SimpleNet | repository contains the architectures Models logs | Machine Learning library

SimpleNet is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. SimpleNet has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However SimpleNet build file is not available. You can download it from GitHub.

ImageNet result was achieved using simple SGD without hyper parameter tuning for 100 epochs(single crop). no multicrop techniques were used. no dense evaluation or combinations of such techniques were used unlike all other architectures. the models will be uploaded when the training is finished. *Note that the Fractional max pooling[13] uses deeper architectures and also uses extreme data augmentation. means No zero-padding or normalization with dropout and means Standard data-augmentation- with dropout. To our knowledge, our architecture has the state of the art result, without aforementioned data-augmentations. *Note that we didn’t intend on achieving the state of the art performance here as we are using a single optimization policy without fine-tuning hyper parameters or data-augmentation for a specific task, and still we nearly achieved state-of-the-art on MNIST. **Results achieved using an ensemble or extreme data-augmentation. Table 6-Slimmed version Results on Different Datasets. *Since we presented their results in their respective sections, we avoided mentioning the results here again. ** Achieved using several data-augmentation tricks. Flops and Parameter Comparison of Models trained on ImageNet. *Inception v3, v4 did not have any Caffe model, so we reported their size related information from MXNet and Tensorflow respectively. Inception-ResNet-V2 would take 60 days of training with 2 Titan X to achieve the reported accuracy. Statistics are obtained using 1# Data-augmentation method used by stochastic depth paper: