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Cascaded Context Dependency: An Extremely Lightweight Module for Deep Convolutional Neural Networks

Xu Ma, Zhinan Qiao, Jingda Guo, Sihai Tang, Qi Chen, Qing Yang, Song Fu ✉️
(Accepted by ICIP 2020)

Implementation

In this repository, all the models are implemented by pytorch.

We use the standard data augmentation strategies with ResNet.

To reproduce our CCD module work, please refer Usage.md.

Trained Models

😊 All trained models and training log files are submitted to Google Drive.

😊 We provide corresponding links in the "download" column.



Table 1: Comparison results of single-crop classification accuracy (%) and complexity on the ImageNet validation set. The best two performances are marked in **bold**.
Model top-1 acc. top-5 acc. FLOPs(G) Parameters(M) Download
ResNet50 75.8974 92.7224 4.122 25.557 model log
SE-ResNet50 77.2877 93.6478 4.130 28.088 model log
GE-ResNet50 76.2357 92.9847 4.127 25.557 model log
CBAM-ResNet50 77.2840 93.6005 4.139 28.090 model log
SK-ResNet50 77.3657 93.5256 4.187 26.154 model log
GC-ResNet50 74.8966 92.2812 4.130 28.105 model log
CCD-ResNet50 (ours) 77.3137 93.6489 4.122 25.560 model log


Table 2: Detection performances (%) with different backbones on the MS-COCO validation dataset. We employ two state-of-the-art detectors: RetinaNet and Cascade R-CNN in our detection experiments.
Detector Backbone AP(50:95) AP(50) AP(75) AP(s) AP(m) AP(l) Download
Retina ResNet50 36.2 55.9 38.5 19.4 39.8 48.3 model log
Retina SE-ResNet50 37.4 57.8 39.8 20.6 40.8 50.3 model log
Retina CCD-ResNet50 37.8 58.5 40.1 21.6 41.5 50.9 model log
Cascade R-CNN ResNet50 40.6 58.9 44.2 22.4 43.7 54.7 model log
Cascade R-CNN GC-ResNet50 41.1 59.7 44.6 23.6 44.1 54.3 model log
Cascade R-CNN CCD-ResNet50 42.5 61.1 46.4 24.7 45.9 56.5 model log

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