├── .gitattributes
├── .gitignore
├── LICENSE
├── README.md
├── models
├── anomaly_detection
│ ├── micronet_large
│ │ └── tflite_int8
│ │ │ ├── README.md
│ │ │ ├── ad_large_int8.tflite
│ │ │ ├── definition.yaml
│ │ │ ├── get_class_labels.sh
│ │ │ ├── testing_input
│ │ │ └── input
│ │ │ │ └── 0.npy
│ │ │ └── testing_output
│ │ │ └── Identity
│ │ │ └── 0.npy
│ ├── micronet_medium
│ │ └── tflite_int8
│ │ │ ├── README.md
│ │ │ ├── ad_medium_int8.tflite
│ │ │ ├── definition.yaml
│ │ │ ├── get_class_labels.sh
│ │ │ ├── testing_input
│ │ │ └── input
│ │ │ │ └── 0.npy
│ │ │ └── testing_output
│ │ │ └── Identity
│ │ │ └── 0.npy
│ └── micronet_small
│ │ └── tflite_int8
│ │ ├── README.md
│ │ ├── ad_small_int8.tflite
│ │ ├── definition.yaml
│ │ ├── get_class_labels.sh
│ │ ├── testing_input
│ │ └── input
│ │ │ └── 0.npy
│ │ └── testing_output
│ │ └── Identity
│ │ └── 0.npy
├── experimental
│ ├── efficientnet_lite0_224
│ │ ├── README.md
│ │ └── efficientnet_lite0_224.tflite
│ ├── har_cnn
│ │ ├── README.md
│ │ └── har_int8.tflite
│ ├── ssd_mobilenet_v3_int8
│ │ ├── README.md
│ │ └── ssd_mobilenet_v3_int8.tflite
│ ├── yolov3_416_416_backbone_mltools_int8
│ │ ├── README.md
│ │ └── yolov3_416_416_backbone_mltools_int8.tflite
│ └── yolov3_tiny_int8_pruned_backbone_only
│ │ ├── README.md
│ │ └── yolov3_tiny_int8_pruned_backbone_only.tflite
├── image_classification
│ └── mobilenet_v2_1.0_224
│ │ ├── tflite_int8
│ │ ├── README.md
│ │ ├── definition.yaml
│ │ ├── get_class_labels.sh
│ │ ├── mobilenet_v2_1.0_224_INT8.tflite
│ │ ├── recreate_model
│ │ │ ├── README.md
│ │ │ ├── benchmark_mobilenetv2.sh
│ │ │ ├── benchmark_model.py
│ │ │ ├── corpus.py
│ │ │ ├── load_data.py
│ │ │ ├── quantise_mobilenetv2.sh
│ │ │ ├── quantise_model.py
│ │ │ ├── requirements.txt
│ │ │ └── write_tfrecord.py
│ │ ├── scripts
│ │ │ └── process_labels.py
│ │ ├── testing_input
│ │ │ └── tfl.quantize
│ │ │ │ └── 0.npy
│ │ └── testing_output
│ │ │ └── MobilenetV2
│ │ │ └── Predictions
│ │ │ └── Reshape_11
│ │ │ └── 0.npy
│ │ └── tflite_uint8
│ │ ├── README.md
│ │ ├── definition.yaml
│ │ ├── get_class_labels.sh
│ │ ├── mobilenet_v2_1.0_224_quantized_1_default_1.tflite
│ │ ├── scripts
│ │ └── process_labels.py
│ │ ├── testing_input
│ │ └── input
│ │ │ └── 0.npy
│ │ └── testing_output
│ │ └── output
│ │ └── 0.npy
├── keyword_spotting
│ ├── cnn_large
│ │ └── model_package_tf
│ │ │ ├── README.md
│ │ │ ├── cnn_l_inference_keras.py
│ │ │ ├── cnn_l_inference_tflite.py
│ │ │ ├── convert_to_tflite.py
│ │ │ ├── data_processing
│ │ │ ├── __init__.py
│ │ │ └── data_preprocessing.py
│ │ │ ├── evaluation.py
│ │ │ ├── how_to_guidance.ipynb
│ │ │ ├── model_archive
│ │ │ ├── TFLite
│ │ │ │ ├── tflite_fp32
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── cnn_l.tflite
│ │ │ │ │ ├── definition.yaml
│ │ │ │ │ ├── testing_input
│ │ │ │ │ │ └── input
│ │ │ │ │ │ │ └── 0.npy
│ │ │ │ │ └── testing_output
│ │ │ │ │ │ └── Identity
│ │ │ │ │ │ └── 0.npy
│ │ │ │ └── tflite_int8
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── cnn_l_quantized.tflite
│ │ │ │ │ ├── definition.yaml
│ │ │ │ │ ├── testing_input
│ │ │ │ │ └── input
│ │ │ │ │ │ └── 0.npy
│ │ │ │ │ └── testing_output
│ │ │ │ │ └── Identity
│ │ │ │ │ └── 0.npy
│ │ │ └── model_source
│ │ │ │ ├── saved_model
│ │ │ │ └── cnn_large
│ │ │ │ │ ├── keras_metadata.pb
│ │ │ │ │ ├── saved_model.pb
│ │ │ │ │ └── variables
│ │ │ │ │ ├── variables.data-00000-of-00001
│ │ │ │ │ └── variables.index
│ │ │ │ └── weights
│ │ │ │ ├── checkpoint
│ │ │ │ ├── cnn_0.94_ckpt.data-00000-of-00001
│ │ │ │ └── cnn_0.94_ckpt.index
│ │ │ ├── model_core_utils
│ │ │ ├── __init__.py
│ │ │ └── models.py
│ │ │ ├── optimisations.py
│ │ │ ├── recreate_model.sh
│ │ │ ├── requirements.txt
│ │ │ ├── train.py
│ │ │ └── validation_utils
│ │ │ └── labels.txt
│ ├── cnn_medium
│ │ └── model_package_tf
│ │ │ ├── README.md
│ │ │ ├── cnn_m_inference_keras.py
│ │ │ ├── cnn_m_inference_tflite.py
│ │ │ ├── convert_to_tflite.py
│ │ │ ├── data_processing
│ │ │ ├── __init__.py
│ │ │ └── data_preprocessing.py
│ │ │ ├── evaluation.py
│ │ │ ├── how_to_guidance.ipynb
│ │ │ ├── model_archive
│ │ │ ├── TFLite
│ │ │ │ ├── tflite_fp32
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── cnn_m.tflite
│ │ │ │ │ ├── definition.yaml
│ │ │ │ │ ├── testing_input
│ │ │ │ │ │ └── input
│ │ │ │ │ │ │ └── 0.npy
│ │ │ │ │ └── testing_output
│ │ │ │ │ │ └── Identity
│ │ │ │ │ │ └── 0.npy
│ │ │ │ └── tflite_int8
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── cnn_m_quantized.tflite
│ │ │ │ │ ├── definition.yaml
│ │ │ │ │ ├── testing_input
│ │ │ │ │ └── input
│ │ │ │ │ │ └── 0.npy
│ │ │ │ │ └── testing_output
│ │ │ │ │ └── Identity
│ │ │ │ │ └── 0.npy
│ │ │ └── model_source
│ │ │ │ ├── saved_model
