# StarNet

**Category:** Efficient/Mobile · **Code:**
[`timm/models/starnet.py`](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/starnet.py) ·
**Architectures:** 7 · **Pretrained weights:** 4 (4 with ImageNet-1k results)

StarNet is a deliberately simple proof-of-concept network built around the star operation, the element-wise
multiplication of two branches.

## Papers

* [Rewrite the Stars](https://arxiv.org/abs/2403.19967)

## 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('starnet_s4.in1k', pretrained=True)
>>> data_config = timm.data.resolve_model_data_config(model)
```

## Model API support

| Feature | `StarNet` |
|---|---|
| [`features_only=True` feature maps](../model_api#intermediate-features) | ✓ (strides 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 (`StarNet` with `starnet_s050` 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 |
|---|---:|---:|---:|---:|
| `starnet_s1` | 2.87 | 0.42 | 224 | 1 |
| `starnet_s2` | 3.68 | 0.55 | 224 | 1 |
| `starnet_s3` | 5.75 | 0.76 | 224 | 1 |
| `starnet_s4` | 7.48 | 1.05 | 224 | 1 |
| `starnet_s050` | 0.54 | 0.09 | 224 | 0 |
| `starnet_s100` | 1.04 | 0.19 | 224 | 0 |
| `starnet_s150` | 1.56 | 0.23 | 224 | 0 |

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 |
|---|---:|---:|---:|---:|---|---|
| `starnet_s1.in1k` | 224 | 73.532 | 224 | 1000 | apache-2.0 | [timm](https://huggingface.co/timm/starnet_s1.in1k) |
| `starnet_s2.in1k` | 224 | 74.666 | 224 | 1000 | apache-2.0 | [timm](https://huggingface.co/timm/starnet_s2.in1k) |
| `starnet_s3.in1k` | 224 | 77.378 | 224 | 1000 | apache-2.0 | [timm](https://huggingface.co/timm/starnet_s3.in1k) |
| `starnet_s4.in1k` | 224 | 78.824 | 224 | 1000 | apache-2.0 | [timm](https://huggingface.co/timm/starnet_s4.in1k) |