU training and inference efficiency. They contain several design tricks including a SpaceToDepth stem, [Anti-Alias downsampling](https://paperswithcode.com/method/anti-alias-down... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/wide-resnet.mdx | # Wide ResNet
**Wide Residual Networks** are a variant on [ResNets](https://paperswithcode.com/method/resnet) where we decrease depth and increase the width of residual networks. This is achieved through the use of [wide residual blocks](https://paperswithcode.com/method/wide-residual-block).
## How do I use this mod... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/xception.mdx | # Xception
**Xception** is a convolutional neural network architecture that relies solely on [depthwise separable convolution layers](https://paperswithcode.com/method/depthwise-separable-convolution).
The weights from this model were ported from [Tensorflow/Models](https://github.com/tensorflow/models).
## How do I... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/reference/data.mdx | # Data
[[autodoc]] timm.data.create_dataset
[[autodoc]] timm.data.create_loader
[[autodoc]] timm.data.create_transform
[[autodoc]] timm.data.resolve_data_config | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/reference/models.mdx | # Models
[[autodoc]] timm.create_model
[[autodoc]] timm.list_models
| 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/reference/optimizers.mdx | # Optimization
This page contains the API reference documentation for learning rate optimizers included in `timm`.
## Optimizers
### Factory functions
[[autodoc]] timm.optim.optim_factory.create_optimizer
[[autodoc]] timm.optim.optim_factory.create_optimizer_v2
### Optimizer Classes
[[autodoc]] timm.optim.adabeli... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/reference/schedulers.mdx | # Learning Rate Schedulers
This page contains the API reference documentation for learning rate schedulers included in `timm`.
## Schedulers
### Factory functions
[[autodoc]] timm.scheduler.scheduler_factory.create_scheduler
[[autodoc]] timm.scheduler.scheduler_factory.create_scheduler_v2
### Scheduler Classes
[[... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/README.md | # Validation and Benchmark Results
This folder contains validation and benchmark results for the models in this collection. Validation scores are currently only run for models with pretrained weights and ImageNet-1k heads, benchmark numbers are run for all.
## Datasets
There are currently results for the ImageNet va... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nchw-pt111-cu113-rtx3090.csv | model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,param_count
tinynet_e,47972.76,21.335,1024,106,2.04
mobilenetv3_small_050,42473.43,24.099,1024,224,1.59
lcnet_035,39739.31,25.756,1024,224,1.64
lcnet_050,35211.0,29.071,1024,224,1.88
mobilenetv3_small_075,31410.3,32.589,1024,224,2.04
mobilenetv... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nchw-pt112-cu113-rtx3090.csv | model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,infer_gmacs,infer_macts,param_count
tinynet_e,49285.12,20.767,1024,106,0.03,0.69,2.04
mobilenetv3_small_050,43905.96,23.312,1024,224,0.03,0.92,1.59
lcnet_035,40961.84,24.988,1024,224,0.03,1.04,1.64
lcnet_050,36451.18,28.081,1024,224,0.05,1.26,1... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nchw-pt113-cu117-rtx3090.csv | model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,infer_gmacs,infer_macts,param_count
tinynet_e,49277.65,20.77,1024,106,0.03,0.69,2.04
mobilenetv3_small_050,45562.75,22.464,1024,224,0.03,0.92,1.59
lcnet_035,41026.68,24.949,1024,224,0.03,1.04,1.64
lcnet_050,37575.13,27.242,1024,224,0.05,1.26,1.... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt111-cu113-rtx3090.csv | model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,param_count
tinynet_e,68298.73,14.982,1024,106,2.04
mobilenetv3_small_050,48773.32,20.985,1024,224,1.59
lcnet_035,47045.94,21.755,1024,224,1.64
lcnet_050,41541.83,24.639,1024,224,1.88
mobilenetv3_small_075,37803.23,27.076,1024,224,2.04
mobilene... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt112-cu113-rtx3090.csv | model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,infer_gmacs,infer_macts,param_count