│ │ │ │ └── cnn_medium
│ │ │ │ │ ├── keras_metadata.pb
│ │ │ │ │ ├── saved_model.pb
│ │ │ │ │ └── variables
│ │ │ │ │ ├── variables.data-00000-of-00001
│ │ │ │ │ └── variables.index
│ │ │ │ └── weights
│ │ │ │ ├── checkpoint
│ │ │ │ ├── cnn_0.93_ckpt.data-00000-of-00001
│ │ │ │ └── cnn_0.93_ckpt.index
│ │ │ ├── model_core_utils
│ │ │ ├── __init__.py
│ │ │ └── models.py
│ │ │ ├── optimisations.py
│ │ │ ├── recreate_model.sh
│ │ │ ├── requirements.txt
│ │ │ ├── train.py
│ │ │ └── validation_utils
│ │ │ └── labels.txt
│ ├── cnn_small
│ │ └── model_package_tf
│ │ │ ├── README.md
│ │ │ ├── cnn_s_inference_keras.py
│ │ │ ├── cnn_s_inference_tflite.py
│ │ │ ├── convert_to_tflite.py
│ │ │ ├── data_processing
│ │ │ ├── __init__.py
│ │ │ └── data_preprocessing.py
│ │ │ ├── evaluation.py
│ │ │ ├── how_to_guidance.ipynb
│ │ │ ├── model_archive
│ │ │ ├── TFLite
│ │ │ │ ├── tflite_fp32
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── cnn_s.tflite
│ │ │ │ │ ├── definition.yaml
│ │ │ │ │ ├── testing_input
│ │ │ │ │ │ └── input
│ │ │ │ │ │ │ └── 0.npy
│ │ │ │ │ └── testing_output
│ │ │ │ │ │ └── Identity
│ │ │ │ │ │ └── 0.npy
│ │ │ │ └── tflite_int8
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── cnn_s_quantized.tflite
│ │ │ │ │ ├── definition.yaml
│ │ │ │ │ ├── testing_input
│ │ │ │ │ └── input
│ │ │ │ │ │ └── 0.npy
│ │ │ │ │ └── testing_output
│ │ │ │ │ └── Identity
│ │ │ │ │ └── 0.npy
│ │ │ └── model_source
│ │ │ │ ├── saved_model
│ │ │ │ └── cnn_small
│ │ │ │ │ ├── keras_metadata.pb
│ │ │ │ │ ├── saved_model.pb
│ │ │ │ │ └── variables
│ │ │ │ │ ├── variables.data-00000-of-00001
│ │ │ │ │ └── variables.index
│ │ │ │ └── weights
│ │ │ │ ├── checkpoint
│ │ │ │ ├── cnn_0.92_ckpt.data-00000-of-00001
│ │ │ │ └── cnn_0.92_ckpt.index
│ │ │ ├── model_core_utils
│ │ │ ├── __init__.py
│ │ │ └── models.py
│ │ │ ├── optimisations.py
│ │ │ ├── recreate_model.sh
│ │ │ ├── requirements.txt
│ │ │ ├── train.py
│ │ │ └── validation_utils
│ │ │ └── labels.txt
│ ├── dnn_large
│ │ └── model_package_tf
│ │ │ ├── README.md
│ │ │ ├── convert_to_tflite.py
│ │ │ ├── data_processing
│ │ │ ├── __init__.py
│ │ │ └── data_preprocessing.py
│ │ │ ├── dnn_l_inference_keras.py
│ │ │ ├── dnn_l_inference_tflite.py
│ │ │ ├── evaluation.py
│ │ │ ├── how_to_guidance.ipynb
│ │ │ ├── model_archive
│ │ │ ├── TFLite
│ │ │ │ ├── tflite_fp32
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── definition.yaml
│ │ │ │ │ ├── dnn_l.tflite
│ │ │ │ │ ├── testing_input
│ │ │ │ │ │ └── input
│ │ │ │ │ │ │ └── 0.npy
│ │ │ │ │ └── testing_output
│ │ │ │ │ │ └── Identity
│ │ │ │ │ │ └── 0.npy
│ │ │ │ └── tflite_int8
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── definition.yaml
│ │ │ │ │ ├── dnn_l_quantized.tflite
│ │ │ │ │ ├── testing_input
│ │ │ │ │ └── input
│ │ │ │ │ │ └── 0.npy
│ │ │ │ │ └── testing_output
│ │ │ │ │ └── Identity
│ │ │ │ │ └── 0.npy
│ │ │ └── model_source
│ │ │ │ ├── saved_model
│ │ │ │ └── dnn_large
│ │ │ │ │ ├── keras_metadata.pb
│ │ │ │ │ ├── saved_model.pb
│ │ │ │ │ └── variables
│ │ │ │ │ ├── variables.data-00000-of-00001
│ │ │ │ │ └── variables.index
│ │ │ │ └── weights
│ │ │ │ ├── checkpoint
│ │ │ │ ├── dnn_0.87_ckpt.data-00000-of-00001
│ │ │ │ └── dnn_0.87_ckpt.index
│ │ │ ├── model_core_utils
│ │ │ ├── __init__.py
│ │ │ └── models.py
│ │ │ ├── optimisations.py
│ │ │ ├── recreate_model.sh
│ │ │ ├── requirements.txt
│ │ │ ├── train.py
│ │ │ └── validation_utils
│ │ │ └── labels.txt
│ ├── dnn_medium
│ │ └── model_package_tf
│ │ │ ├── README.md
│ │ │ ├── convert_to_tflite.py
│ │ │ ├── data_processing
│ │ │ ├── __init__.py
│ │ │ └── data_preprocessing.py
│ │ │ ├── dnn_m_inference_keras.py
│ │ │ ├── dnn_m_inference_tflite.py
│ │ │ ├── evaluation.py
│ │ │ ├── how_to_guidance.ipynb
│ │ │ ├── model_archive
│ │ │ ├── TFLite
│ │ │ │ ├── tflite_fp32
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── definition.yaml
│ │ │ │ │ ├── dnn_m.tflite
│ │ │ │ │ ├── testing_input
│ │ │ │ │ │ └── input
│ │ │ │ │ │ │ └── 0.npy
│ │ │ │ │ └── testing_output
│ │ │ │ │ │ └── Identity
│ │ │ │ │ │ └── 0.npy
│ │ │ │ └── tflite_int8
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── definition.yaml
│ │ │ │ │ ├── dnn_m_quantized.tflite
│ │ │ │ │ ├── testing_input
│ │ │ │ │ └── input
│ │ │ │ │ │ └── 0.npy
│ │ │ │ │ └── testing_output
│ │ │ │ │ └── Identity
│ │ │ │ │ └── 0.npy
│ │ │ └── model_source
│ │ │ │ ├── saved_model
│ │ │ │ └── dnn_medium
│ │ │ │ │ ├── keras_metadata.pb
│ │ │ │ │ ├── saved_model.pb
│ │ │ │ │ └── variables
│ │ │ │ │ ├── variables.data-00000-of-00001
│ │ │ │ │ └── variables.index
│ │ │ │ └── weights
│ │ │ │ ├── checkpoint
│ │ │ │ ├── dnn_0.86_ckpt.data-00000-of-00001
│ │ │ │ └── dnn_0.86_ckpt.index
│ │ │ ├── model_core_utils
│ │ │ ├── __init__.py
│ │ │ └── models.py
│ │ │ ├── optimisations.py
│ │ │ ├── recreate_model.sh
│ │ │ ├── requirements.txt
│ │ │ ├── train.py
│ │ │ └── validation_utils
│ │ │ └── labels.txt
│ ├── dnn_small
│ │ └── model_package_tf
│ │ │ ├── README.md
│ │ │ ├── convert_to_tflite.py
│ │ │ ├── data_processing
│ │ │ ├── __init__.py
│ │ │ └── data_preprocessing.py
│ │ │ ├── dnn_s_inference_keras.py
│ │ │ ├── dnn_s_inference_tflite.py
│ │ │ ├── evaluation.py
│ │ │ ├── how_to_guidance.ipynb
│ │ │ ├── model_archive
│ │ │ ├── TFLite
│ │ │ │ ├── tflite_fp32
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── definition.yaml
│ │ │ │ │ ├── dnn_s.tflite
│ │ │ │ │ ├── testing_input
│ │ │ │ │ │ └── input
│ │ │ │ │ │ │ └── 0.npy
│ │ │ │ │ └── testing_output
│ │ │ │ │ │ └── Identity
│ │ │ │ │ │ └── 0.npy
│ │ │ │ └── tflite_int8
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── definition.yaml
│ │ │ │ │ ├── dnn_s_quantized.tflite
│ │ │ │ │ ├── testing_input
│ │ │ │ │ └── input
│ │ │ │ │ │ └── 0.npy
│ │ │ │ │ └── testing_output
│ │ │ │ │ └── Identity
│ │ │ │ │ └── 0.npy
│ │ │ └── model_source
│ │ │ │ ├── saved_model
│ │ │ │ └── dnn_small
│ │ │ │ │ ├── keras_metadata.pb
│ │ │ │ │ ├── saved_model.pb
│ │ │ │ │ └── variables
│ │ │ │ │ ├── variables.data-00000-of-00001
│ │ │ │ │ └── variables.index
│ │ │ │ └── weights
│ │ │ │ ├── checkpoint
│ │ │ │ ├── dnn_0.84_ckpt.data-00000-of-00001
│ │ │ │ └── dnn_0.84_ckpt.index
│ │ │ ├── model_core_utils
│ │ │ ├── __init__.py
│ │ │ └── models.py
│ │ │ ├── optimisations.py
│ │ │ ├── recreate_model.sh
│ │ │ ├── requirements.txt
│ │ │ ├── train.py
│ │ │ └── validation_utils
│ │ │ └── labels.txt
│ ├── ds_cnn_large
│ │ └── model_package_tf
│ │ │ ├── README.md
│ │ │ ├── convert_to_tflite.py
│ │ │ ├── data_processing
│ │ │ ├── __init__.py
│ │ │ └── data_preprocessing.py
│ │ │ ├── ds_cnn_l_inference_keras.py
│ │ │ ├── ds_cnn_l_inference_tflite.py
│ │ │ ├── evaluation.py
│ │ │ ├── how_to_guidance.ipynb
│ │ │ ├── model_archive
│ │ │ ├── TFLite
│ │ │ │ ├── tflite_clustered_fp32
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── definition.yaml
│ │ │ │ │ ├── ds_cnn_l_clustered_fp32.tflite
│ │ │ │ │ ├── testing_input
│ │ │ │ │ │ └── input
│ │ │ │ │ │ │ └── 0.npy
│ │ │ │ │ └── testing_output
│ │ │ │ │ │ └── Identity
│ │ │ │ │ │ └── 0.npy
│ │ │ │ ├── tflite_clustered_int8
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── definition.yaml
│ │ │ │ │ ├── ds_cnn_l_clustered_int8.tflite
│ │ │ │ │ ├── testing_input
│ │ │ │ │ │ └── input
│ │ │ │ │ │ │ └── 0.npy
│ │ │ │ │ └── testing_output
│ │ │ │ │ │ └── Identity
│ │ │ │ │ │ └── 0.npy
│ │ │ │ ├── tflite_fp32
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── definition.yaml
│ │ │ │ │ ├── ds_cnn_l.tflite
│ │ │ │ │ ├── testing_input
│ │ │ │ │ │ └── input
│ │ │ │ │ │ │ └── 0.npy
│ │ │ │ │ └── testing_output
│ │ │ │ │ │ └── Identity
│ │ │ │ │ │ └── 0.npy
│ │ │ │ └── tflite_int8
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── definition.yaml
│ │ │ │ │ ├── ds_cnn_l_quantized.tflite
│ │ │ │ │ ├── testing_input
│ │ │ │ │ └── input
│ │ │ │ │ │ └── 0.npy
│ │ │ │ │ └── testing_output
│ │ │ │ │ └── Identity
│ │ │ │ │ └── 0.npy
│ │ │ └── model_source
│ │ │ │ ├── saved_model
│ │ │ │ └── ds_cnn_large
│ │ │ │ │ ├── keras_metadata.pb
│ │ │ │ │ ├── saved_model.pb
│ │ │ │ │ └── variables
│ │ │ │ │ ├── variables.data-00000-of-00001
│ │ │ │ │ └── variables.index
│ │ │ │ └── weights
│ │ │ │ ├── checkpoint
│ │ │ │ ├── ds_cnn_0.95_ckpt.data-00000-of-00001
│ │ │ │ └── ds_cnn_0.95_ckpt.index
│ │ │ ├── model_core_utils
│ │ │ ├── __init__.py
│ │ │ └── models.py
│ │ │ ├── optimisations.py
│ │ │ ├── recreate_model.sh
│ │ │ ├── requirements.txt
│ │ │ ├── train.py
│ │ │ └── validation_utils
│ │ │ └── labels.txt
│ ├── ds_cnn_medium
│ │ └── model_package_tf
│ │ │ ├── README.md
│ │ │ ├── convert_to_tflite.py
│ │ │ ├── data_processing
│ │ │ ├── __init__.py
│ │ │ └── data_preprocessing.py
│ │ │ ├── ds_cnn_m_inference_keras.py
│ │ │ ├── ds_cnn_m_inference_tflite.py
│ │ │ ├── evaluation.py
│ │ │ ├── how_to_guidance.ipynb
│ │ │ ├── model_archive
│ │ │ ├── TFLite
│ │ │ │ ├── tflite_fp32
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── definition.yaml
│ │ │ │ │ ├── ds_cnn_m.tflite
│ │ │ │ │ ├── testing_input
│ │ │ │ │ │ └── input
│ │ │ │ │ │ │ └── 0.npy
│ │ │ │ │ └── testing_output
│ │ │ │ │ │ └── Identity
│ │ │ │ │ │ └── 0.npy
│ │ │ │ └── tflite_int8
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── definition.yaml
│ │ │ │ │ ├── ds_cnn_m_quantized.tflite
│ │ │ │ │ ├── testing_input
│ │ │ │ │ └── input
│ │ │ │ │ │ └── 0.npy
│ │ │ │ │ └── testing_output
│ │ │ │ │ └── Identity
│ │ │ │ │ └── 0.npy
│ │ │ └── baseline
│ │ │ │ ├── saved_model
│ │ │ │ └── ds_cnn_medium
│ │ │ │ │ ├── keras_metadata.pb
│ │ │ │ │ ├── saved_model.pb
│ │ │ │ │ └── variables
│ │ │ │ │ ├── variables.data-00000-of-00001
│ │ │ │ │ └── variables.index
│ │ │ │ └── weights
│ │ │ │ ├── checkpoint
│ │ │ │ ├── ds_cnn_0.95_ckpt.data-00000-of-00001
│ │ │ │ └── ds_cnn_0.95_ckpt.index
│ │ │ ├── model_core_utils
│ │ │ ├── __init__.py
│ │ │ └── models.py
│ │ │ ├── optimisations.py
│ │ │ ├── recreate_model.sh
│ │ │ ├── requirements.txt
│ │ │ ├── train.py
│ │ │ └── validation_utils
│ │ │ └── labels.txt
│ ├── ds_cnn_small
│ │ └── model_package_tf
│ │ │ ├── README.md
│ │ │ ├── convert_to_tflite.py
│ │ │ ├── data_processing
│ │ │ ├── __init__.py
│ │ │ └── data_preprocessing.py
│ │ │ ├── ds_cnn_s_inference_keras.py
│ │ │ ├── ds_cnn_s_inference_tflite.py
│ │ │ ├── evaluation.py
│ │ │ ├── how_to_guidance.ipynb
│ │ │ ├── model_archive
│ │ │ ├── TFLite
│ │ │ │ ├── tflite_fp32
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── definition.yaml
│ │ │ │ │ ├── ds_cnn_s.tflite
│ │ │ │ │ ├── testing_input
│ │ │ │ │ │ └── input
│ │ │ │ │ │ │ └── 0.npy
│ │ │ │ │ └── testing_output
│ │ │ │ │ │ └── Identity
│ │ │ │ │ │ └── 0.npy
│ │ │ │ ├── tflite_int16
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── definition.yaml
│ │ │ │ │ ├── ds_cnn_s_quantized_int16.tflite
│ │ │ │ │ ├── testing_input
│ │ │ │ │ │ └── 0.npy
│ │ │ │ │ └── testing_output
│ │ │ │ │ │ └── 0.npy
│ │ │ │ └── tflite_int8
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── definition.yaml
│ │ │ │ │ ├── ds_cnn_s_quantized.tflite
│ │ │ │ │ ├── testing_input
│ │ │ │ │ └── input
│ │ │ │ │ │ └── 0.npy
│ │ │ │ │ └── testing_output
│ │ │ │ │ └── Identity
│ │ │ │ │ └── 0.npy
│ │ │ └── model_source
│ │ │ │ ├── saved_model
│ │ │ │ └── ds_cnn_small
│ │ │ │ │ ├── keras_metadata.pb
│ │ │ │ │ ├── saved_model.pb
│ │ │ │ │ └── variables
│ │ │ │ │ ├── variables.data-00000-of-00001
│ │ │ │ │ └── variables.index
│ │ │ │ └── weights
│ │ │ │ ├── checkpoint
│ │ │ │ ├── ds_cnn_0.94_ckpt.data-00000-of-00001
│ │ │ │ └── ds_cnn_0.94_ckpt.index
│ │ │ ├── model_core_utils
│ │ │ ├── __init__.py
│ │ │ └── models.py
│ │ │ ├── optimisations.py
│ │ │ ├── recreate_model.sh
│ │ │ ├── requirements.txt
│ │ │ ├── train.py
│ │ │ └── validation_utils
│ │ │ └── labels.txt
│ ├── micronet_large
│ │ └── tflite_int8
│ │ │ ├── README.md
│ │ │ ├── definition.yaml
│ │ │ ├── get_class_labels.sh
│ │ │ ├── kws_micronet_l.tflite
│ │ │ ├── testing_input
│ │ │ └── input
│ │ │ │ └── 0.npy
│ │ │ └── testing_output
│ │ │ └── Identity
│ │ │ └── 0.npy
│ ├── micronet_medium
│ │ └── tflite_int8
│ │ │ ├── README.md
│ │ │ ├── definition.yaml
│ │ │ ├── get_class_labels.sh
│ │ │ ├── kws_micronet_m.tflite
│ │ │ ├── testing_input
│ │ │ └── input
│ │ │ │ └── 0.npy
│ │ │ └── testing_output
│ │ │ └── Identity
│ │ │ └── 0.npy
│ └── micronet_small
│ │ └── tflite_int8
│ │ ├── README.md
│ │ ├── definition.yaml
│ │ ├── get_class_labels.sh
│ │ ├── kws_micronet_s.tflite
│ │ ├── testing_input
│ │ └── input
│ │ │ └── 0.npy
│ │ └── testing_output
│ │ └── Identity
│ │ └── 0.npy
├── noise_suppression
│ └── RNNoise
│ │ └── tflite_int8
│ │ ├── README.md
│ │ ├── definition.yaml
│ │ ├── recreate_model
│ │ ├── README.md
│ │ ├── convert.py
│ │ ├── data.py
│ │ ├── get_data.sh
│ │ ├── model.py
│ │ ├── requirements.txt
│ │ ├── rnnoise_pre_processing.py
│ │ ├── test.py
│ │ ├── train.py
│ │ └── train_and_quantise_model.sh
│ │ ├── rnnoise_INT8.tflite
│ │ ├── testing_input
│ │ ├── denoise_gru_prev_state_int8
│ │ │ └── 0.npy
│ │ ├── main_input_int8
│ │ │ └── 0.npy
│ │ ├── noise_gru_prev_state_int8
│ │ │ └── 0.npy
│ │ └── vad_gru_prev_state_int8
│ │ │ └── 0.npy
│ │ └── testing_output
│ │ ├── Identity_1_int8
│ │ └── 0.npy
│ │ ├── Identity_2_int8
│ │ └── 0.npy
│ │ ├── Identity_3_int8
│ │ └── 0.npy
│ │ ├── Identity_4_int8
│ │ └── 0.npy
│ │ └── Identity_int8
│ │ └── 0.npy
├── object_detection
│ ├── ssd_mobilenet_v1
│ │ ├── tflite_fp32
│ │ │ ├── README.md
│ │ │ ├── definition.yaml
│ │ │ ├── get_class_labels.sh
│ │ │ ├── recreate_model
│ │ │ │ ├── README.md
│ │ │ │ ├── recreate_model.sh
│ │ │ │ └── requirements.txt
│ │ │ ├── scripts
│ │ │ │ └── export_labels.py
│ │ │ ├── ssd_mobilenet_v1.tflite
│ │ │ ├── testing_input
│ │ │ │ └── normalized_input_image_tensor
│ │ │ │ │ └── 0.npy
│ │ │ └── testing_output
│ │ │ │ ├── TFLite_Detection_PostProcess
│ │ │ │ └── 0.npy
│ │ │ │ ├── TFLite_Detection_PostProcess:1
│ │ │ │ └── 0.npy
│ │ │ │ ├── TFLite_Detection_PostProcess:2
│ │ │ │ └── 0.npy
│ │ │ │ └── TFLite_Detection_PostProcess:3
│ │ │ │ └── 0.npy
│ │ ├── tflite_int8
│ │ │ ├── README.md
│ │ │ ├── definition.yaml
│ │ │ ├── get_class_labels.sh
│ │ │ ├── recreate_model
│ │ │ │ ├── README.md
│ │ │ │ ├── benchmark_model.py
│ │ │ │ ├── benchmark_ssd_mobilenet_v1.sh
│ │ │ │ ├── quantize_model.py
│ │ │ │ ├── quantize_ssd_mobilenet_v1.sh
