SCNet: Training Inference Sample Consistency for Instance Segmentation
Authors: Thang Vu, Haeyong Kang, Chang D. Yoo2701-2709
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the standard COCO dataset reveal the effectiveness of the proposed method over multiple evaluation metrics, including box AP, mask AP, and inference speed. |
| Researcher Affiliation | Academia | Thang Vu, Haeyong Kang, Chang D. Yoo Department of Electrical Engineering, Korea Advanced Institute of Science and Technology {thangvubk,haeyong.kang,cd yoo}@kaist.ac.kr |
| Pseudocode | No | The paper includes architectural diagrams but no structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/thangvubk/SCNet. |
| Open Datasets | Yes | The COCO (Lin et al. 2014) train and val splits are using for training and inference, respectively. |
| Dataset Splits | Yes | The COCO (Lin et al. 2014) train and val splits are using for training and inference, respectively. |
| Hardware Specification | Yes | It takes about one day for the models to converge on 8 Tesla V100 GPUs. ... The runtime is measured on a single Tesla V100 GPU. |
| Software Dependencies | No | Py Torch (Paszke et al. 2017) and MMDetection (Chen et al. 2019b) are used for implementation. Specific version numbers for these software components are not provided. |
| Experiment Setup | Yes | The stage loss weights and semantic loss weight, which are adopted from (Chen et al. 2019a), are set to α = [1, 0.5, 0.25] and γ = 0.2, respectively. The global context loss weight is set to λ = 3. In all experiments, the long edge and short edge of the images are resized to 1333 and 800, respectively, without changing the aspect ratio. ... The learning rate is initialized to 0.02 and divided by 10 after 16 and 19 epochs, respectively. |