tinynet_e,70939.06,14.424,1024,106,0.03,0.69,2.04
mobilenetv3_small_050,53363.87,19.179,1024,224,0.03,0.92,1.59
lcnet_035,39908.29,25.648,1024,224,0.03,1.04,1.64
mobilenetv3_small_075,38048.72,26.902,1024,224... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt113-cu117-rtx3090.csv | model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,infer_gmacs,infer_macts,param_count
tinynet_e,72737.62,14.068,1024,106,0.03,0.69,2.04
mobilenetv3_small_050,54822.3,18.668,1024,224,0.03,0.92,1.59
lcnet_035,53629.35,19.084,1024,224,0.03,1.04,1.64
lcnet_050,45492.41,22.499,1024,224,0.05,1.26,1.... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-train-amp-nchw-pt111-cu113-rtx3090.csv | model,train_samples_per_sec,train_step_time,train_batch_size,train_img_size,param_count
tinynet_e,9380.97,53.881,512,106,2.04
mobilenetv3_small_050,7276.68,69.643,512,224,1.59
tf_mobilenetv3_small_minimal_100,6334.14,80.291,512,224,2.04
mobilenetv3_small_075,5920.21,85.765,512,224,2.04
lcnet_035,5760.61,88.397,512,224,... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-train-amp-nchw-pt112-cu113-rtx3090.csv | model,train_samples_per_sec,train_step_time,train_batch_size,train_img_size,param_count
tinynet_e,10001.12,50.423,512,106,2.04
mobilenetv3_small_050,7406.47,68.392,512,224,1.59
tf_mobilenetv3_small_minimal_100,6438.14,78.983,512,224,2.04
mobilenetv3_small_075,6186.83,82.006,512,224,2.04
tf_mobilenetv3_small_075,5783.46... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-train-amp-nhwc-pt111-cu113-rtx3090.csv | model,train_samples_per_sec,train_step_time,train_batch_size,train_img_size,param_count
tinynet_e,10725.36,46.047,512,106,2.04
mobilenetv3_small_050,9864.52,50.786,512,224,1.59
lcnet_035,9593.72,52.888,512,224,1.64
lcnet_050,8283.82,61.296,512,224,1.88
tf_mobilenetv3_small_minimal_100,8178.73,62.055,512,224,2.04
tinyne... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-train-amp-nhwc-pt112-cu113-rtx3090.csv | model,train_samples_per_sec,train_step_time,train_batch_size,train_img_size,param_count
tinynet_e,11915.85,41.681,512,106,2.04
mobilenetv3_small_050,11290.99,44.293,512,224,1.59
lcnet_035,10015.98,50.125,512,224,1.64
lcnet_050,9286.37,54.37,512,224,1.88
tf_mobilenetv3_small_minimal_100,9042.22,55.986,512,224,2.04
mobil... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/generate_csv_results.py | import numpy as np
import pandas as pd
results = {
'results-imagenet.csv': [
'results-imagenet-real.csv',
'results-imagenetv2-matched-frequency.csv',
'results-sketch.csv'
],
'results-imagenet-a-clean.csv': [
'results-imagenet-a.csv',
],
'results-imagenet-r-clean.csv... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/model_metadata-in1k.csv | model,pretrain
adv_inception_v3,in1k-adv
bat_resnext26ts,in1k
beit_base_patch16_224,in21k-selfsl
beit_base_patch16_384,in21k-selfsl
beit_large_patch16_224,in21k-selfsl
beit_large_patch16_384,in21k-selfsl
beit_large_patch16_512,in21k-selfsl
botnet26t_256,in1k
cait_m36_384,in1k-dist
cait_m48_448,in1k-dist
cait_s24_224,in... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-imagenet-a-clean.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,98.930,1.070,99.910,0.090,305.08,448,1.000,bicubic
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,98.850,1.150,99.880,0.120,305.08,448,1.000,bicubic
eva02_large_patch14_448.mim_in22k_ft_in1k,98.... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-imagenet-a.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,88.227,11.773,97.093,2.907,305.08,448,1.000,bicubic,-10.623,-2.787,+1
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,87.920,12.080,96.920,3.080,305.08,448,1.000,bic... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-imagenet-r-clean.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,98.160,1.840,99.880,0.120,305.08,448,1.000,bicubic
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,98.030,1.970,99.890,0.110,305.08,448,1.000,bicubic
eva_giant_patch14_560.m30m_ft_in22k_in1k,98.0... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-imagenet-r.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff
convnext_xxlarge.clip_laion2b_soup_ft_in1k,90.623,9.377,97.913,2.087,846.47,256,1.000,bicubic,-7.127,-1.897,+18
eva_giant_patch14_336.clip_ft_in1k,90.550,9.450,97.230,2.770,"1,013.01",336,1.000,bicubic,-7.310,-2.... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-imagenet-real.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,91.129,8.871,98.713,1.287,305.08,448,1.000,bicubic,+1.077,-0.335,0
eva_giant_patch14_336.clip_ft_in1k,91.058,8.942,98.602,1.399,"1,013.01",336,1.000,bicubic,+1.592,-... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-imagenet.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,90.052,9.948,99.048,0.952,305.08,448,1.000,bicubic
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,89.966,10.034,99.012,0.988,305.08,448,1.000,bicubic
eva_giant_patch14_560.m30m_ft_in22k_in1k,89.... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-imagenetv2-matched-frequency.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff
eva_giant_patch14_336.clip_ft_in1k,82.200,17.800,96.290,3.710,"1,013.01",336,1.000,bicubic,-7.266,-2.536,+6
eva02_large_patch14_448.mim_in22k_ft_in1k,82.130,17.870,96.260,3.740,305.08,448,1.000,bicubic,-7.494,-2.... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-sketch.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff
eva_giant_patch14_336.clip_ft_in1k,71.177,28.823,90.299,9.701,"1,013.01",336,1.000,bicubic,-18.289,-8.527,+6
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,70.662,29.338,89.854,10.146,305.08,448,1.000,bicubic,-19... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/tests/test_layers.py | import torch
import torch.nn as nn
from timm.layers import create_act_layer, set_layer_config
class MLP(nn.Module):
def __init__(self, act_layer="relu", inplace=True):
super(MLP, self).__init__()
self.fc1 = nn.Linear(1000, 100)
self.act = create_act_layer(act_layer, inplace=inplace)
... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/tests/test_models.py | """Run tests for all models
Tests that run on CI should have a specific marker, e.g. @pytest.mark.base. This
marker is used to parallelize the CI runs, with one runner for each marker.
If new tests are added, ensure that they use one of the existing markers
(documented in pyproject.toml > pytest > markers) or that a ... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/tests/test_optim.py | """ Optimzier Tests
These tests were adapted from PyTorch' optimizer tests.
"""
import math
import pytest
import functools
from copy import deepcopy
import torch
from torch.testing._internal.common_utils import TestCase
from torch.autograd import Variable
from timm.scheduler import PlateauLRScheduler
from timm.opti... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/tests/test_utils.py | from torch.nn.modules.batchnorm import BatchNorm2d
from torchvision.ops.misc import FrozenBatchNorm2d
import timm
from timm.utils.model import freeze, unfreeze
def test_freeze_unfreeze():
model = timm.create_model('resnet18')
# Freeze all
freeze(model)
# Check top level module
assert model.fc.we... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/timm/__init__.py | from .version import __version__
from .layers import is_scriptable, is_exportable, set_scriptable, set_exportable
from .models import create_model, list_models, list_pretrained, is_model, list_modules, model_entrypoint, \
is_model_pretrained, get_pretrained_cfg, get_pretrained_cfg_value
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/timm/version.py | __version__ = '0.9.5'
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/__init__.py | from .auto_augment import RandAugment, AutoAugment, rand_augment_ops, auto_augment_policy,\
rand_augment_transform, auto_augment_transform
from .config import resolve_data_config, resolve_model_data_config
from .constants import *
from .dataset import ImageDataset, IterableImageDataset, AugMixDataset
from .dataset_... | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/auto_augment.py | """ AutoAugment, RandAugment, AugMix, and 3-Augment for PyTorch
This code implements the searched ImageNet policies with various tweaks and improvements and
does not include any of the search code.
