Parametric Instance Classification for Unsupervised Visual Feature learning
Authors: Yue Cao, Zhenda Xie, Bin Liu, Yutong Lin, Zheng Zhang, Han Hu
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform unsupervised feature pre-training on the most widely-used dataset, Image Net-1K [8], which have 1.28 million training images. For Image Net-1K, we vary the training lengths from 200 epochs to 1600 epochs2 to facilitate comparison with previous reported results. In all experiments, a Res Net-50 [16, 17] model is adopted as the backbone network. Eight GPUs of Titan V100 and a total batch size of 512 are adopted. We follow the similar augmentations and training settings as [5, 6], with details shown in Appendix C. For the cosine soft-max loss (1), we find out that τ = 0.2 could generally perform well thus we adopt it for all experiments. |
| Researcher Affiliation | Collaboration | Yue Cao 1, Zhenda Xie 12, Bin Liu 12, Yutong Lin13, Zheng Zhang1, Han Hu1 1Microsoft Research Asia 2Tsinghua University 3Xi an Jiaotong University {yuecao,t-zhxie,v-liubin,v-yutlin,zhez,hanhu}@microsoft.com |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. It describes the methodology in text and with a diagram (Figure 1). |
| Open Source Code | Yes | The code and network configurations are available at https://github.com/bl0/PIC. |
| Open Datasets | Yes | We perform unsupervised feature pre-training on the most widely-used dataset, Image Net-1K [8], which have 1.28 million training images. |
| Dataset Splits | Yes | The linear evaluation protocol [18, 15, 5] on the Image Net-1k dataset is used in ablations. |
| Hardware Specification | Yes | Eight GPUs of Titan V100 and a total batch size of 512 are adopted. |
| Software Dependencies | No | The paper mentions ResNet-50 models and ImageNet-1K, but it does not specify software dependencies like Python, PyTorch, TensorFlow versions, or other libraries with their version numbers. |
| Experiment Setup | Yes | We perform unsupervised feature pre-training on the most widely-used dataset, Image Net-1K [8], which have 1.28 million training images. For Image Net-1K, we vary the training lengths from 200 epochs to 1600 epochs2 to facilitate comparison with previous reported results. In all experiments, a Res Net-50 [16, 17] model is adopted as the backbone network. Eight GPUs of Titan V100 and a total batch size of 512 are adopted. We follow the similar augmentations and training settings as [5, 6], with details shown in Appendix C. For the cosine soft-max loss (1), we find out that τ = 0.2 could generally perform well thus we adopt it for all experiments. |