Container: Context Aggregation Networks
Authors: peng gao, Jiasen Lu, hongsheng Li, Roozbeh Mottaghi, Aniruddha Kembhavi
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our CONTAINER architecture achieves 82.7 % Top-1 accuracy on Image Net using 22M parameters, +2.8 improvement compared with Dei T-Small, and can converge to 79.9 % Top-1 accuracy in just 200 epochs. |
| Researcher Affiliation | Collaboration | Peng Gao1,2, Jiasen Lu4, Hongsheng Li2, Roozbeh Mottaghi3,4, Aniruddha Kembhavi3,4 1 Shanghai AI Laboratory 2 CUHK-Sense Time Joint Lab, CUHK 3 University of Washington 4 PRIOR @ Allen Institute for AI |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is released at https://github.com/allenai/container. |
| Open Datasets | Yes | Our CONTAINER architecture achieves 82.7 % Top-1 accuracy on Image Net using 22M parameters... Table 1: Image Net [12] Top-1 accuracy comparison... Table 4: Comparing the CONTAINER-LIGHT backbone with several previous methods at the tasks of object detection and instance segmentation using the Mask-RCNN and Retina Net networks. on the COCO dataset [38]. |
| Dataset Splits | Yes | We train Dei T [54] and CONTAINER-LIGHT for 100 epochs at the self supervised task of visual representation learning using the DINO framework [6]. Table 3: Image Net Top-1 Acc for CONTAINER-LIGHT and Dei T-S with varying training sizes. Table 4 compares several backbones applied to the Retina Net detector [37] on the COCO dataset [38]. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. It only mentions general terms like 'huge GPU memory'. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., 'PyTorch 1.9', 'TensorFlow 2.x') needed to replicate the experiment. |
| Experiment Setup | No | The paper states: 'Please see the appendix for details of the models, training and setup.' This indicates that specific experimental setup details, such as hyperparameters or system-level training settings, are not provided in the main text. |