Created by Jinming Cao, Yangyan Li, Mingchao Sun, Ying Chen, Dani Lischinski, Daniel Cohen-Or, Baoquan Chen, and Changhe Tu.
DO-Conv is a depthwise over-parameterized convolutional layer, which can be used as a replacement of conventional convolutional layer in CNNs in the training phase to achieve higher accuracies. In the inference phase, DO-Conv can be fused into a conventional convolutional layer, resulting in the computation amount that is exactly the same as that of a conventional convolutional layer.
Please see our preprint on arXiv for more details, where we demonstrated the advantages of DO-Conv on various benchmark datasets/tasks.
We take the model zoo of GluonCV as baselines. The settings in the baselines have been tuned to favor baselines, and they are not touched during the switch to DO-Conv. In other words, DO-Conv is the one and only change over baselines, and no hyper-parameter tuning is conducted to favor DO-Conv. We consider GluonCV highly reproducible, but still, to exclude clutter factors as much as possible, we train the baselines ourselves, and compare DO-Conv versions with them, while reporting the performance provided by GluonCV as reference. The results are summarized in this table where the “DO-Conv” column shows the performance gain over the baselines.
| Network | Depth | Reference | Baseline | DO-Conv |
|---|---|---|---|---|
| Plain | 18 | - | 69.97 | +1.01 |
| ResNet-v1 | 18 | 70.93 | 70.87 | +0.82 |
| 34 | 74.37 | 74.49 | +0.49 | |
| 50 | 77.36 | 77.32 | +0.08 | |
| 101 | 78.34 | 78.16 | +0.46 | |
| 152 | 79.22 | 79.34 | +0.07 | |
| ResNet-v1b | 18 | 70.94 | 71.08 | +0.71 |
| 34 | 74.65 | 74.35 | +0.77 | |
| 50 | 77.67 | 77.56 | +0.44 | |
| 101 | 79.20 | 79.14 | +0.25 | |
| 152 | 79.69 | 79.60 | +0.10 | |
| ResNet-v2 | 18 | 71.00 | 70.80 | +0.64 |
| 34 | 74.40 | 74.76 | +0.22 | |
| 50 | 77.17 | 77.17 | +0.31 | |
| 101 | 78.53 | 78.56 | +0.11 | |
| 152 | 79.21 | 79.24 | +0.14 | |
| ResNext | 50_32x4d | 79.32 | 79.21 | +0.40 |
| MobileNet-v1 | - | 73.28 | 73.30 | +0.03 |
| MobileNet-v2 | - | 72.04 | 71.89 | +0.16 |
| MobileNet-v3 | - | 75.32 | 75.16 | +0.14 |
In thie repo, we provide reference implementation of DO-Conv in Tensorflow, PyTorch and GluonCV, as replacement to tf.keras.layers.Conv2D, torch.nn.Conv2d and mxnet.gluon.nn.Conv2D, respectively. Please see the code for more details.