│ │ │ │ └── requirements.txt
│ │ │ ├── scripts
│ │ │ │ └── export_labels.py
│ │ │ ├── ssd_mobilenet_v1.tflite
│ │ │ ├── testing_input
│ │ │ │ └── tfl.quantize
│ │ │ │ │ └── 0.npy
│ │ │ └── testing_output
│ │ │ │ ├── TFLite_Detection_PostProcess:01
│ │ │ │ └── 0.npy
│ │ │ │ ├── TFLite_Detection_PostProcess:02
│ │ │ │ └── 0.npy
│ │ │ │ ├── TFLite_Detection_PostProcess:03
│ │ │ │ └── 0.npy
│ │ │ │ └── TFLite_Detection_PostProcess:04
│ │ │ │ └── 0.npy
│ │ └── tflite_uint8
│ │ │ ├── README.md
│ │ │ ├── definition.yaml
│ │ │ ├── get_class_labels.sh
│ │ │ ├── recreate_model
│ │ │ ├── README.md
│ │ │ ├── recreate_model.sh
│ │ │ └── requirements.txt
│ │ │ ├── scripts
│ │ │ └── export_labels.py
│ │ │ ├── ssd_mobilenet_v1.tflite
│ │ │ ├── testing_input
│ │ │ └── normalized_input_image_tensor
│ │ │ │ └── 0.npy
│ │ │ └── testing_output
│ │ │ ├── TFLite_Detection_PostProcess
│ │ │ └── 0.npy
│ │ │ ├── TFLite_Detection_PostProcess:1
│ │ │ └── 0.npy
│ │ │ ├── TFLite_Detection_PostProcess:2
│ │ │ └── 0.npy
│ │ │ └── TFLite_Detection_PostProcess:3
│ │ │ └── 0.npy
│ └── yolo_v3_tiny
│ │ └── tflite_fp32
│ │ ├── README.md
│ │ ├── definition.yaml
│ │ ├── get_class_labels.sh
│ │ ├── recreate_model
│ │ ├── README.md
│ │ ├── recreate_model.sh
│ │ └── requirements.txt
│ │ ├── testing_input
│ │ └── inputs
│ │ │ └── 0.npy
│ │ ├── testing_output
│ │ └── output_boxes
│ │ │ └── 0.npy
│ │ └── yolo_v3_tiny_darknet_fp32.tflite
├── speech_recognition
│ ├── tiny_wav2letter
│ │ ├── tflite_int8
│ │ │ ├── README.md
│ │ │ ├── definition.yaml
│ │ │ ├── demo_input
│ │ │ │ └── 84-121550-0000.flac
│ │ │ ├── inference_demo.ipynb
│ │ │ ├── model_development_guide.md
│ │ │ ├── recreate_code
│ │ │ │ ├── .idea
│ │ │ │ │ ├── misc.xml
│ │ │ │ │ └── workspace.xml
│ │ │ │ ├── README.md
│ │ │ │ ├── __pycache__
│ │ │ │ │ ├── load_mfccs.cpython-36.pyc
│ │ │ │ │ ├── tinywav2letter.cpython-36.pyc
│ │ │ │ │ └── train_model.cpython-36.pyc
│ │ │ │ ├── corpus.py
│ │ │ │ ├── evaluate_saved_weights.py
│ │ │ │ ├── load_mfccs.py
│ │ │ │ ├── preprocessing.py
│ │ │ │ ├── preprocessing_convert_to_flac.py
│ │ │ │ ├── preprocessing_fluent_speech_commands.py
│ │ │ │ ├── prune_and_quantise_model.py
│ │ │ │ ├── recreate_model.sh
│ │ │ │ ├── requirements.txt
│ │ │ │ ├── saved_models
│ │ │ │ │ ├── pruned_tiny_wav2letter
│ │ │ │ │ │ ├── saved_model.pb
│ │ │ │ │ │ └── variables
│ │ │ │ │ │ │ ├── variables.data-00000-of-00001
│ │ │ │ │ │ │ └── variables.index
│ │ │ │ │ └── tiny_wav2letter
│ │ │ │ │ │ ├── saved_model.pb
│ │ │ │ │ │ └── variables
│ │ │ │ │ │ ├── variables.data-00000-of-00001
│ │ │ │ │ │ └── variables.index
│ │ │ │ ├── tinywav2letter.py
│ │ │ │ └── train_model.py
│ │ │ ├── testing_input
│ │ │ │ └── input_1_int8
│ │ │ │ │ └── 0.npy
│ │ │ ├── testing_output
│ │ │ │ └── Identity_int8
│ │ │ │ │ └── 0.npy
│ │ │ └── tiny_wav2letter_int8.tflite
│ │ └── tflite_pruned_int8
│ │ │ ├── README.md
│ │ │ ├── definition.yaml
│ │ │ ├── demo_input
│ │ │ └── 84-121550-0000.flac
│ │ │ ├── inference_demo.ipynb
│ │ │ ├── model_development_guide.md
│ │ │ ├── recreate_code
│ │ │ ├── .idea
│ │ │ │ ├── misc.xml
│ │ │ │ └── workspace.xml
│ │ │ ├── README.md
│ │ │ ├── __pycache__
│ │ │ │ ├── load_mfccs.cpython-36.pyc
│ │ │ │ ├── tinywav2letter.cpython-36.pyc
│ │ │ │ └── train_model.cpython-36.pyc
│ │ │ ├── corpus.py
│ │ │ ├── evaluate_saved_weights.py
│ │ │ ├── load_mfccs.py
│ │ │ ├── preprocessing.py
│ │ │ ├── preprocessing_convert_to_flac.py
│ │ │ ├── preprocessing_fluent_speech_commands.py
│ │ │ ├── prune_and_quantise_model.py
│ │ │ ├── recreate_model.sh
│ │ │ ├── requirements.txt
│ │ │ ├── saved_models
│ │ │ │ ├── pruned_tiny_wav2letter
│ │ │ │ │ ├── saved_model.pb
│ │ │ │ │ └── variables
│ │ │ │ │ │ ├── variables.data-00000-of-00001
│ │ │ │ │ │ └── variables.index
│ │ │ │ └── tiny_wav2letter
│ │ │ │ │ ├── saved_model.pb
│ │ │ │ │ └── variables
│ │ │ │ │ ├── variables.data-00000-of-00001
│ │ │ │ │ └── variables.index
│ │ │ ├── tinywav2letter.py
│ │ │ └── train_model.py
│ │ │ ├── testing_input
│ │ │ └── input_1_int8
│ │ │ │ └── 0.npy
│ │ │ ├── testing_output
│ │ │ └── Identity_int8
│ │ │ │ └── 0.npy
│ │ │ └── tiny_wav2letter_pruned_int8.tflite
│ └── wav2letter
│ │ ├── tflite_int8
│ │ ├── README.md
│ │ ├── definition.yaml
│ │ ├── get_class_labels.sh
│ │ ├── scripts
│ │ │ └── create_labels.py
│ │ ├── testing_input
│ │ │ └── input_2_int8
│ │ │ │ └── 0.npy
│ │ ├── testing_output
│ │ │ └── Identity_int8
│ │ │ │ └── 0.npy
│ │ └── wav2letter_int8.tflite
│ │ └── tflite_pruned_int8
│ │ ├── README.md
│ │ ├── definition.yaml
│ │ ├── get_class_labels.sh
│ │ ├── recreate_model
│ │ ├── README.md
│ │ ├── librispeech_mfcc.py
│ │ ├── prune_quantize_model.py
│ │ ├── recreate_model.sh
│ │ ├── requirements.txt
│ │ ├── wav2letter.py
│ │ └── weights
