README.md

metadata

license: mit
library_name: pytorch
tags:
  - visual-question-answering
  - vivqa
  - beit3
  - vietnamese
  - multimodal
language:
  - vi

ViVQA-BEiT3 — Expert A (clean baseline)

BEiT-3 base (patch16, 224) finetuned on ViVQA (Vietnamese VQA, 218 answer classes). This is the clean baseline expert (no augmentation) from the question-type routing study.

Results (test, 3001 samples)

Total	what/other	counting	color	location
70.21%	71.53	61.26	78.40	66.13

Training

• Init: beit3_base_indomain_patch16_224, XLM-R (beit3.spm) tokenization
• 30 epochs, lr 5e-5, layer_decay 0.8, batch 64, seed 42, --clip_grad 1.0, --randaug
• --augmented none

Files

• checkpoint-best.pth — best-val checkpoint (model key in state_dict)

Load

from huggingface_hub import hf_hub_download
ckpt = hf_hub_download("ThucPD/vivqa-beit3-routing-A-clean", "checkpoint-best.pth")
# use with BEiT-3 run_beit3_finetuning.py --model beit3_base_patch16_224 --finetune <ckpt>

See the routing study: per-type augmentation effects reproduce but are sub-noise; this clean baseline is the robust optimum. Paired with the YOLO-augmented expert ThucPD/vivqa-beit3-routing-C-yolo.