A study compares vision models on non-standard metrics beyond ImageNet. Models like ConvNet and ViT, trained using supervised and CLIP methods, are examined. Different models show varied strengths, which a single statistic cannot fully measure. This emphasizes the need for new benchmarks and evaluation metrics for precise model selection in specific contexts.
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Comparative Analysis of Vision Models: ConvNet vs. ViT, Supervised vs. CLIP
There has been a significant increase in the complexity of computer vision models, from ConvNets to Vision Transformers. Traditional metrics fall short for real-world vision problems, leading to the need for new evaluation methods beyond ImageNet accuracy.
Research Insights
A study by MBZUAI and Meta AI Research compares the behaviors of four top vision models: ConvNeXt, Vision Transformer (ViT), and CLIP, using supervised and CLIP training methods. The research explores model properties such as prediction errors, generalizability, calibration, and invariances of learned representations.
The primary goal of the study is to provide insights on model qualities that do not require further training, aiding informed decision-making when working with pre-trained models.
Key Findings
Findings show that different models exhibit varied strengths. For example, CLIP models produce fewer classification errors compared to their ImageNet performance, while supervised models demonstrate better performance on ImageNet robustness benchmarks.
The research highlights the importance of new benchmarks and evaluation metrics for precise model selection, emphasizing the need to consider context-specific requirements.
Choosing the Right Vision Model
When selecting a vision model, it’s essential to consider your specific needs and desired task distribution. For tasks similar to ImageNet, the study recommends using supervised ConvNeXt due to its outperformance in numerous benchmarks. However, for significant domain transitions, CLIP models are suggested.
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