│ │ │ └── wav2letter.h5
│ │ ├── scripts
│ │ └── create_labels.py
│ │ ├── testing_input
│ │ └── input_4
│ │ │ └── 0.npy
│ │ ├── testing_output
│ │ └── Identity
│ │ │ └── 0.npy
│ │ └── wav2letter_pruned_int8.tflite
├── superresolution
│ └── SESR
│ │ └── tflite_int8
│ │ ├── README.md
│ │ ├── SESR_1080p_to_4K_withD2S_full_int8.tflite
│ │ ├── definition.yaml
│ │ ├── testing_input
│ │ └── net_input
│ │ │ └── 0.npy
│ │ └── testing_output
│ │ └── net_output
│ │ └── 0.npy
└── visual_wake_words
│ ├── micronet_vww2
│ └── tflite_int8
│ │ ├── README.md
│ │ ├── definition.yaml
│ │ ├── get_class_labels.sh
│ │ ├── testing_input
│ │ └── input
│ │ │ └── 0.npy
│ │ ├── testing_output
│ │ └── Identity
│ │ │ └── 0.npy
│ │ └── vww2_50_50_INT8.tflite
│ ├── micronet_vww3
│ └── tflite_int8
│ │ ├── README.md
│ │ ├── definition.yaml
│ │ ├── get_class_labels.sh
│ │ ├── testing_input
│ │ └── input
│ │ │ └── 0.npy
│ │ ├── testing_output
│ │ └── Identity
│ │ │ └── 0.npy
│ │ └── vww3_128_128_INT8.tflite
│ └── micronet_vww4
│ └── tflite_int8
│ ├── README.md
│ ├── definition.yaml
│ ├── get_class_labels.sh
│ ├── testing_input
│ └── input
│ │ └── 0.npy
│ ├── testing_output
│ └── Identity
│ │ └── 0.npy
│ └── vww4_128_128_INT8.tflite
└── tutorials
└── transformer_tutorials
├── README.md
├── ViT_2x4-PQAT.ipynb
├── ViT_PCQAT.ipynb
├── install.sh
├── requirements.txt
├── translation.ipynb
└── translation_PQAT.ipynb
/.gitattributes:
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1 | *.tflite filter=lfs diff=lfs merge=lfs -text
2 | *.pb filter=lfs diff=lfs merge=lfs -text
3 | *.data* filter=lfs diff=lfs merge=lfs -text
4 | *.index filter=lfs diff=lfs merge=lfs -text
5 | *.npy filter=lfs diff=lfs merge=lfs -text
6 | *.h5 filter=lfs diff=lfs merge=lfs -text
7 |
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/.gitignore:
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1 | .DS_STORE
2 | .vscode
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/models/anomaly_detection/micronet_large/tflite_int8/definition.yaml:
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1 | benchmark:
2 | DCASE 2020 Task 2 Slide rail:
3 | AUC: 0.968
4 | description: This is a fully quantized version (asymmetrical int8) of the MicroNet
5 | Large model developed by Arm, from the MicroNets paper. It is trained on the 'slide
6 | rail' task from http://dcase.community/challenge2020/task-unsupervised-detection-of-anomalous-sounds.
7 | license:
8 | - Apache-2.0
9 | network:
10 | file_size_bytes: 442000
11 | filename: ad_large_int8.tflite
12 | framework: TensorFlow Lite
13 | hash:
14 | algorithm: sha1
15 | value: 0b7e7776c79fac28c186b2ba00314a05b7faadbf
16 | provenance: https://arxiv.org/pdf/2010.11267.pdf
17 | network_parameters:
18 | input_nodes:
19 | - description: Input is 64 steps of a Log Mel Spectrogram using 64 mels resized
20 | to 32x32.
21 | example_input:
22 | path: models/anomaly_detection/micronet_large/tflite_int8/testing_input/input
23 | name: input
24 | shape:
25 | - 1
26 | - 32
27 | - 32
28 | - 1
29 | output_nodes:
30 | - description: Raw logits corresponding to different machine IDs being anomalous
31 | name: Identity
32 | shape:
33 | - 1
34 | - 8
35 | test_output_path: models/anomaly_detection/micronet_large/tflite_int8/testing_output/Identity
36 | operators:
37 | TensorFlow Lite:
38 | - AVERAGE_POOL_2D
39 | - CONV_2D
40 | - DEPTHWISE_CONV_2D
41 | - RELU6
42 | - RESHAPE
43 | paper: https://arxiv.org/pdf/2010.11267.pdf
44 |
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/models/anomaly_detection/micronet_large/tflite_int8/get_class_labels.sh:
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1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | touch ./labelmappings.txt
20 | echo "id0" >> labelmappings.txt
21 | echo "id2" >> labelmappings.txt
22 | echo "id4" >> labelmappings.txt
23 | echo "id6" >> labelmappings.txt
24 |
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3 | size 1152
4 |
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/models/anomaly_detection/micronet_medium/tflite_int8/ad_medium_int8.tflite:
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3 | size 463792
4 |
--------------------------------------------------------------------------------
/models/anomaly_detection/micronet_medium/tflite_int8/definition.yaml:
--------------------------------------------------------------------------------
1 | benchmark:
2 | DCASE 2020 Task 2 Slide rail:
3 | AUC: 0.963
4 | description: This is a fully quantized version (asymmetrical int8) of the MicroNet
5 | Medium model developed by Arm, from the MicroNets paper. It is trained on the 'slide
6 | rail' task from http://dcase.community/challenge2020/task-unsupervised-detection-of-anomalous-sounds.