AA and RA Implementation adapted from:
https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/au... | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/config.py | import logging
from .constants import *
_logger = logging.getLogger(__name__)
def resolve_data_config(
args=None,
pretrained_cfg=None,
model=None,
use_test_size=False,
verbose=False
):
assert model or args or pretrained_cfg, "At least one of model, args, or pretrained_cfg... | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/constants.py | DEFAULT_CROP_PCT = 0.875
DEFAULT_CROP_MODE = 'center'
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5)
IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5)
IMAGENET_DPN_MEAN = (124 / 255, 117 / 255, 104 / 255)
IMAGENET_DPN_STD = tuple([1 / (.0167 *... | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/dataset.py | """ Quick n Simple Image Folder, Tarfile based DataSet
Hacked together by / Copyright 2019, Ross Wightman
"""
import io
import logging
from typing import Optional
import torch
import torch.utils.data as data
from PIL import Image
from .readers import create_reader
_logger = logging.getLogger(__name__)
_ERROR_RETR... | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/dataset_factory.py | """ Dataset Factory
Hacked together by / Copyright 2021, Ross Wightman
"""
import os
from torchvision.datasets import CIFAR100, CIFAR10, MNIST, KMNIST, FashionMNIST, ImageFolder
try:
from torchvision.datasets import Places365
has_places365 = True
except ImportError:
has_places365 = False
try:
from tor... | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/dataset_info.py | from abc import ABC, abstractmethod
from typing import Dict, List, Optional, Union
class DatasetInfo(ABC):
def __init__(self):
pass
@abstractmethod
def num_classes(self):
pass
@abstractmethod
def label_names(self):
pass
@abstractmethod
def label_descriptions(sel... | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/distributed_sampler.py | import math
import torch
from torch.utils.data import Sampler
import torch.distributed as dist
class OrderedDistributedSampler(Sampler):
"""Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with
:class:`torch.nn.parallel.DistributedDataParallel`. In suc... | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/imagenet_info.py | import csv
import os
import pkgutil
import re
from typing import Dict, List, Optional, Union
from .dataset_info import DatasetInfo
# NOTE no ambiguity wrt to mapping from # classes to ImageNet subset so far, but likely to change
_NUM_CLASSES_TO_SUBSET = {
1000: 'imagenet-1k',
11221: 'imagenet-21k-miil', # m... | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/loader.py | """ Loader Factory, Fast Collate, CUDA Prefetcher
Prefetcher and Fast Collate inspired by NVIDIA APEX example at
https://github.com/NVIDIA/apex/commit/d5e2bb4bdeedd27b1dfaf5bb2b24d6c000dee9be#diff-cf86c282ff7fba81fad27a559379d5bf
Hacked together by / Copyright 2019, Ross Wightman
"""
import logging
import random
from... | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/mixup.py | """ Mixup and Cutmix
Papers:
mixup: Beyond Empirical Risk Minimization (https://arxiv.org/abs/1710.09412)
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features (https://arxiv.org/abs/1905.04899)
Code Reference:
CutMix: https://github.com/clovaai/CutMix-PyTorch
Hacked together by / Co... | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/random_erasing.py | """ Random Erasing (Cutout)
Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
Copyright Zhun Zhong & Liang Zheng
Hacked together by / Copyright 2019, Ross Wightman
"""
import random
import math
import torch
def _get_pixels(per_pixel, rand_color, patch_size, dtype=torch.float3... | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/real_labels.py | """ Real labels evaluator for ImageNet
Paper: `Are we done with ImageNet?` - https://arxiv.org/abs/2006.07159
Based on Numpy example at https://github.com/google-research/reassessed-imagenet
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import json
import numpy as np
import pkgutil
class RealLabels... | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/tf_preprocessing.py | """ Tensorflow Preprocessing Adapter
Allows use of Tensorflow preprocessing pipeline in PyTorch Transform
Copyright of original Tensorflow code below.
