Acuity model zoo contains a set of popular neural-network models created or converted (from Caffe or Tensorflow) by Acuity toolset.
Acuity uses JSON format to describe a neural-network model, and we provide an online model viewer to help visualized data flow graphs. The model viewer is inspired by netscope.
- Alexnet
- Inception-v1
- Inception-v2
- Inception-v3
- Inception-v4
- Mobilenet-v1
- Mobilenet-v1-025
- Nasnet-Large
- Nasnet-Mobile
- Resnet-50
- Resnext-50
- Senet-50
- Squeezenet
- VGG-16
- Xception
Acuity is a python based neural-network framework built on top of Tensorflow, it provides a set of easy to use high level layer API as well as infrastructure for optimizing neural networks for deployment on Vivante Vision IP powered hardware platforms.
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Importing from popular frameworks such as Caffe and Tensorflow
AcuityNet natively supports Caffe and Tensorflow imports, although it can be expanded to other NN frameworks.
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Fixed Point Quantization
AcuityNet provides accurate Fixed Point Quantization from floating point 32 with a calibration dataset and produces accuracy numbers before and after quantization for comparison
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Graph Optimization
Neural-network graph optimization is performed to reduce graph complexity for inference, such as Layer Merging, Layer Removal and Layer Swapping
- Merge consective layers into dense layers, such as ConvolutionReluPool, FullyConnectedRelu, etc.
- Fold BatchNrom layers into Convolution
- Swap layer ordering when suitable to reduce output size
- Remove Concatenation layers
- Intelligent layer optimization when mathamatically equivalent
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Tensor Pruning
Pruning neural networks tensors to remove ineffective connections and neurons to create sparse matrix
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Training and Validation
Acuitynet provides capability to train and validate Neural Networks
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Inference Code Generator
Generates OpenVX Neural Network inference code which can run on any OpenVX enabled platforms