---
license: apache-2.0
datasets:
- ILSVRC/imagenet-1k
metrics:
- accuracy
language:
- en
tags:
- vision
- image-classification
- pytorch_model_hub_mixin
pipeline_tag: image-classification
library_name: PyTorch
model_index:
- name: SpaRTAN-S
  results:
  - task:
      type: image-classification
    dataset:
      type: ILSVRC/imagenet-1k
      name: ImageNet-1k
    metrics:
    - name: top-1 accuracy
      type: accuracy
      value: 82.35
    - name: top-5 accuracy
      type: accuracy
      value: 96.14
---

# SpaRTAN-S

SpaRTAN is a lightweight architectural design which shows consistent efficiency and competitive performance when benchmarked against ImageNet and COCO dataset. It was introduced in the paper [SpaRTAN](https://arxiv.org/abs/2507.10999) and released in this [repo](https://github.com/henry-pay/SpaRTAN). SpaRTAN-S is a scaled-up version of SpaRTAN-T.

# Model Description

SpaRTAN-S shares the same configurations as SpaRTAN-T presented in the paper, [SpaRTAN](https://arxiv.org/abs/2507.10999), except the number of channels at each stage, as outlined below.

| Stage | Channel |
|:---:|:---:|
| S1 | 64 |
| S2 | 128 |
| S3 | 320 |
| S4 | 512 |

# Intended Uses & Limitations

You can use the raw model for image classification. Using as a feature extractor, SpaRTAN-S can be fine-tuned on various downstream tasks including object detection.

# Training Procedure

Same training procedure as outlined in the paper, [SpaRTAN](https://arxiv.org/abs/2507.10999), is used to train this model.

# Evaluation Result

| Model | Resolution | Params (M) | FLOPs (G) | Top-1 (%) | top-5 (%) |
|:---:|:---:|:---:|:---:|:---:|:---:|
| SpaRTAN-S | 224x224 | 18.51 | 3.86 | 82.35 | 96.14 |

# Implementation

Please refer to this [repo](https://github.com/henry-pay/SpaRTAN) for full implementation.

# Citation

```bibtex
@inproceedings{
    title={SpaRTAN: Spatial Reinforcement Token-based Aggregation Network for Visual Recognition},
    author={Pay, Quan Bi and Baskaran, Vishnu Monn and Loo, Junn Yong and Wong, KokSheik and See, Simon},
    booktitle={2025 International Joint Conference on Neural Networks (IJCNN)},
    pages={to appear},
    year={2025},
    organization={IEEE},
    note={Accepted}
}
```