# CSATv2

**Category:** Hybrid · **Code:**
[`timm/models/csatv2.py`](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/csatv2.py) ·
**Architectures:** 2 · **Pretrained weights:** 3 (0 with ImageNet-1k results)

CSATv2 is a frequency-domain vision model that works on DCT transforms of the input image, with spatial attention.

## Papers

The module docstring does not reference any papers.

## 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('csatv2.r512_in1k', pretrained=True)
>>> data_config = timm.data.resolve_model_data_config(model)
```

## Model API support

| Feature | `CSATv2` |
|---|---|
| [`features_only=True` feature maps](../model_api#intermediate-features) | ✓ (strides 8, 16, 32, 64) |
| [`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 (`CSATv2` with `csatv2` at 256), 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 |
|---|---:|---:|---:|---:|
| `csatv2` | 11.10 | 1.39 | 512 | 1 |
| `csatv2_21m` | 20.70 | 4.72 | 640 | 2 |

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 |
|---|---:|---:|---:|---:|---|---|
| `csatv2.r512_in1k` | 512 | - | - | 1000 | - | [timm](https://huggingface.co/timm/csatv2.r512_in1k) |
| `csatv2_21m.sw_r512_in1k` | 512 | - | - | 1000 | - | [timm](https://huggingface.co/timm/csatv2_21m.sw_r512_in1k) |
| `csatv2_21m.sw_r640_in1k` | 640 | - | - | 1000 | - | [timm](https://huggingface.co/timm/csatv2_21m.sw_r640_in1k) |