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ThucPD/
vivqa-beit3-routing-C-yolo

Name: vivqa-beit3-routing-C-yolo
Availability: InStock

pytorch

visual-question-answering

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ViVQA-BEiT3 — Expert C (YOLO-augmented)

BEiT-3 base (patch16, 224) finetuned on ViVQA (Vietnamese VQA, 218 answer classes). This is the YOLO-counting-augmented expert from the question-type routing study.

Results (test, 3001 samples)

Total	what/other	counting	color	location
69.58%	72.09	56.53	76.96	66.72

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 yolo (4,228 YOLOv8m counting QA, loss_weight 0.5)

Files

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

Load

python

from huggingface_hub import hf_hub_download
ckpt = hf_hub_download("ThucPD/vivqa-beit3-routing-C-yolo", "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-A-clean.