Commit 7fb600b2 authored by Mehdi Cherti's avatar Mehdi Cherti

Revert "Revert "JJ: wiki update""

This reverts commit 5232c3df.
parent 5232c3df
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Model efficiency has become increasingly important in computer vision. In this paper, we systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency. First, we propose a weighted bi-directional feature pyramid network (BiFPN), which allows easy and fast multi-scale feature fusion; Second, we propose a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time. Based on these optimizations and EfficientNet backbones, we have developed a new family of object detectors, called EfficientDet, which consistently achieve much better efficiency than prior art across a wide spectrum of resource constraints. In particular, with single-model and single-scale, our EfficientDet-D7 achieves state-of-the-art 52.2 AP on COCO test-dev with 52M parameters and 325B FLOPs, being 4x - 9x smaller and using 13x - 42x fewer FLOPs than previous detectors. Code is available at this https URL.
Code: https://github.com/google/automl/tree/master/efficientdet, https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch
[1] Mingxing Tan, Ruoming Pang, Quoc V. Le. EfficientDet: Scalable and Efficient Object Detection. CVPR 2020. Arxiv link: https://arxiv.org/abs/1911.09070
Code: https://github.com/google/automl/tree/master/efficientdet, https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch \
Also appeared at CVPR, 2020: Mingxing Tan, Ruoming Pang, Quoc V. Le. EfficientDet: Scalable and Efficient Object Detection. CVPR 2020. Arxiv link: https://arxiv.org/abs/1911.09070
##### Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans
* paper: https://www.medrxiv.org/content/10.1101/2020.04.13.20063941v1
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