# Deep Residual Learning for Image Recognition: A Survey
## 11 Evaluation Metrics for DRN  
The author in [1] studied the performance of DRN on four image recognition tasks. They found that DRN significantly outperformed previous state-of-the-art methods on all four tasks. The authors also proposed a new evaluation metric for DRN, which they called the Top-1 error. This metric is based on the idea that the top-1 error is a more accurate measure of performance than the traditional accuracy metric. The authors found that DRN achieved a top-1 error of only 3.56% on the ImageNet dataset, which is significantly lower than the previous state-of-the-art method (4.82%). In addition, DRN achieves a top 5 error of 9.8%, again much better than the previous state-of-the-art method (17.2%). Finally, DRN has a mean Average Precision of 0.927, while the previous state-of-the-art method had an average precision of 0.648 on the same datasets. These results show that DRN outperforms previous state-of-the-art methods in terms of all three metrics: Top 1 error, Top 5 error and Mean Average Precision. To evaluate how well their models are performing, researchers use various methods such as accuracy or precision/recall measurements. However, these metrics can be misleading if they're applied blindly without considering their underlying assumptions. For example, suppose that you have two image recognition models, Model A and Model Both with 99% accuracy, but Model A recognizes every photo correctly, whereas Model B returns no false positives but misses half of photos it should recognize. It would be wrong to conclude from our analysis above that model B is superior to model A! As we've seen before, however, measures like top-1 error and top-5 error are better at making such comparisons because false negatives have a greater impact on their values than false positives do. Therefore, let us take a look at how DRN compares to other algorithms when evaluated using this metric. One advantage of evaluating an algorithm using top-1 error instead of traditional accuracy is that one can examine algorithms side by side on equal footing. For instance, comparing algorithm A and algorithm B where Algorithm A achieves 90% accuracy and Algorithm B achieves 98% accuracy sounds like Algorithm B is better, right? But what if Algorithm A just does not make any mistakes whereas Algorithm B makes two mistakes out of every 100 images? Using top-1 error one can see that this would equate to two errors out of 10 images (i.e., 20%) instead of four errors out of 100 images (0.01%), making the difference between them much clearer. Indeed, across all four image recognition tasks modeled here, we find that DRN performs much better than the competition according to this new metric. On the CIFAR10 dataset, DRN achieved a top-1 error of 16.3% compared to 23.6% for Mask R-CNN (a popular deep learning architecture). This means that even if we allow masks R-CNN higher false positive rate due to its more sophisticated features and complexity, it still has a worse bottom line result with respect to correctness. Another noteworthy observation besides improving test accuracy and reducing computational cost, deep residual networks also preserve local information from the previous layer. This property, combined with the ability to learn from a large set of data, enables DRN to achieve state-of-the-art accuracy on several image recognition tasks. These improvements come mostly from training deep networks with large amounts of data (data augmentation), which reduces overfitting, but deep residual networks may not always require high amounts of data to achieve similar accuracies; depending on the task some combinations may actually perform better than others. In the case of mask-based networks, for example, accuracy might be boosted slightly from a few percentage points to many percent points by trading top-1 error for top-5 error. Of course, this is a tradeoff with consequences.