Hacked together by / Copyright 2020 Ross Wightman
"""
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.... | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/transforms.py | import math
import numbers
import random
import warnings
from typing import List, Sequence
import torch
import torchvision.transforms.functional as F
try:
from torchvision.transforms.functional import InterpolationMode
has_interpolation_mode = True
except ImportError:
has_interpolation_mode = False
from PI... | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/transforms_factory.py | """ Transforms Factory
Factory methods for building image transforms for use with TIMM (PyTorch Image Models)
Hacked together by / Copyright 2019, Ross Wightman
"""
import math
import torch
from torchvision import transforms
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, DEFAULT_CROP_PC... | 0 |
hf_public_repos/pytorch-image-models/timm/data | hf_public_repos/pytorch-image-models/timm/data/_info/imagenet12k_synsets.txt | n00005787
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... | 0 |
hf_public_repos/pytorch-image-models/timm/data | hf_public_repos/pytorch-image-models/timm/data/_info/imagenet21k_goog_synsets.txt | n00004475
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n00441824
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... | 0 |
hf_public_repos/pytorch-image-models/timm/data | hf_public_repos/pytorch-image-models/timm/data/_info/imagenet21k_goog_to_12k_indices.txt | 1
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hf_public_repos/pytorch-image-models/timm/data | hf_public_repos/pytorch-image-models/timm/data/_info/imagenet21k_goog_to_22k_indices.txt | 0
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hf_public_repos/pytorch-image-models/timm/data | hf_public_repos/pytorch-image-models/timm/data/_info/imagenet21k_miil_synsets.txt | n00005787
n00006484
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... | 0 |
hf_public_repos/pytorch-image-models/timm/data | hf_public_repos/pytorch-image-models/timm/data/_info/imagenet21k_miil_w21_synsets.txt | n00005787
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... | 0 |
hf_public_repos/pytorch-image-models/timm/data | hf_public_repos/pytorch-image-models/timm/data/_info/imagenet22k_ms_synsets.txt | n01440764
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... | 0 |
hf_public_repos/pytorch-image-models/timm/data | hf_public_repos/pytorch-image-models/timm/data/_info/imagenet22k_ms_to_12k_indices.txt | 1001
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1076
... | 0 |
hf_public_repos/pytorch-image-models/timm/data | hf_public_repos/pytorch-image-models/timm/data/_info/imagenet22k_ms_to_22k_indices.txt | 1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
... | 0 |
hf_public_repos/pytorch-image-models/timm/data | hf_public_repos/pytorch-image-models/timm/data/_info/imagenet22k_synsets.txt | n00004475
n00005787
n00006024
n00006484
n00007846
n00015388
n00017222
n00021265
n00021939
n00120010
n00141669
n00288000
n00288190
n00288384
n00324978
n00326094
n00433458
n00433661
n00433802
n00434075
n00439826
n00440039
n00440218
n00440382
n00440509
n00440643
n00440747
n00440941
n00441073
n00441824
n00442115
n00442437
... | 0 |
hf_public_repos/pytorch-image-models/timm/data | hf_public_repos/pytorch-image-models/timm/data/_info/imagenet22k_to_12k_indices.txt | 1
3
4
5
6
7
8
9
10
11
13
14
15
16
17
18
19
20
21
23
24
26
27
28
29
30
31
32
33
34
37
38
41
43
44
45
46
47
48
49
50
51
53
55
56
57
58
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
89
90
91
93
94
95
96
97
99
100
101
102
103
105
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121... | 0 |
hf_public_repos/pytorch-image-models/timm/data | hf_public_repos/pytorch-image-models/timm/data/_info/imagenet_a_indices.txt | 6
11
13
15
17
22
23
27
30
37
39
42
47
50
57
70
71
76
79
89
90
94
96
97
99
105
107
108
110
113
124
125
130
132
143
144
150
151
207
234
235
254
277
283
287
291
295
298
301
306
307
308
309
310
311
313
314
315
317
319
323
324
326
327
330
334
335
336
347
361
363
372
378
386
397
400
401
402
404
407
411
416
417
420
425
428
43... | 0 |
hf_public_repos/pytorch-image-models/timm/data | hf_public_repos/pytorch-image-models/timm/data/_info/imagenet_a_synsets.txt | n01498041
n01531178
n01534433
n01558993
n01580077
n01614925
n01616318
n01631663
n01641577
n01669191
n01677366
n01687978
n01694178
n01698640
n01735189
n01770081
n01770393
n01774750
n01784675
n01819313
n01820546
n01833805
n01843383
n01847000
n01855672
n01882714
n01910747
n01914609
n01924916
n01944390
n01985128
n01986214
... | 0 |
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