repo_id stringlengths 15 89 | file_path stringlengths 27 180 | content stringlengths 1 2.23M | __index_level_0__ int64 0 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,-7.492,-2.... | 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-infer-amp-nhwc-pt210-cu121-rtx3090.csv | model,infer_img_size,infer_batch_size,infer_samples_per_sec,infer_step_time,infer_gmacs,infer_macts,param_count
tinynet_e,106,1024.0,75290.96,13.591,0.03,0.69,2.04
mobilenetv3_small_050,224,1024.0,56785.93,18.023,0.03,0.92,1.59
efficientvit_m0,224,1024.0,50656.23,20.205,0.08,0.91,2.35
lcnet_035,224,1024.0,48853.22,20.9... | 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/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/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,98.150,1.850,99.880,0.120,305.08,448,1.000,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/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-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/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,89.970,10.030,99.012,0.988,305.08,448,1.000,bicubic
eva_giant_patch14_560.m30m_ft_in22k_in1k,89.... | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nchw-pt210-cu121-rtx3090.csv | model,infer_img_size,infer_batch_size,infer_samples_per_sec,infer_step_time,infer_gmacs,infer_macts,param_count
tinynet_e,106,1024.0,50604.03,20.225,0.03,0.69,2.04
mobilenetv3_small_050,224,1024.0,46069.42,22.217,0.03,0.92,1.59
lcnet_035,224,1024.0,41190.64,24.85,0.03,1.04,1.64
lcnet_050,224,1024.0,37663.82,27.178,0.05... | 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,70.662,29.338,89.856,10.144,305.08,448,1.000,bicubic,-19... | 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/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/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-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/hfdocs/README.md | # Hugging Face Timm Docs
## Getting Started
```
pip install git+https://github.com/huggingface/doc-builder.git@main#egg=hf-doc-builder
pip install watchdog black
```
## Preview the Docs Locally
```
doc-builder preview timm hfdocs/source
```
| 0 |
hf_public_repos/pytorch-image-models/hfdocs | hf_public_repos/pytorch-image-models/hfdocs/source/training_script.mdx | # Scripts
A train, validation, inference, and checkpoint cleaning script included in the github root folder. Scripts are not currently packaged in the pip release.
The training and validation scripts evolved from early versions of the [PyTorch Imagenet Examples](https://github.com/pytorch/examples). I have added sign... | 0 |
hf_public_repos/pytorch-image-models/hfdocs | hf_public_repos/pytorch-image-models/hfdocs/source/installation.mdx | # Installation
Before you start, you'll need to setup your environment and install the appropriate packages. `timm` is tested on **Python 3+**.
## Virtual Environment
You should install `timm` in a [virtual environment](https://docs.python.org/3/library/venv.html) to keep things tidy and avoid dependency conflicts.
... | 0 |
hf_public_repos/pytorch-image-models/hfdocs | hf_public_repos/pytorch-image-models/hfdocs/source/hf_hub.mdx | # Sharing and Loading Models From the Hugging Face Hub
The `timm` library has a built-in integration with the Hugging Face Hub, making it easy to share and load models from the 🤗 Hub.
In this short guide, we'll see how to:
1. Share a `timm` model on the Hub
2. How to load that model back from the Hub
## Authent... | 0 |
hf_public_repos/pytorch-image-models/hfdocs | hf_public_repos/pytorch-image-models/hfdocs/source/index.mdx | # timm
<img class="float-left !m-0 !border-0 !dark:border-0 !shadow-none !max-w-lg w-[150px]" src="https://huggingface.co/front/thumbnails/docs/timm.png"/>
`timm` is a library containing SOTA computer vision models, layers, utilities, optimizers, schedulers, data-loaders, augmentations, and training/evaluation script... | 0 |
hf_public_repos/pytorch-image-models/hfdocs | hf_public_repos/pytorch-image-models/hfdocs/source/models.mdx | # Model Summaries
The model architectures included come from a wide variety of sources. Sources, including papers, original impl ("reference code") that I rewrote / adapted, and PyTorch impl that I leveraged directly ("code") are listed below.
