VGG models perform image classification - they take images as input and classify the major object in the image into a set of pre-defined classes. They are trained on ImageNet dataset which contains images from 1000 classes.
VGG models provide very high accuracies but at the cost of increased model sizes. They are ideal for cases when high accuracy of classification is essential and there are limited constraints on model sizes.
| Model | Download | Top-1 accuracy (%) | Top-5 accuracy (%) |
|---|---|---|---|
| VGG 16-fp32 | 527.8 MB | 72.38 | 91.00 |
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VGG 16 and VGG 19 are from the paper Very Deep Convolutional Networks for Large-Scale Image Recognition
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VGG 16_bn and VGG 19_bn are the same models as above but with batch normalization applied after each convolution layer
- abhinavs95 (Amazon AI)
- ankkhedia (Amazon AI)
- mengniwang95 (Intel)
- airMeng (Intel)
- ftian1 (Intel)
- hshen14 (Intel)
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