Unveiling the Ultimate Model Winner: ConvNet vs Vision Transformers

Unveiling the Ultimate Model Winner: ConvNet vs Vision Transformers

Table of Contents

1. Introduction
2. Background on Computer Vision Landscape
3. Need for Performance Metrics beyond ImageNet Accuracy
4. Comparison of Conet and Transformers
   1. Parameter Counts and ImageNet Accuracy
   2. Design Choices of the Study
5. Understanding Model Mistakes
   1. Analysis of ImageNet X Dataset
   2. Error Ratios for Texture and Shape Factors
   3. Differences between Supervised and Clip Training
6. Assessing Shape and Textural Bias
   1. Metrics for Shape and Texture Favoring
   2. Comparing Vits and Clip Models
7. Calibration of Models
   1. Confidence and Reliability
   2. Comparing Clip and Supervised Models
8. Robustness and Transferability of Models
   1. Adapting to Distribution Shifts and Perturbations
   2. Performance of Conex and Vit Models
9. Performance on Synthetic Data
   1. Control Experiment with Pug ImageNet Synthetic Data
   2. Comparison of Conex and Vit Models
10. Discussions and Critiques of the Study
11. Conclusion
12. Resources

🔍 Introduction

Recently, an exciting paper comparing Conet and Transformers in terms of supervised and clip-based training paradigms stirred up the computer vision community. The authors aimed to explore the performance of various computer vision models on different metrics beyond the traditional ImageNet accuracy. This article will dive into the findings of the study and discuss the implications for model selection and understanding model behavior.

🌆 Background on Computer Vision Landscape

The computer vision landscape has grown increasingly complex with the emergence of various architectures and training paradigms. From the numerous Conet variants to the rising popularity of vision Transformers, researchers now face the challenge of determining which models to use and why. Traditionally, the ImageNet accuracy metric has been the benchmark for model evaluation. However, this metric has its limitations and fails to consider other important aspects such as training data, architectural paradigms, and model robustness.

💡 Need for Performance Metrics beyond ImageNet Accuracy

To address the limitations of solely relying on ImageNet accuracy, the authors of the paper proposed the use of additional metrics to gain deeper insights into model performance, strengths, and weaknesses. By considering an array of metrics, they aimed to understand the nuances of different models and investigate factors such as shape biases, mistakes, calibration, robustness, and transferability.

🔁 Comparison of Conet and Transformers

In order to conduct a fair comparison, the study selected Conet and vision Transformers (Vits) as representative models due to their similar parameter counts and accuracy on ImageNet. They also examined both supervised and clip-based training approaches. The paper focused on assessing pre-trained models "out of the box" without additional fine-tuning to provide a comprehensive understanding of their performance.

🔍 Understanding Model Mistakes

The research began by analyzing the performance of Conet and Transformers models on the ImageNet X dataset, which features 16 factors of annotations. To measure the relative performance of each factor, the authors introduced the error ratio. Texture was found to be the most challenging factor for both models. Interestingly, supervised Conet exhibited a lower error ratio than supervised Vits, while clip-based training showed lower performance compared to the supervised approaches.

🌈 Assessing Shape and Textural Bias

To evaluate shape and textural biases in the models, the authors introduced two metrics: shape proportion favoring shapes and texture proportion favoring textures. The results indicated that Vits had a higher shape bias, while Clip models showed lower textural bias compared to supervised training. However, all models exhibited a high fraction of texture decisions, suggesting the complexity of capturing texture variations.

📊 Calibration of Models

Model calibration plays a crucial role in ensuring reliable predictions. The study explored the confidence and reliability of Conet and Transformers models. The findings revealed that clip models tended to be overconfident, while supervised models exhibited slight underconfidence. Moreover, supervised Conet was found to be better calibrated than supervised Vits, suggesting its potential in tasks where reliability is crucial.

🚀 Robustness and Transferability of Models

Robustness and transferability are essential factors in real-world applications. The researchers investigated how well the models adapted to distribution shifts and perturbations such as fog, rain, and camera noises. The results showed that clip-based training models were less robust compared to supervised models. While Vits and Conex demonstrated similar average performances, Conex outperformed Vits significantly in terms of transferability.

🌐 Performance on Synthetic Data

The study also examined the models' performance on synthetic data generated by the Pug ImageNet dataset. Conex consistently outperformed Vits in both supervised and clip-based training paradigms. The authors observed that Conex achieved superior performance across various factors, highlighting its strengths in handling synthetic data.

🗣️ Discussions and Critiques of the Study

Despite the paper's compelling findings, there have been discussions and critiques from the computer vision community. Some argue that a hybrid model combining Conet and Vision Transformers might yield even better results. The study was also called upon to compare Vits with other backbones and explore downstream tasks and fine-tuning. These suggestions highlight the need for further research and exploration in the field.

🔚 Conclusion

In conclusion, the paper provides valuable insights into the performance of Conet and Transformers models in supervised and clip-based training paradigms. Through their comprehensive analysis, the authors shed light on various aspects such as mistakes, biases, calibration, robustness, transferability, and synthetic data performance. However, more research is needed to fully understand the strengths and limitations of different models and to make informed decisions in real-world applications.

🔗 Resources

• Paper Code: [link to code]
• Additional Study on Backbone Comparison: [link to study]
• Twitter Thread by Zangu: [link to thread]

FAQ

1. Q: Is this paper conclusive in determining which model to use? A: The paper provides compelling insights, but there is room for further research and exploration to make general conclusions. It is important to consider specific use cases and evaluate models accordingly.

2. Q: Are there comparisons between Vision Transformers (Vits) and other backbones? A: The paper primarily focuses on the comparison between Conet and Vits. However, it would be interesting to explore comparisons with other backbones in future studies.

3. Q: How do these models perform in downstream tasks? A: The study solely focuses on pre-trained models "out-of-the-box" without additional fine-tuning. Exploring downstream tasks and fine-tuning could provide valuable insights into model performance beyond the scope of this study.