7 | license:
8 | - Apache-2.0
9 | network:
10 | file_size_bytes: 463792
11 | filename: ad_medium_int8.tflite
12 | framework: TensorFlow Lite
13 | hash:
14 | algorithm: sha1
15 | value: ed709fccb1d57393cbc88f36da38a4ab70f97b4a
16 | provenance: https://arxiv.org/pdf/2010.11267.pdf
17 | network_parameters:
18 | input_nodes:
19 | - description: Input is 64 steps of a Log Mel Spectrogram using 64 mels resized
20 | to 32x32.
21 | example_input:
22 | path: models/anomaly_detection/micronet_medium/tflite_int8/testing_input/input
23 | name: input
24 | shape:
25 | - 1
26 | - 32
27 | - 32
28 | - 1
29 | output_nodes:
30 | - description: Raw logits corresponding to different machine IDs being anomalous
31 | name: Identity
32 | shape:
33 | - 1
34 | - 8
35 | test_output_path: models/anomaly_detection/micronet_medium/tflite_int8/testing_output/Identity
36 | operators:
37 | TensorFlow Lite:
38 | - AVERAGE_POOL_2D
39 | - CONV_2D
40 | - DEPTHWISE_CONV_2D
41 | - RELU6
42 | - RESHAPE
43 | paper: https://arxiv.org/pdf/2010.11267.pdf
44 |
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/models/anomaly_detection/micronet_medium/tflite_int8/get_class_labels.sh:
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1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | touch ./labelmappings.txt
20 | echo "id0" >> labelmappings.txt
21 | echo "id2" >> labelmappings.txt
22 | echo "id4" >> labelmappings.txt
23 | echo "id6" >> labelmappings.txt
24 |
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1 | benchmark:
2 | DCASE 2020 Task 2 Slide rail:
3 | AUC: 0.955
4 | description: This is a fully quantized version (asymmetrical int8) of the MicroNet
5 | Small model developed by Arm, from the MicroNets paper. It is trained on the 'slide
6 | rail' task from http://dcase.community/challenge2020/task-unsupervised-detection-of-anomalous-sounds.
7 | license:
8 | - Apache-2.0
9 | network:
10 | file_size_bytes: 252848
11 | filename: ad_small_int8.tflite
12 | framework: TensorFlow Lite
13 | hash:
14 | algorithm: sha1
15 | value: 6dc73515caea226065c3408d82d857b9908e3ffa
16 | provenance: https://arxiv.org/pdf/2010.11267.pdf
17 | network_parameters:
18 | input_nodes:
19 | - description: Input is 64 steps of a Log Mel Spectrogram using 64 mels resized
20 | to 32x32.
21 | example_input:
22 | path: models/anomaly_detection/micronet_small/tflite_int8/testing_input/input
23 | name: input
24 | shape:
25 | - 1
26 | - 32
27 | - 32
28 | - 1
29 | output_nodes:
30 | - description: Raw logits corresponding to different machine IDs being anomalous
31 | name: Identity
32 | shape:
33 | - 1
34 | - 8
35 | test_output_path: models/anomaly_detection/micronet_small/tflite_int8/testing_output/Identity
36 | operators:
37 | TensorFlow Lite:
38 | - AVERAGE_POOL_2D
39 | - CONV_2D
40 | - DEPTHWISE_CONV_2D
41 | - RELU6
42 | - RESHAPE
43 | paper: https://arxiv.org/pdf/2010.11267.pdf
44 |
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/models/anomaly_detection/micronet_small/tflite_int8/get_class_labels.sh:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | touch ./labelmappings.txt
20 | echo "id0" >> labelmappings.txt
21 | echo "id2" >> labelmappings.txt
22 | echo "id4" >> labelmappings.txt
23 | echo "id6" >> labelmappings.txt
24 |
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/models/experimental/efficientnet_lite0_224/README.md:
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1 | # image_classification/efficientnet_lite0_224/tflite_int8
2 |
3 | ## Description
4 | This work is developed from the codebase located [here](https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/lite/README.md) and is under an Apache 2 license available [here](https://github.com/tensorflow/tpu/blob/master/LICENSE).
5 |
6 | The original networks, which we have optimized via tooling but left otherwise unchanged are copyright the tensorflow authors as in the license file linked.
7 |
8 | ## License
9 | [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html)
10 |
11 | ## Network Information
12 | | Network Information | Value |
13 | |---------------------|-------|
14 | | Framework | TensorFlow Lite |
15 | | SHA-1 Hash | 35f9dafaf25f8abf2225265b0724979a68bf6d67 |
16 | | Size (Bytes) | 5422760 |
17 | | Provenance | https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/lite/efficientnet-lite0.tar.gz |
18 | | Paper | https://arxiv.org/pdf/1905.11946.pdf |
19 |
20 |
21 | ## Accuracy
22 | Dataset: ILSVRC 2012
23 |
24 | | Metric | Value |
25 | |--------|-------|
26 | | top_1_accuracy | 0.744 |
27 |
28 | ## Network Inputs
29 | | Input Node Name | Shape | Example Path | Example Type | Example Use Case |
30 | |-----------------|-------|--------------|------------------|--------------|
31 | | images | (1, 224, 224, 3) | models/image_classification/efficientnet_lite0_224/tflite_int8/testing_input | | Typical ImageNet-style single-batch cat resized to 224x224. |
32 |
33 | ## Network Outputs
34 | | Output Node Name | Shape | Description |
35 | |------------------|-------|-------------|
36 | | Softmax | (1, 1000) | Probability distribution over 1000 ImageNet classes with uint8 values. |
37 |
38 |
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3 | size 5422760
4 |
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/models/experimental/har_cnn/README.md:
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1 | # har_int8.tflite
2 |
3 | ## Description
4 | Model internally developed.
