---
license: apache-2.0
language:
- en
tags:
- remote-sensing
- earth-observation
- self-supervised-learning
- semantic-segmentation
- feature-extraction
- vision
- s5
- s4p
- vit
- transformers
library_name: transformers
pipeline_tag: feature-extraction
---

# S5 Transformers Models

Hugging Face–compatible checkpoints converted from the official [S5](https://arxiv.org/abs/2508.12409) S4P pretrain weights. Each subfolder is a standalone model repo layout (`config.json`, `model.safetensors`, preprocessor, and remote code) for **encoder feature extraction** on optical remote sensing imagery.

## Model Description

These are ViT encoders pretrained with **S4P** (Semi-supervised Semantic Segmentation Pre-training) on [RS4P-1M](https://huggingface.co/datasets/lianglyu/R4P-1M). This collection currently bundles **2 converted backbone checkpoints**:

- **ViT-B:** ViT-Base/16, hidden size 768, 12 layers
- **ViT-L:** ViT-Large/16, hidden size 1024, 24 layers

Both checkpoints use `architecture: s4p_backbone` and expose the `s5-feature-extraction` pipeline. They are **encoder-only** weights (not UPerNet segmentation or MoE-MDF heads).

All folders ship self-contained remote code (`modeling_s5.py`, processor, pipeline) and load with `trust_remote_code=True`.

**Developed by:** [lianglyu / S5](https://huggingface.co/lianglyu/S5)  
**Converted for Hugging Face by:** BiliSakura  
**License (weights):** Apache 2.0  
**Original paper:** [S5: Scalable Semi-Supervised Semantic Segmentation in Remote Sensing](https://arxiv.org/abs/2508.12409) (AAAI 2026 Oral)

## Available checkpoints

| Folder | Backbone | Hidden size | Layers | Heads | Patch | Image size | Original file |
|--------|----------|-------------|--------|-------|-------|------------|---------------|
| `ViT-B` | ViT-Base | 768 | 12 | 12 | 16 | 512 | `vit_b_s4p.pth` |
| `ViT-L` | ViT-Large | 1024 | 24 | 16 | 16 | 512 | `vit_l_s4p.pth` |

Original singular `.pth` files were converted and removed from this directory.

## Usage

Processors default to **`do_resize: false`**. Pass RGB images at native resolution; ImageNet mean/std normalization is applied when enabled.

```python
from transformers import pipeline
import numpy as np

REPO = "/path/to/S5-transformers"

pipe = pipeline(
    task="s5-feature-extraction",
    model=f"{REPO}/ViT-B",
    trust_remote_code=True,
)

image = np.random.randint(0, 255, (512, 512, 3), dtype=np.uint8)

# Global pooled features
features = pipe(image, pool=True, return_tensors=True)
print(features.shape)  # [1, 768] for ViT-B, [1, 1024] for ViT-L

# Dense feature map
featmap = pipe(image, pool=False, return_tensors=True)
print(featmap.shape)   # [1, 768, 32, 32] for ViT-B, [1, 1024, 32, 32] for ViT-L
```

ViT-L:

```python
pipe = pipeline(
    task="s5-feature-extraction",
    model=f"{REPO}/ViT-L",
    trust_remote_code=True,
)
features = pipe(image, pool=True, return_tensors=True)
print(features.shape)  # [1, 1024]
```

To force 512×512 resize:

```python
features = pipe(
    image,
    pool=True,
    return_tensors=True,
    image_processor_kwargs={"do_resize": True},
)
```

Load components directly:

```python
from transformers import AutoModel, AutoImageProcessor

model = AutoModel.from_pretrained(f"{REPO}/ViT-B", trust_remote_code=True)
processor = AutoImageProcessor.from_pretrained(f"{REPO}/ViT-B", trust_remote_code=True)
```

## Normalization

The bundled image processor applies ImageNet mean/std normalization by default (`do_normalize=True`, `rescale_factor=1/255`). Inputs should be RGB optical imagery.

## Conversion

Checkpoints were converted with [`scripts/convert_s5_checkpoint.py`](https://github.com/lianglyu/S5-transformers) from the official release:

```bash
python scripts/convert_s5_checkpoint.py \
  --input-path /path/to/vit_b_s4p.pth \
  --output-dir /path/to/ViT-B \
  --clean-output
```

For semantic segmentation heads, convert `vit_*_s4p_upernet.pth` or `s5_vit_*_moe_mdf_seg.pth` instead (task: `s5-semantic-segmentation`).

## Dependencies

- `transformers>=4.45.0`
- `torch>=2.1.0`
- `safetensors`
- `Pillow`
- `numpy`

## Citation

```bibtex
@article{S5,
  title={S5: Scalable Semi-Supervised Semantic Segmentation in Remote Sensing},
  author={Liang Lv and Di Wang and Jing Zhang and Lefei Zhang},
  journal={arXiv preprint arXiv:2508.12409},
  year={2025}
}
```