Revisiting Weakly Supervised Pre-Training of Visual Perception Models

Model pre-training is a cornerstone of modern visual recognition systems. Although fully supervised pre-training on datasets like ImageNet is still the de-facto standard, recent studies suggest that large-scale weakly supervised pre-training can outperform fully supervised approaches. This paper revisits weakly-supervised pre-training of models using hashtag supervision with modern versions of residual networks and the largest-ever dataset of images and corresponding hashtags. We study the performance of the resulting models in various transfer-learning settings including zero-shot transfer. We also compare our models with those obtained via large-scale self-supervised learning. We find our weakly-supervised models to be very competitive across all settings, and find they substantially outperform their self-supervised counterparts. We also include an investigation into whether our models learned potentially troubling associations or stereotypes. Overall, our results provide a compelling argument for the use of weakly supervised learning in the development of visual recognition systems. Our models, Supervised Weakly through hashtAGs (SWAG), are available publicly.

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Results from the Paper


 Ranked #1 on Image Classification on Places365-Standard (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Fine-Grained Image Classification CUB-200-2011 SWAG (ViT H/14) Accuracy 91.7 # 3
Image Classification ImageNet SWAG (ViT H/14) Top 1 Accuracy 88.6% # 21
Image Classification ImageNet ReaL SWAG (RegNetY 128GF) Accuracy 90.7% # 10
Image Classification ImageNet V2 SWAG (ViT H/14) Top 1 Accuracy 81.1 # 4
Image Classification iNaturalist 2018 SWAG (ViT H/14) Top-1 Accuracy 86.0% # 3
Image Classification ObjectNet SWAG (ViT H/14) Top-1 Accuracy 69.5 # 4
Image Classification Places365-Standard SWAG (ViT H/14) Top 1 Accuracy 60.7 # 1

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