# ResNeSt

**Category:** CNN · **Code:**
[`timm/models/resnest.py`](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/resnest.py) ·
**Architectures:** 8 · **Pretrained weights:** 8 (8 with ImageNet-1k results)

ResNeSt uses split-attention blocks, channel attention across groups of feature map splits, in a ResNet style network.

## Papers

* [ResNeSt: Split-Attention Networks](https://arxiv.org/abs/2004.08955)

## Usage

See the [Quickstart](../quickstart) for loading pretrained weights, image preprocessing and fine-tuning, and [Feature
Extraction](../feature_extraction) for using the models as backbones.

```py
>>> import timm
>>> model = timm.create_model('resnest50d_1s4x24d.in1k', pretrained=True)
>>> data_config = timm.data.resolve_model_data_config(model)
```

## Model API support

| Feature | `ResNet` |
|---|---|
| [`features_only=True` feature maps](../model_api#intermediate-features) | ✓ (strides 2, 4, 8, 16, 32) |
| [`forward_intermediates()`](../model_api#intermediate-features) | ✓ |
| [`prune_intermediate_layers()`](../model_api#intermediate-features) | ✓ |
| [Any input size, no changes needed](../model_api#input-size) | ✓ |
| [`set_input_size()`](../model_api#input-size) | – |
| [`dynamic_img_size=True`](../model_api#input-size) | – |
| [`set_grad_checkpointing()`](../model_api#training-hooks) | ✓ |
| [Layer-wise LR decay (`group_matcher()`)](../model_api#training-hooks) | ✓ |
| [`reset_classifier()`](../model_api#classifier) | ✓ |

✓ supported, ✓ (square) works for square sizes only, ✗ not supported, – not needed (the model already accepts any input
size). Checked by running each feature on a randomly initialized model (`ResNet` with `resnest14d` at 224), other
architectures of the same class are expected to behave the same. See the [Model API](../model_api) page for details.

## Architectures

| Architecture | Params (M) | GMACs | Input size | Pretrained weights |
|---|---:|---:|---:|---:|
| `resnest14d` | 10.61 | 2.76 | 224 | 1 |
| `resnest26d` | 17.07 | 3.64 | 224 | 1 |
| `resnest50d` | 27.48 | 5.40 | 224 | 1 |
| `resnest50d_1s4x24d` | 25.68 | 4.43 | 224 | 1 |
| `resnest50d_4s2x40d` | 30.42 | 4.40 | 224 | 1 |
| `resnest101e` | 48.28 | 13.38 | 256 | 1 |
| `resnest200e` | 70.20 | 35.69 | 320 | 1 |
| `resnest269e` | 110.93 | 77.69 | 416 | 1 |

GMACs and parameter counts are for the default input size and classifier of each architecture, from the [benchmark
results](https://github.com/huggingface/pytorch-image-models/tree/main/results).

## Pretrained weights

Top-1 is ImageNet-1k validation accuracy from
[`results-imagenet.csv`](https://github.com/huggingface/pytorch-image-models/blob/main/results/results-imagenet.csv) at
the listed eval size, - if the weight has no ImageNet-1k result (e.g. pretraining or image-text weights). Classes is the
size of the pretrained classifier, none for weights without one.

| Weight | Input size | Top-1 (%) | Eval size | Classes | License | Hub |
|---|---:|---:|---:|---:|---|---|
| `resnest14d.gluon_in1k` | 224 | 75.516 | 224 | 1000 | apache-2.0 | [timm](https://huggingface.co/timm/resnest14d.gluon_in1k) |
| `resnest26d.gluon_in1k` | 224 | 78.476 | 224 | 1000 | apache-2.0 | [timm](https://huggingface.co/timm/resnest26d.gluon_in1k) |
| `resnest50d.in1k` | 224 | 80.958 | 224 | 1000 | apache-2.0 | [timm](https://huggingface.co/timm/resnest50d.in1k) |
| `resnest50d_1s4x24d.in1k` | 224 | 81.024 | 224 | 1000 | apache-2.0 | [timm](https://huggingface.co/timm/resnest50d_1s4x24d.in1k) |
| `resnest50d_4s2x40d.in1k` | 224 | 81.146 | 224 | 1000 | apache-2.0 | [timm](https://huggingface.co/timm/resnest50d_4s2x40d.in1k) |
| `resnest101e.in1k` | 256 | 82.916 | 256 | 1000 | apache-2.0 | [timm](https://huggingface.co/timm/resnest101e.in1k) |
| `resnest200e.in1k` | 320 | 83.880 | 320 | 1000 | apache-2.0 | [timm](https://huggingface.co/timm/resnest200e.in1k) |
| `resnest269e.in1k` | 416 | 84.542 | 416 | 1000 | apache-2.0 | [timm](https://huggingface.co/timm/resnest269e.in1k) |