- Title: DSOD: learning deeply supervised object detectors from scratch
- Task: Object Detection
- Author: Z. Shen, Z. Liu, J. Li, Y. Jiang, Y. Chen, and X. Xue
- Date: Aug. 2017
- Arxiv: 1708.01241
- Published: ICCV 2017
Highlights & Drawbacks
- Object Detection without pre-training
- DenseNet-like network
Motivation & Design
A common practice that used in earlier works such as R-CNN is to pre-train a backbone network on a categorical dataset like ImageNet, and then use these pre-trained weights as initialization of detection model. Although I have once successfully trained a small detection network from random initialization on a large dataset, there are few models trained from scratch when the number of instances in a dataset is limited like Pascal VOC and COCO. Actually, using better pre-trained weights is one of the tricks in detection challenges. DSOD attempts to train the detection network from scratch with the help of “Deep Supervision” from DenseNet.
The 4 principles authors argued for object detection networks:
1. Proposal-free 2. Deep supervision 3. Stem Block 4. Dense Prediction Structure
Performance & Ablation Study
DSOD outperforms detectors with pre-trained weights.
Ablation Study on parts: