Application note description
This application note describes some common errors that can occur when converting neural network files and provides a list of supported layers.
- Getting Started with Firefly-DL in Linux
- Getting Started with Firefly-DL in Windows
- Tips on creating Training Data for Deep Learning Neural Networks
- Neural Networks Supported by the Firefly-DL
Preparing for use
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Whether using either the FLIR NeuroUtility (Windows) or mvNCCompile (Linux) for converting your inference network files, here are some common errors and ways to fix them:
Toolkit Error: Stage Details Not Supported
This error can occur if at least one of the layers being used in the network is unsupported.
- Check the layer name for type (the error gives the name).
- Check of list of accepted layer types (listed at end of this application note).
It can also mean that not all training code or placeholders were properly removed before doing the final conversion.
Toolkit error: Provided OutputNode/InputNode name does not exist or does not match with one contained in caffemodel file provided
This error can occur when at least one of the node names provided is incorrect. This can be as simple as having an incorrect capitalization or spelling, or the wrong node name entirely.
Toolkit Error: Parser not supported
This error can occur when an incorrect file location is provided, for example the inference network file.
Setup Error: Not enough resources on Myriad to process this network
This error can occur when there is not enough memory for the number of layers for the inference network file.
- Reduce the number of layers, or
- Reduce the channels per layer
List of Supported Layers
The following convolution cases have been extensively tested (for stride s): 1x1s1,3x3s1,5x5s1,7x7s1, 7x7s2, 7x7s4
- Max Pooling Radix NxM with Stride S
- Average Pooling: Radix NxM with Stride S, Global average pooling
- Local Response Normalization
- Relu, Relu-X, Prelu, Leaky-Relu
- Slice (in SW via crop layer)
- ElmWise unit : supported operations - sum, prod, max
- Fully Connected Layers (limited support)
- Crop (SW in ChannelMinor format only)
- Batch Normalization (fused)
- L2 Normalization
- Input Layer