|
|
--- |
|
|
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} |
|
|
} |
|
|
``` |
|
|
|