5 | Based on dataset https://www.cis.fordham.edu/wisdm/dataset.php
6 |
7 | ## License
8 | Apache v2
9 |
10 | ## Network Inputs
11 | | Input Node Name | Shape | Example Path | Example Type | Example Use Case |
12 | |-----------------|-------|--------------|------------------|--------------|
13 | | conv2d_input | (1, 90, 3, 1) | models/ | | Accelerometer data of someone walking. |
14 |
15 | ## Network Outputs
16 | | Output Node Name | Shape | Description |
17 | |------------------|-------|-------------|
18 | | Identity | (1, 6) | Class probability of 6 classes |
19 |
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/models/experimental/har_cnn/har_int8.tflite:
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3 | size 747936
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/models/experimental/ssd_mobilenet_v3_int8/README.md:
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1 | # ssd_mobilenet_v3_int8.tflite
2 |
3 | ## Description
4 | http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v3_small_coco_2019_08_14.tar.gz
5 | This model is directly derived from the above URLs, with only the post processing removed
6 |
7 | ## License
8 | https://github.com/tensorflow/models/blob/master/LICENSE
9 | Apache v2
10 |
11 | ## Network Inputs
12 | | Input Node Name | Shape | Example Path | Example Type | Example Use Case |
13 | |-----------------|-------|--------------|------------------|--------------|
14 | | normalized_input_image_tensor | (1, 320, 320, 3) | N/A | N/A | N/A |
15 |
16 | ## Network Outputs
17 | | Output Node Name | Shape | Description |
18 | |------------------|-------|-------------|
19 | | raw_outputs/class_predictions | (1, 2034, 91) | Class predictions |
20 | | raw_outputs/box_encodings | (1, 2034, 4) | Boxe Encodings |
21 |
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/models/experimental/ssd_mobilenet_v3_int8/ssd_mobilenet_v3_int8.tflite:
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4 |
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/models/experimental/yolov3_416_416_backbone_mltools_int8/README.md:
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1 | # object_detection/yolo_v3_backbone_mltools/tflite_int8
2 |
3 | ## Description
4 | Backbone of the Yolo v3 model with an input size of 416 x 416. The backbone is quantized with an int8 precision using the first 1000 images of the COCO 2014 training set for calibration. The DarkNet original pre-trained weights are used as initial weights.
5 |
6 | ## License
7 | [MIT](https://spdx.org/licenses/MIT.html)
8 | [MIT]https://github.com/zzh8829/yolov3-tf2/blob/master/LICENSE
9 |
10 | ## Network Information
11 | | Network Information | Value |
12 | |---------------------|-------|
13 | | Framework | TensorFlow Lite |
14 | | SHA-1 Hash | 4adc0b716c5af29d957396fab2bcbc460e8b94ee |
15 | | Size (Bytes) | 62958128 |
16 | | Provenance | https://confluence.arm.com/display/MLENG/Yolo+v3 |
17 | | Paper | https://pjreddie.com/media/files/papers/YOLOv3.pdf |
18 |
19 |
20 | ## Accuracy
21 | Dataset: coco-val-2014
22 |
23 | | Metric | Value |
24 | |--------|-------|
25 | | mAP50 | 0.563 |
26 |
27 | ## Network Inputs
28 | | Input Node Name | Shape | Example Path | Example Type | Example Use Case |
29 | |-----------------|-------|--------------|------------------|--------------|
30 | | input_int8 | (1, 416, 416, 3) | models/object_detection/yolo_v3_backbone_mltools/tflite_int8/testing_input/0.npy | int8 | |
31 |
32 | ## Network Outputs
33 | | Output Node Name | Shape | Description |
34 | |------------------|-------|-------------|
35 | | Identity_int8 | (1, 13, 13, 3, 85) | None |
36 | | Identity_1_int8 | (1, 26, 26, 3, 85) | None |
37 | | Identity_2_int8 | (1, 52, 52, 3, 85) | None |
38 |
39 |
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/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/definition.yaml:
--------------------------------------------------------------------------------
1 | benchmark:
2 | ILSVRC 2012:
3 | top-1-accuracy: 0.697
4 | description: "INT8 quantised version of MobileNet v2 model. Trained on ImageNet."
5 | license:
6 | - Apache-2.0
7 | network:
8 | file_size_bytes: 4020936
9 | filename: mobilenet_v2_1.0_224_INT8.tflite
10 | framework: TensorFlow Lite
11 | hash:
12 | algorithm: sha1
13 | value: 8de7996dfeadb5ab6f09e3114f3905fd03879eee
14 | provenance: https://arxiv.org/pdf/1801.04381.pdf
15 | network_parameters:
16 | input_nodes:
17 | - description: Single 224x224 RGB image with INT8 values between -128 and 127
18 | example_input:
19 | path: models/image_classification/mobilenet_v2_1.0_224/tflite_int8/testing_input/tfl.quantize
20 | name: tfl.quantize
21 | shape:
22 | - 1
23 | - 224
24 | - 224
25 | - 3
26 | output_nodes:
27 | - description: Per-class confidence for 1001 ImageNet classes
28 | name: MobilenetV2/Predictions/Reshape_11
29 | shape:
30 | - 1
31 | - 1001
32 | test_output_path: models/image_classification/mobilenet_v2_1.0_224/tflite_int8/testing_output/MobilenetV2/Predictions/Reshape_11
33 | operators:
34 | TensorFlow Lite:
35 | - ADD
36 | - AVERAGE_POOL_2D
37 | - CONV_2D
38 | - DEPTHWISE_CONV_2D
39 | - DEQUANTIZE
40 | - QUANTIZE
41 | - RELU6
42 | - RESHAPE
43 | - SOFTMAX
44 | paper: https://arxiv.org/pdf/1801.04381.pdf
45 |
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/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/get_class_labels.sh:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | wget https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt
20 | python scripts/process_labels.py --path imagenet1000_clsidx_to_labels.txt
21 |
22 | rm imagenet1000_clsidx_to_labels.txt
23 |
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/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/mobilenet_v2_1.0_224_INT8.tflite:
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/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/recreate_model/README.md:
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1 | # MobileNet v2 INT8 Re-Creation
2 | This folder contains scripts that allow you to re-create the model and benchmark it's performance.
3 |
4 | ## Requirements
5 | The scripts in this folder requires that the following must be installed:
6 | - Python 3.7
7 |
8 | ## Required Datasets
9 | Quantising and Benchmarking the model requires ImageNet Validation Set (ILSVRC2012). This can either be provided as a TFRecord file or in the form of images, with a text file providing the corresponding class labels. The script "write_tfrecord.py" then writes the images to the required TFRecord format for the quantisation scripts.
10 | In the case of raw images, the images themselves should be stored in 'data --> validation_data --> validation_images --> ILSVRC_val_00000001.JPEG, ILSVRC_val_00000002.JPEG ...' while the text file should be saved as 'data --> validation_data --> val.txt'. "write_tfrecord.py" will create a TFRecord file "data --> validation_set --> validation-dataset.tfrecord"
11 | In the case of the ImageNet Validation Set already being present as TFRecords, save them as 'data --> validation_data --> validation-dataset1.tfrecord, validation-dataset2.tfrecord ..."
12 |
13 | ## Running The Script
14 | ### