Papers › Rethinking ImageNet Pre-training
Rethinking ImageNet Pre-training
Kaiming He, Ross Girshick, Piotr Dollár
We report competitive results on object detection and instance segmentation on the COCO dataset using standard models trained from random initialization. The results are no worse than their ImageNet pre-training counterparts even when using the hyper-parameters of the baseline system (Mask R-CNN) that were optimized for fine-tuning pre-trained models, with the sole exception of increasing the number of training iterations so the randomly initialized models may converge. Training from random initialization is surprisingly robust; our results hold even when: (i) using only 10% of the training data, (ii) for deeper and wider models, and (iii) for multiple tasks and metrics. Experiments show that ImageNet pre-training speeds up convergence early in training, but does not necessarily provide regularization or improve final target task accuracy. To push the envelope we demonstrate 50.9 AP on COCO object detection without using any external data---a result on par with the top COCO 2017 competition results that used ImageNet pre-training. These observations challenge the conventional wisdom of ImageNet pre-training for dependent tasks and we expect these discoveries will encourage people to rethink the current de facto paradigm of `pre-training and fine-tuning' in computer vision.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Object Detection | COCO minival | Mask R-CNN (ResNeXt-152-FPN, cascade) | AP50 | 66.8 | #88 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Mask R-CNN (ResNeXt-152-FPN, cascade) | AP75 | 52.9 | #88 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Mask R-CNN (ResNeXt-152-FPN, cascade) | box AP | 48.6 | #88 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Mask R-CNN (ResNet-101-FPN, GN, Cascade) | box AP | 47.4 | #96 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Mask R-CNN (ResNeXt-152-FPN) | AP50 | 67.1 | #104 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Mask R-CNN (ResNeXt-152-FPN) | AP75 | 51.1 | #104 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Mask R-CNN (ResNeXt-152-FPN) | box AP | 46.4 | #104 of 220 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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