EfficientNet v2-S TensorFlow2 checkpoint trained on Imagenet using 1 DGX A100 (batchsize=3680=8x460)
Model Overview
EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, being an order-of-magnitude smaller and faster.
Model Architecture
EfficientNets are developed based on AutoML and Compound Scaling. In particular, a mobile-size baseline network called EfficientNet-B0 is developed from AutoML MNAS Mobile framework, the building block is mobile inverted bottleneck MBConv with squeeze-and-excitation optimization. Then, through a compound scaling method, this baseline is scaled up to obtain EfficientNet-B1 to B7.
Training
This model was trained using script available on NGC and in GitHub repo
Dataset
The following datasets were used to train this model:
- ImageNet - Image database organized according to the WordNet hierarchy, in which each noun is depicted by hundreds and thousands of images.
Performance
Performance numbers for this model are available in NGC
References
License
This model was trained using open-source software available in Deep Learning Examples repository. For terms of use, please refer to the license of the script and the datasets the model was derived from.