Most included models have pretrained weights. The weights are either:
1. ... | 0 |
hf_public_repos/pytorch-image-models/hfdocs | hf_public_repos/pytorch-image-models/hfdocs/source/feature_extraction.mdx | # Feature Extraction
All of the models in `timm` have consistent mechanisms for obtaining various types of features from the model for tasks besides classification.
## Penultimate Layer Features (Pre-Classifier Features)
The features from the penultimate model layer can be obtained in several ways without requiring ... | 0 |
hf_public_repos/pytorch-image-models/hfdocs | hf_public_repos/pytorch-image-models/hfdocs/source/_toctree.yml | - sections:
- local: index
title: Home
- local: quickstart
title: Quickstart
- local: installation
title: Installation
title: Get started
- sections:
- local: feature_extraction
title: Using Pretrained Models as Feature Extractors
- local: training_script
title: Training With The Offici... | 0 |
hf_public_repos/pytorch-image-models/hfdocs | hf_public_repos/pytorch-image-models/hfdocs/source/results.mdx | # Results
CSV files containing an ImageNet-1K and out-of-distribution (OOD) test set validation results for all models with pretrained weights is located in the repository [results folder](https://github.com/rwightman/pytorch-image-models/tree/master/results).
## Self-trained Weights
The table below includes ImageNe... | 0 |
hf_public_repos/pytorch-image-models/hfdocs | hf_public_repos/pytorch-image-models/hfdocs/source/quickstart.mdx | # Quickstart
This quickstart is intended for developers who are ready to dive into the code and see an example of how to integrate `timm` into their model training workflow.
First, you'll need to install `timm`. For more information on installation, see [Installation](installation).
```bash
pip install timm
```
## ... | 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/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/gloun-senet.mdx | # (Gluon) 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](http... | 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/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 mode... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/efficientnet.mdx | # 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 scales network wi... | 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/ensemble-adversarial.mdx | # # Ensemble Adversarial 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 arch... | 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/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 mode... | 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/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/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}... | 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/dpn.mdx | # Dual Path Network (DPN)
A **Dual Path Network (DPN)** is a convolutional neural network which presents a new topology of connection paths internally. The intuition is that [ResNets](https://paperswithcode.com/method/resnet) enables feature re-usage while DenseNet enables new feature exploration, and both are importa... | 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/hrnet.mdx | # HRNet
**HRNet**, or **High-Resolution Net**, is a general purpose convolutional neural network for tasks like semantic segmentation, object detection and image classification. It is able to maintain high resolution representations through the whole process. We start from a high-resolution convolution stream, gradual... | 0 |
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/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/ese-vovnet.mdx | # ESE-VoVNet
**VoVNet** is a convolutional neural network that seeks to make [DenseNet](https://paperswithcode.com/method/densenet) more efficient by concatenating all features only once in the last feature map, which makes input size constant and enables enlarging new output channel.
Read about [one-shot aggregatio... | 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/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/advprop.mdx | # AdvProp (EfficientNet)
**AdvProp** is an adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting. Key to the method is the usage of a separate auxiliary batch norm for adversarial examples, as they have different underlying distributions to normal examples.
The w... | 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/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/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/densenet.mdx | # DenseNet
**DenseNet** is a type of convolutional neural network that utilises dense connections between layers, through [Dense Blocks](http://www.paperswithcode.com/method/dense-block), where we connect *all layers* (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each... | 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/ecaresnet.mdx | # ECA-ResNet
An **ECA ResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that utilises an [Efficient Channel Attention module](https://paperswithcode.com/method/efficient-channel-attention). Efficient Channel Attention is an architectural unit based on [squeeze-and-excitation blocks](https:/... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/csp-resnext.mdx | # CSP-ResNeXt
**CSPResNeXt** is a convolutional neural network where we apply the Cross Stage Partial Network (CSPNet) approach to [ResNeXt](https://paperswithcode.com/method/resnext). The CSPNet partitions the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use o... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/gloun-xception.mdx | # (Gluon) Xception
**Xception** is a convolutional neural network architecture that relies solely on [depthwise separable convolution](https://paperswithcode.com/method/depthwise-separable-convolution) layers.
The weights from this model were ported from [Gluon](https://cv.gluon.ai/model_zoo/classification.html).
##... | 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/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/csp-darknet.mdx | # CSP-DarkNet
**CSPDarknet53** is a convolutional neural network and backbone for object detection that uses [DarkNet-53](https://paperswithcode.com/method/darknet-53). It employs a CSPNet strategy to partition the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The u... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/dla.mdx | # Deep Layer Aggregation
Extending “shallow” skip connections, **Dense Layer Aggregation (DLA)** incorporates more depth and sharing. The authors introduce two structures for deep layer aggregation (DLA): iterative deep aggregation (IDA) and hierarchical deep aggregation (HDA). These structures are expressed through ... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/gloun-resnext.mdx | # (Gluon) 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 transformatio... | 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/gloun-seresnext.mdx | # (Gluon) 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.
The weights from this... | 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/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/efficientnet-pruned.mdx | # EfficientNet (Knapsack Pruned)
**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/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/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/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/ig-resnext.mdx | # Instagram ResNeXt WSL
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 transfo... | 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/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/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/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/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/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/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/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/adversarial-inception-v3.mdx | # Adversarial 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://paper... | 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) \\( ... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/gloun-resnet.mdx | # (Gluon) 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 [residu... | 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/fbnet.mdx | # FBNet
**FBNet** is a type of convolutional neural architectures discovered through [DNAS](https://paperswithcode.com/method/dnas) neural architecture search. It utilises a basic type of image model block inspired by [MobileNetv2](https://paperswithcode.com/method/mobilenetv2) that utilises depthwise convolutions and... | 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/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/csp-resnet.mdx | # CSP-ResNet
**CSPResNet** is a convolutional neural network where we apply the Cross Stage Partial Network (CSPNet) approach to [ResNet](https://paperswithcode.com/method/resnet). The CSPNet partitions the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a ... | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/big-transfer.mdx | # Big Transfer (BiT)
**Big Transfer (BiT)** is a type of pretraining recipe that pre-trains on a large supervised source dataset, and fine-tunes the weights on the target task. Models are trained on the JFT-300M dataset. The finetuned models contained in this collection are finetuned on ImageNet.
## How do I use thi... | 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/gloun-inception-v3.mdx | # (Gluon) 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://paperswit... | 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__ = '0.9.14dev0'
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/scheduler/poly_lr.py | """ Polynomial Scheduler
Polynomial LR schedule with warmup, noise.
Hacked together by / Copyright 2021 Ross Wightman
"""
import math
import logging
import torch
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
class PolyLRScheduler(Scheduler):
""" Polynomial LR Scheduler w/ warmup, no... | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/scheduler/scheduler_factory.py | """ Scheduler Factory
Hacked together by / Copyright 2021 Ross Wightman
"""
from typing import List, Optional, Union
from torch.optim import Optimizer
from .cosine_lr import CosineLRScheduler
from .multistep_lr import MultiStepLRScheduler
from .plateau_lr import PlateauLRScheduler
from .poly_lr import PolyLRScheduler... | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/scheduler/tanh_lr.py | """ TanH Scheduler
TanH schedule with warmup, cycle/restarts, noise.
Hacked together by / Copyright 2021 Ross Wightman
"""
import logging
import math
import numpy as np
import torch
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
class TanhLRScheduler(Scheduler):
"""
Hyberbolic-Tan... | 0 |
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