This is a patch release for 1.3.0 that contains the following changes:
- Performance on some unstructured sparse quantized YOLOv5 models has been improved. This fixes a performance regression compared to DeepSparse 1.1.
- DeepSparse no longer throws an exception when it cannot determine L3 cache information and instead logs a warning message.
- An assertion failure on some compound sparse quantized transformer models has been fixed.
- Models with ONNX opset 13 Squeeze operators no longer exhibit poor performance, and DeepSparse now sees speedup from sparsity when running them.
- NumPy version pinned to <=1.21.6 to avoid deprecation warning/index errors in pipelines.