This is a fully quantized version (asymmetrical int8) of the MicroNet Large model developed by Arm, from the MicroNets paper. It is trained on the 'slide rail' task from http://dcase.community/challenge2020/task-unsupervised-detection-of-anomalous-sounds.
Apache-2.0
The class labels associated with this model can be created by running the script get_class_labels.sh.
| Platform |
Optimized |
| Cortex-A |
✖️ |
| Cortex-M |
✔️ |
| Mali GPU |
✔️ |
| Ethos U |
✔️ |
- ✔️ - Will run on this platform.
- ✖️ - Will not run on this platform.
Dataset: Dcase 2020 Task 2 Slide Rail
| Optimization |
Value |
| Quantization |
INT8 |
| Input Node Name |
Shape |
Description |
| input |
(1, 32, 32, 1) |
Input is 64 steps of a Log Mel Spectrogram using 64 mels resized to 32x32. |
| Output Node Name |
Shape |
Description |
| Identity |
(1, 8) |
Raw logits corresponding to different machine IDs being anomalous |