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BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training
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| 2026-07-24 | 399 |
| 2026-07-25 | 399 |
| 2026-07-28 | 399 |
| 2026-07-30 | 399 |
| 2026-08-06 | 399 |
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# BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training
By [Likun Cai](https://github.com/cailk), Zhi Zhang, Yi Zhu, Li Zhang, Mu Li, Xiangyang Xue.
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<img src="./resources/bigdetection.png" height="250px" />
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This repo is the official implementation of [BigDetection](https://arxiv.org/abs/2203.13249). It is based on [mmdetection](https://github.com/open-mmlab/mmdetection) and [CBNetV2](https://github.com/VDIGPKU/CBNetV2).
## Introduction
We construct a new large-scale benchmark termed *BigDetection*. Our goal is to simply leverage the training data from existing datasets ([LVIS](https://www.lvisdataset.org/), [OpenImages](https://storage.googleapis.com/openimages/web/index.html) and [Object365](https://www.objects365.org/overview.html)) with carefully designed principles, and curate a larger dataset for improved detector pre-training. BigDetection dataset has 600 object categories and contains 3.4M training images with 36M object bounding boxes. We show some important statistics of BigDetection in the following figure.
*Left*: Number of images per category of BigDetection. *Right*: Number of instances in different object sizes.
## Results and Models
### BigDetection Validation
We show the evaluation results on BigDetection Validation. We hope BigDetection could serve as a new challenging benchmark for evaluating next-level object detection methods.
| Method | mAP (bigdet val) | Links |
| --- | :---: | :---: |
| YOLOv3 | 9.7 | [model](https://big-detection.s3.us-west-2.amazonaws.com/bigdet_cpts/mmdetection_cpts/yolov3_d53_bigdet_8x.pth)/[config](configs/BigDetection/yolov3/yolov3_d53_mstrain-608_8x_bigdet.py) |
| Deformable DETR | 13.1 | [model](https://big-detection.s3.us-west-2.amazonaws.com/bigdet_cpts/mmdetection_cpts/deformable_detr_bigdet_8x.pth)/[config](configs/BigDetection/deformable_detr/deformable_detr_r50_16x2_8x_bigdet.py) |
| Faster R-CNN (C4)\* | 18.9 | [model](https://big-detection.s3.us-west-2.amazonaws.com/bigdet_cpts/detectron2_cpts/faster_rcnn_r50_c4_bigdet_8x.pth) |
| Faster R-CNN (FPN)\* | 19.4 | [model](https://big-detection.s3.us-west-2.amazonaws.com/bigdet_cpts/detectron2_cpts/faster_rcnn_r50_fpn_bigdet_8x.pth) |
| CenterNet2\* | 23.1 | [model](https://big-detection.s3.us-west-2.amazonaws.com/bigdet_cpts/detectron2_cpts/centernet2_r50_bigdet_8x.pth) |
| Cascade R-CNN\* | 24.1 | [model](https://big-detection.s3.us-west-2.amazonaws.com/bigdet_cpts/detectron2_cpts/crcnn_r50_bigdet_8x.pth) |
| CBNetV2-Swin-Base | 35.1 | [model](https://big-detection.s3.us-west-2.amazonaws.com/bigdet_cpts/mmdetection_cpts/htc_cbv2_swin_base_giou_4conv1f_bigdet.pth)/[config](configs/BigDetection/cbnetv2/htc_cbv2_swin_base_giou_4conv1f_adamw_bigdet.py) |
### COCO Validation
We show the finetuning performance on COCO minival/test-dev. Results show that BigDetection pre-training provides significant benefits for different detector architectures. We achieve 59.8 mAP on COCO test-dev with a single model.
| Method | mAP (coco minival/test-dev) | Links |
| --- | :---: | :---: |
| YOLOv3 | 30.5/- | [config](configs/BigDetection/yolov3/yolov3_d53_mstrain-608_8x_bigdet.py) |
| Deformable DETR | 39.9/- | [model](https://big-detection.s3.us-west-2.amazonaws.com/bigdet_cpts/mmdetection_cpts/deformable_detr_bigdet_coco-ft_1x.pth)/[config](configs/BigDetection/deformable_detr/deformable_detr_r50_16x2_8x_bigdet.py) |
| Faster R-CNN (C4)\* | 38.8/- | [model](https://big-detection.s3.us-west-2.amazonaws.com/bigdet_cpts/detectron2_cpts/faster_rcnn_r50_c4_bigdet_coco-ft_1x.pth) |
| Faster R-CNN (FPN)\* | 40.5/- | [model](https://big-detection.s3.us-west-2.amazonaws.com/bigdet_cpts/detectron2_cpts/faster_rcnn_r50_fpn_bigdet_coco-ft_1x.pth) |
| CenterNet2\* | 45.3/- | [model](https://big-detection.s3.us-west-2.amazonaws.com/bigdet_cpts/detectron2_cpts/centernet2_r50_bigdet_coco-ft_1x.pth) |
| Cascade Excerpt of 10,956 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:dd08358a75dad6e9, topic:computer-vision, topic:object-detection, readme:object detection
matched fp:dd08358a75dad6e9, topic:pretraining, desc:pre-training, readme:pre-training