SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table S17: Detection performance on the Lyft dataset. We report the evaluation results at IoU 0.25. This metric is evaluating "localization", i.e. if there is a bounding box with a tiny overlap with the GT. Our method matches the baselines on this match threshold, and surpasses on the more strict IoU thresholds of 0.5 and 0.7.
mAP IoU 0.25 (↑)
0-30	30-50	50-80	0-80
No Finetuning	63.5	34.9	6.0	37.5
Self-Train. (60 ep)	67.7	43.2	8.7	43.1
Self-Train. (600 ep)	67.7	48.0	13.3	45.5
MODEST (60 ep)	68.5	46.2	10.5	45.1
MODEST (600 ep)	73.6	56.8	21.0	53.6
DRIFT (60ep)	72.3	51.5	19.2	50.7
DRIFT (120 ep)	72.5	51.7	25.8	52.9
Supervised on KITTI	78.6	53.9	26.1	55.3
Supervised on Lyft	81.8	63.6	40.0	64.2
Table S19: Ablation on the number of factors in the class mixture. We test out additional class sizes other than the ground truth number of classes in the label set (car, pedestrian, cyclist, truck). Observe that the number of factors does not significantly affect the performance of DRIFT.
Num. Factors	mAP IoU 0.5	mAP IoU 0.7
0-30	30-50	50-80	0-80	0-30	30-50	50-80	0-80
4 (GT)	60.1	40.2	9.1	38.3	39.0	24.2	3.6	23.1
5	59.8	37.9	7.4	37.0	38.6	24.7	2.9	23.0
6	58.5	38.3	6.1	36.0	39.1	23.9	2.6	22.7
Figure S8: Qualitative visualizations. Additional visualizations of the DRIFT method, as compared to no fine-tuning.