SqueezeNet is a family of models that achieve AlexNet-level accuracy on the ImageNet validation set with 50 times fewer parameters: https://github.com/DeepScale/SqueezeNet.
As many people are wondering how to improve network designs, it’s really interesting to benchmark AlexNet vs SqueezeNet.
Unfortunately, TensorRT 1.0 fails to parse the SqueezeNet 1.0 and SqueezeNet 1.1 models with exactly the same message:
Parameter check failed in addPooling, condition: windowSize.h > 0 && windowSize.w > 0 && windowSize.h*windowSize.w < MAX_KERNEL_DIMS_PRODUCT error parsing layer type Pooling index 64
The same models work fine through Caffe. Here are the links to the deploy.prototxt files: