RESEARCH

COMPUTER VISION

Rethinking ImageNet Pre-training

October 27, 2019

Abstract

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 pretrained 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 ‘pretraining and fine-tuning’ in computer vision.

Download the Paper

AUTHORS

Written by

Kaiming He

Piotr Dollar

Ross Girshick

Publisher

ICCV

Research Topics

Computer Vision

Related Publications

March 20, 2024

COMPUTER VISION

SceneScript: Reconstructing Scenes With An Autoregressive Structured Language Model

Armen Avetisyan, Chris Xie, Henry Howard-Jenkins, Tsun-Yi Yang, Samir Aroudj, Suvam Patra, Fuyang Zhang, Duncan Frost, Luke Holland, Campbell Orme, Jakob Julian Engel, Edward Miller, Richard Newcombe, Vasileios Balntas

March 20, 2024

February 13, 2024

GRAPHICS

COMPUTER VISION

IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation

Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, Natalia Neverova, Andrea Vedaldi, Oran Gafni, Filippos Kokkinos

February 13, 2024

January 25, 2024

COMPUTER VISION

LRR: Language-Driven Resamplable Continuous Representation against Adversarial Tracking Attacks

Felix Xu, Di Lin, Jianjun Zhao, Jianlang Chen, Lei Ma, Qing Guo, Wei Feng, Xuhong Ren

January 25, 2024

December 08, 2023

COMPUTER VISION

Learning Fine-grained View-Invariant Representations from Unpaired Ego-Exo Videos via Temporal Alignment

Sherry Xue, Kristen Grauman

December 08, 2023

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.