File size: 1,556 Bytes
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license: mit
library_name: open_clip
pipeline_tag: zero-shot-image-classification
tags:
- clip
- open_clip
- multilingual
- southeast-asia
language:
- en
- id
- jv
- su
- ms
- th
- vi
- my
---
# SEA-CLIP-Tiny — English initialisation (e0)
The English CLIP-KD student checkpoint used to initialise every SEA-CLIP-Tiny run in
[SEA-CLIP-Tiny (ACCV 2026)](https://huggingface.co/collections/fassabilf/sea-clip-tiny-accv-2026-6ab15f4d6cf7d50e98d5117c). It is the *starting point*, not a trained SEA model:
no Southeast Asian data has been seen at this point.
## Usage
```python
import open_clip
model, _, preprocess = open_clip.create_model_and_transforms('hf-hub:fassabilf/sea-clip-tiny-init')
tokenizer = open_clip.get_tokenizer('hf-hub:fassabilf/sea-clip-tiny-init')
```
## Model
| | |
|---|---|
| Architecture | ViT-T/16 vision tower + 12-layer / 384-wide text tower, embed dim 512 |
| Tokenizer | CLIP BPE, vocab 49408, context length 77 |
| Parameters | 46.11M (5.62M vision + 40.49M text) |
| Training data | English CLIP-KD pretraining (no SEA data) |
| Teacher | — |
## Reproduce
Training and evaluation code: [https://github.com/fassabilf/sea-clip-tiny](https://github.com/fassabilf/sea-clip-tiny).
The exact training configuration of this checkpoint is in `params.txt` in this repo.
## Citation
```bibtex
@inproceedings{seacliptiny2026,
title = {SEA-CLIP-Tiny: Efficient Multilingual Text-Vision Embedding for Southeast Asian Languages},
booktitle = {Asian Conference on Computer Vision (ACCV)},
year = {2026}
}
```
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