Papers › A Novel lightweight Convolutional Neural Network, ExquisiteNetV2
A Novel lightweight Convolutional Neural Network, ExquisiteNetV2
Shi-Yao Zhou, Chung-Yen Su
In the paper of ExquisiteNetV1, the ability of classification of ExquisiteNetV1 is worse than DenseNet. In this article, we propose a faster and better model ExquisiteNetV2. We conduct many experiments to evaluate its performance. We test ExquisiteNetV2, ExquisiteNetV1 and other 9 well-known models on 15 credible datasets under the same condition. According to the experimental results, ExquisiteNetV2 gets the highest classification accuracy over half of the datasets. Important of all, ExquisiteNetV2 has fewest amounts of parameters. Besides, in most instances, ExquisiteNetV2 has fastest computing speed.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | MNIST | ExquisiteNetV2 | Accuracy | 99.71 | #16 of 81 | Archive leaderboard | report |
| Image Classification | MNIST | ExquisiteNetV2 | Percentage error | 0.29 | #16 of 81 | Archive leaderboard | report |
| Image Classification | MNIST | ExquisiteNetV2 | Trainable Parameters | 518230 | #16 of 81 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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