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models/anomaly_detection/micronet_large/tflite_int8/README.md
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| # MicroNet Large INT8 | ||
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| ## Description | ||
| 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. | ||
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| ## License | ||
| [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) | ||
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| ## Related Materials | ||
| ### Class Labels | ||
| The class labels associated with this model can be created by running the script `get_class_labels.sh`. | ||
|
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| ## Network Information | ||
| | Network Information | Value | | ||
| |---------------------|----------------| | ||
| | Framework | TensorFlow Lite | | ||
| | SHA-1 Hash | 0b7e7776c79fac28c186b2ba00314a05b7faadbf | | ||
| | Size (Bytes) | 442000 | | ||
| | Provenance | https://arxiv.org/pdf/2010.11267.pdf | | ||
| | Paper | https://arxiv.org/pdf/2010.11267.pdf | | ||
|
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| ## Performance | ||
| | Platform | Optimized | | ||
| |----------|:---------:| | ||
| | Cortex-A |:heavy_multiplication_x: | | ||
| | Cortex-M |:heavy_check_mark: | | ||
| | Mali GPU |:heavy_multiplication_x: | | ||
| | Ethos U |:heavy_check_mark: | | ||
|
|
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| ### Key | ||
| * :heavy_check_mark: - Will run on this platform. | ||
| * :heavy_multiplication_x: - Will not run on this platform. | ||
|
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| ## Accuracy | ||
| Dataset: Dcase 2020 Task 2 Slide Rail | ||
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| | Metric | Value | | ||
| |--------|-------| | ||
| | AUC | 0.968 | | ||
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| ## Optimizations | ||
| | Optimization | Value | | ||
| |--------------|---------| | ||
| | Quantization | INT8 | | ||
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| ## Network Inputs | ||
| <table> | ||
| <tr> | ||
| <th width="200">Input Node Name</th> | ||
| <th width="100">Shape</th> | ||
| <th width="300">Description</th> | ||
| </tr> | ||
| <tr> | ||
| <td>input</td> | ||
| <td>(1, 32, 32, 1)</td> | ||
| <td>Input is 64 steps of a Log Mel Spectrogram using 64 mels resized to 32x32.</td> | ||
| </tr> | ||
| </table> | ||
|
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| ## Network Outputs | ||
| <table> | ||
| <tr> | ||
| <th width="200">Output Node Name</th> | ||
| <th width="100">Shape</th> | ||
| <th width="300">Description</th> | ||
| </tr> | ||
| <tr> | ||
| <td>Identity</td> | ||
| <td>(1, 8)</td> | ||
| <td>Raw logits corresponding to different machine IDs being anomalous</td> | ||
| </tr> | ||
| </table> |
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models/anomaly_detection/micronet_large/tflite_int8/ad_large_int8.tflite
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models/anomaly_detection/micronet_large/tflite_int8/definition.yaml
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| benchmark: | ||
| DCASE 2020 Task 2 Slide rail: | ||
| AUC: 0.968 | ||
| description: 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. | ||
| license: | ||
| - Apache-2.0 | ||
| network: | ||
| file_size_bytes: 442000 | ||
| filename: ad_large_int8.tflite | ||
| framework: TensorFlow Lite | ||
| hash: | ||
| algorithm: sha1 | ||
| value: 0b7e7776c79fac28c186b2ba00314a05b7faadbf | ||
| provenance: https://arxiv.org/pdf/2010.11267.pdf | ||
| network_parameters: | ||
| input_nodes: | ||
| - description: Input is 64 steps of a Log Mel Spectrogram using 64 mels resized | ||
| to 32x32. | ||
| example_input: | ||
| path: models/anomaly_detection/micronet_large/tflite_int8/testing_input/input | ||
| name: input | ||
| shape: | ||
| - 1 | ||
| - 32 | ||
| - 32 | ||
| - 1 | ||
| output_nodes: | ||
| - description: Raw logits corresponding to different machine IDs being anomalous | ||
| name: Identity | ||
| shape: | ||
| - 1 | ||
| - 8 | ||
| test_output_path: models/anomaly_detection/micronet_large/tflite_int8/testing_output/Identity | ||
| operators: | ||
| TensorFlow Lite: | ||
| - AVERAGE_POOL_2D | ||
| - CONV_2D | ||
| - DEPTHWISE_CONV_2D | ||
| - RELU6 | ||
| - RESHAPE | ||
| paper: https://arxiv.org/pdf/2010.11267.pdf |
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models/anomaly_detection/micronet_large/tflite_int8/get_class_labels.sh
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| # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved. | ||
| # | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the License); you may | ||
| # not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an AS IS BASIS, WITHOUT | ||
| # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
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| #!/usr/bin/env bash | ||
|
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| touch ./labelmappings.txt | ||
| echo "id0" >> labelmappings.txt | ||
| echo "id2" >> labelmappings.txt | ||
| echo "id4" >> labelmappings.txt | ||
| echo "id6" >> labelmappings.txt |
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models/anomaly_detection/micronet_large/tflite_int8/testing_input/input/0.npy
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models/anomaly_detection/micronet_large/tflite_int8/testing_output/Identity/0.npy
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models/anomaly_detection/micronet_medium/tflite_int8/README.md
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| # MicroNet Medium INT8 | ||
|
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||
| ## Description | ||
| This is a fully quantized version (asymmetrical int8) of the MicroNet Medium 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. | ||
|
|
||
| ## License | ||
| [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) | ||
|
|
||
| ## Related Materials | ||
| ### Class Labels | ||
| The class labels associated with this model can be created by running the script `get_class_labels.sh`. | ||
|
|
||
| ## Network Information | ||
| | Network Information | Value | | ||
| |---------------------|----------------| | ||
| | Framework | TensorFlow Lite | | ||
| | SHA-1 Hash | ed709fccb1d57393cbc88f36da38a4ab70f97b4a | | ||
| | Size (Bytes) | 463792 | | ||
| | Provenance | https://arxiv.org/pdf/2010.11267.pdf | | ||
| | Paper | https://arxiv.org/pdf/2010.11267.pdf | | ||
|
|
||
| ## Performance | ||
| | Platform | Optimized | | ||
| |----------|:---------:| | ||
| | Cortex-A |:heavy_multiplication_x: | | ||
| | Cortex-M |:heavy_check_mark: | | ||
| | Mali GPU |:heavy_multiplication_x: | | ||
| | Ethos U |:heavy_check_mark: | | ||
|
|
||
| ### Key | ||
| * :heavy_check_mark: - Will run on this platform. | ||
| * :heavy_multiplication_x: - Will not run on this platform. | ||
|
|
||
| ## Accuracy | ||
| Dataset: Dcase 2020 Task 2 Slide Rail | ||
|
|
||
| | Metric | Value | | ||
| |--------|-------| | ||
| | AUC | 0.9632 | | ||
|
|
||
| ## Optimizations | ||
| | Optimization | Value | | ||
| |--------------|---------| | ||
| | Quantization | INT8 | | ||
|
|
||
| ## Network Inputs | ||
| <table> | ||
| <tr> | ||
| <th width="200">Input Node Name</th> | ||
| <th width="100">Shape</th> | ||
| <th width="300">Description</th> | ||
| </tr> | ||
| <tr> | ||
| <td>input</td> | ||
| <td>(1, 32, 32, 1)</td> | ||
| <td>Input is 64 steps of a Log Mel Spectrogram using 64 mels resized to 32x32.</td> | ||
| </tr> | ||
| </table> | ||
|
|
||
| ## Network Outputs | ||
| <table> | ||
| <tr> | ||
| <th width="200">Output Node Name</th> | ||
| <th width="100">Shape</th> | ||
| <th width="300">Description</th> | ||
| </tr> | ||
| <tr> | ||
| <td>Identity</td> | ||
| <td>(1, 8)</td> | ||
| <td>Raw logits corresponding to different machine IDs being anomalous</td> | ||
| </tr> | ||
| </table> |
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models/anomaly_detection/micronet_medium/tflite_int8/ad_medium_int8.tflite
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models/anomaly_detection/micronet_medium/tflite_int8/definition.yaml
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,43 @@ | ||
| benchmark: | ||
| DCASE 2020 Task 2 Slide rail: | ||
| AUC: 0.9632 | ||
| description: This is a fully quantized version (asymmetrical int8) of the MicroNet | ||
| Medium 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. | ||
| license: | ||
| - Apache-2.0 | ||
| network: | ||
| file_size_bytes: 463792 | ||
| filename: ad_medium_int8.tflite | ||
| framework: TensorFlow Lite | ||
| hash: | ||
| algorithm: sha1 | ||
| value: ed709fccb1d57393cbc88f36da38a4ab70f97b4a | ||
| provenance: https://arxiv.org/pdf/2010.11267.pdf | ||
| network_parameters: | ||
| input_nodes: | ||
| - description: Input is 64 steps of a Log Mel Spectrogram using 64 mels resized | ||
| to 32x32. | ||
| example_input: | ||
| path: models/anomaly_detection/micronet_medium/tflite_int8/testing_input/input | ||
| name: input | ||
| shape: | ||
| - 1 | ||
| - 32 | ||
| - 32 | ||
| - 1 | ||
| output_nodes: | ||
| - description: Raw logits corresponding to different machine IDs being anomalous | ||
| name: Identity | ||
| shape: | ||
| - 1 | ||
| - 8 | ||
| test_output_path: models/anomaly_detection/micronet_medium/tflite_int8/testing_output/Identity | ||
| operators: | ||
| TensorFlow Lite: | ||
| - AVERAGE_POOL_2D | ||
| - CONV_2D | ||
| - DEPTHWISE_CONV_2D | ||
| - RELU6 | ||
| - RESHAPE | ||
| paper: https://arxiv.org/pdf/2010.11267.pdf |
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models/anomaly_detection/micronet_medium/tflite_int8/get_class_labels.sh
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| # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved. | ||
| # | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the License); you may | ||
| # not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an AS IS BASIS, WITHOUT | ||
| # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
|
||
| #!/usr/bin/env bash | ||
|
|
||
| touch ./labelmappings.txt | ||
| echo "id0" >> labelmappings.txt | ||
| echo "id2" >> labelmappings.txt | ||
| echo "id4" >> labelmappings.txt | ||
| echo "id6" >> labelmappings.txt |
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models/anomaly_detection/micronet_medium/tflite_int8/testing_input/input/0.npy
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models/anomaly_detection/micronet_medium/tflite_int8/testing_output/Identity/0.npy
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