Papers › ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
Sachin Mehta, Mohammad Rastegari, Linda Shapiro, Hannaneh Hajishirzi
We introduce a light-weight, power efficient, and general purpose convolutional neural network, ESPNetv2, for modeling visual and sequential data. Our network uses group point-wise and depth-wise dilated separable convolutions to learn representations from a large effective receptive field with fewer FLOPs and parameters. The performance of our network is evaluated on four different tasks: (1) object classification, (2) semantic segmentation, (3) object detection, and (4) language modeling. Experiments on these tasks, including image classification on the ImageNet and language modeling on the PenTree bank dataset, demonstrate the superior performance of our method over the state-of-the-art methods. Our network outperforms ESPNet by 4-5% and has 2-4x fewer FLOPs on the PASCAL VOC and the Cityscapes dataset. Compared to YOLOv2 on the MS-COCO object detection, ESPNetv2 delivers 4.4% higher accuracy with 6x fewer FLOPs. Our experiments show that ESPNetv2 is much more power efficient than existing state-of-the-art efficient methods including ShuffleNets and MobileNets. Our code is open-source and available at https://github.com/sacmehta/ESPNetv2
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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 | ImageNet | ESPNetv2 | GFLOPs | 0.602 | #967 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ESPNetv2 | Number of params | 5.9M | #967 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ESPNetv2 | Top 1 Accuracy | 74.9% | #967 of 1060 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes test | ESPNetv2 | Mean IoU (class) | 66.2% | #94 of 105 | Archive leaderboard | report |
| Semantic Segmentation | PASCAL VOC 2012 test | ESPNetv2 | Mean IoU | 68.0% | #41 of 51 | 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
Introduced by this paper: EESP, ESPNetv2, Strided EESP
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