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| author | Kshitij Sisodia <kshitij.sisodia@arm.com> | 2021-05-20 11:18:53 +0100 |
|---|---|---|
| committer | Kshitij Sisodia <kshitij.sisodia@arm.com> | 2021-05-20 12:57:49 +0000 |
| commit | e12ac836d2110403475d0e8b4bdfec03a0874f6c (patch) | |
| tree | 2f4ac0a379cd2cf6d5ad9bc11c2a0c14abb740c3 /docs/documentation.md | |
| parent | 659fcd951ac18d1ee7737a6ddf6a3ec162c73ca5 (diff) | |
| download | ml-embedded-evaluation-kit-e12ac836d2110403475d0e8b4bdfec03a0874f6c.tar.gz | |
MLECO-1883: Updating wav2letter model
Using the new pruned wav2letter model from Arm Model Zoo.
The new model when optimised by Vela, produces a tflite
file ~10 MB smaller than the current.
Change-Id: I4ab6007c5b6111f41d8097e29b2af6cde2abc457
Diffstat (limited to 'docs/documentation.md')
| -rw-r--r-- | docs/documentation.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/docs/documentation.md b/docs/documentation.md index 9ec73a3..7f8fbf9 100644 --- a/docs/documentation.md +++ b/docs/documentation.md @@ -10,7 +10,7 @@ - [Deployment](#deployment) - [Implementing custom ML application](#implementing-custom-ml-application) - [Testing and benchmarking](#testing-and-benchmarking) - - [Memory considerations](#memory-considerations) + - [Memory Considerations](#memory-considerations) - [Troubleshooting](#troubleshooting) - [Appendix](#appendix) @@ -184,7 +184,7 @@ from [Arm ML-Zoo](https://github.com/ARM-software/ML-zoo/). - [Mobilenet V2](https://github.com/ARM-software/ML-zoo/blob/master/models/image_classification/mobilenet_v2_1.0_224/tflite_uint8). - [DS-CNN](https://github.com/ARM-software/ML-zoo/blob/master/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8). -- [Wav2Letter](https://github.com/ARM-software/ML-zoo/blob/master/models/speech_recognition/wav2letter/tflite_int8). +- [Wav2Letter](https://github.com/ARM-software/ML-zoo/tree/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8). - [Anomaly Detection](https://github.com/ARM-software/ML-zoo/raw/7c32b097f7d94aae2cd0b98a8ed5a3ba81e66b18/models/anomaly_detection/micronet_medium/tflite_int8/ad_medium_int8.tflite). When using Ethos-U55 NPU backend, the NN model is assumed to be optimized by Vela compiler. |