How to Choose the Right Vision Model for Your Specific Needs: Beyond ImageNet Accuracy – A Comparative Analysis of Convolutional Neural Networks and Vision Transformer Architectures

There was a dramatic enhance within the complexity of the pc imaginative and prescient mannequin panorama. Many fashions at the moment are at your fingertips, from the primary ConvNets to the most recent Imaginative and prescient Transformers. Simply as supervised studying on ImageNet gave solution to self-supervised studying and image-text pair coaching, similar to CLIP, so did coaching paradigms.

In comparison with ResNets, CLIP’s visible encoder is way extra resilient and transferable, although it has comparable ImageNet accuracy. Due to this, research have begun to analyze and broaden upon CLIP’s distinct benefits, which weren’t obvious when utilizing the ImageNet metric alone. This proves that a number of attributes might result in discovering sensible fashions.

Along with fundamental analysis, an in-depth data of the behaviors of imaginative and prescient fashions is required as a consequence of their growing incorporation into manufacturing techniques. Conventional metrics fall brief with regards to real-world imaginative and prescient issues, similar to completely different digital camera postures, lighting situations, or occlusions.

To fill this hole, a brand new examine by MBZUAI and Meta AI Analysis investigates mannequin traits past ImageNet correctness. The researchers look at 4 prime fashions in pc imaginative and prescient: ConvNeXt, which stands for ConvNet, and Imaginative and prescient Transformer (ViT), all educated utilizing supervised and CLIP strategies. All the chosen fashions are comparable in parameter counts and ImageNet-1K accuracy throughout all coaching paradigms, guaranteeing an goal comparability. Kinds of prediction errors, generalizability, calibration, invariances of the discovered representations, and plenty of extra mannequin properties are explored on this work.

The staff’s main objective was to make clear the mannequin’s intrinsic qualities that don’t require additional coaching or fine-tuning in order that practitioners could make knowledgeable selections when working with pre-trained fashions. They discover that varied topologies and coaching paradigms exhibit considerably diverse mannequin behaviors. When in comparison with their ImageNet efficiency, CLIP fashions, as an illustration, produce fewer classification errors. Alternatively, with regards to ImageNet robustness benchmarks, supervised fashions are likely to carry out higher and have higher calibration. In comparison with ViT, ConvNeXt is extra texture-biased, though it excels on artificial information.

Moreover, the researchers found that supervised ConvNeXt outperforms CLIP fashions relating to transferability and performs fairly nicely on quite a few benchmarks. These outcomes present that completely different fashions present their strengths otherwise and {that a} single statistic can’t adequately measure these variations. The findings spotlight the significance of creating new, unbiased benchmarks and extra complete analysis metrics for exact, context-specific mannequin choice.

When the specified job distribution is much like ImageNet, the staff chooses supervised ConvNeXt as a result of it outperforms quite a few benchmarks. They suggest using CLIP fashions within the occasion of a major area transition.

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