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fassabilf/sea-clip-tiny-abl-loss-crd

sourceHugging Facemitupdated 2d agoView on Hugging Face

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SEA-CLIP-Tiny — ablation: CLIP + CRD

One row of the ablation table of SEA-CLIP-Tiny (ACCV 2026). Same architecture, pipeline and hyperparameters as the main model; the difference is the distillation objective: contrastive + contrastive relational distillation only.

Usage

python

import open_clip

model, _, preprocess = open_clip.create_model_and_transforms('hf-hub:fassabilf/sea-clip-tiny-abl-loss-crd')
tokenizer = open_clip.get_tokenizer('hf-hub:fassabilf/sea-clip-tiny-abl-loss-crd')

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	CC12M + CulturalGround-OE-filt + WIT + Bloom + Mammoth-VL-SEA (12.72M pairs)
Teacher	MetaCLIP2-ViT-B-16-worldwide

Results

Retrieval R@1 on the held-out splits of each training source, zero-shot ImageNet accuracy, and the paper's retrieval-only Avg@1 over XM3600, Flickr30k-200 and XTD-200 (%).

CG R@1	WIT R@1	Bloom R@1	ImageNet	R@1-Avg
28.5	20.3	11.3	32.7	9.3

Reproduce

Training and evaluation code: 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}
}