The Battle of the Backbones (BoB) is a large-scale benchmark that compares different pretrained checkpoints and baselines in computer vision. It found that supervised convolutional networks perform better than transformers, while self-supervised models perform better than supervised models on same-sized datasets. ViTs are more sensitive to parameters and pretraining data, and transformers may be more task-dependent than CNNs. CLIP pretraining in vision language improves results. The paper emphasizes the need to constantly evaluate and compare new infrastructures in computer vision. Check out the paper for more details.
Introducing a Comprehensive Analysis of Computer Vision Backbones: Unveiling the Strengths and Weaknesses of Pretrained Models
In the field of computer vision, backbones play a crucial role in deep learning models. They extract features that are essential for tasks like categorization, detection, and segmentation. However, with the increasing number of pretraining strategies and backbone architectures available, it can be challenging for practitioners to choose the ideal backbone for their specific needs.
The Battle of the Backbones (BoB) is a large-scale benchmark developed by researchers from top institutions like New York University, Johns Hopkins University, and Georgia Institute of Technology. It compares popular pretrained checkpoints and baselines on various tasks to provide insights into the merits of different backbone topologies and pretraining strategies.
Key Findings:
- Pretrained supervised convolutional networks generally outperform transformers, likely due to their accessibility and training on larger datasets. However, self-supervised models perform better than supervised models when comparing results across the same-sized datasets.
- ViTs (Vision Transformers) are more sensitive to the number of parameters and pretraining data quantity compared to CNNs (Convolutional Neural Networks). Training ViTs may require more data and processing power. Consider the trade-offs between accuracy, compute cost, and data availability when selecting a backbone architecture.
- The best BoB backbones perform well across a wide range of scenarios, indicating a high degree of correlation between task performance.
- Transformers benefit more from end-to-end tweaking than CNNs in dense prediction jobs. Transformers may be more task- and dataset-dependent.
- CLIP models and other advanced architectures show promise in vision-language modeling. CLIP pretraining outperforms ImageNet-21k supervised trained backbones. Professionals are advised to explore pre-trained backbones available through CLIP.
The BoB benchmark provides a comprehensive analysis of computer vision frameworks. However, the field is constantly evolving with new architectures and pretraining techniques. It is crucial to continuously evaluate and compare new infrastructures to boost performance.
To learn more, check out the Paper. All credit goes to the researchers behind this project. Don’t forget to join our ML SubReddit, Facebook Community, Discord Channel, and Email Newsletter for the latest AI research news and updates.
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