Early experience with low-pass filtered images facilitates visual category learning in a neural network model

Dataset consisting of neural network models trained on low-pass filtered and intact images. 

Related to our paper: 
Jinsi* O, Henderson* MM, Tarr MJ (2023) Early experience with low-pass filtered images facilitates visual category learning in a neural network model. PLoS ONE 18(1): e0280145. https://doi.org/10.1371/journal.pone.0280145

Each .tar file contains .csv and .pt files resulting from one method of model training (grayscale or colored images, training from-scratch on images from ecoset or fine-tuning models on imagenet). Numbered folders correspond to models initialized with different random seeds. Different files in each folder correspond to different blur conditions. 
See paper for more details.

For all experiment code, see our github repository at: https://github.com/tarrlab/startingblurry

Contact [e-mail] or [e-mail] with any questions or concerns.

该数据集由经过低通滤波处理和未处理的图像训练的神经网络模型组成。与我们的论文相关：

Jinsi* O, Henderson* MM, Tarr MJ (2023) 低通滤波图像的早期经验有助于神经网络模型中的视觉类别学习。PLoS ONE 18(1): e0280145. https://doi.org/10.1371/journal.pone.0280145

每个.tar文件包含由一种模型训练方法（灰度图像或彩色图像，从零开始对ecoset图像进行训练或对ImageNet模型进行微调）生成的.csv和.pt文件。编号文件夹对应于使用不同随机种子初始化的模型。每个文件夹中的不同文件对应于不同的模糊条件。

更详细的信息请参阅论文。

所有实验代码可在我们的GitHub仓库查看：https://github.com/tarrlab/startingblurry

如有任何疑问或关切，请联系[e-mail]或[e-mail]。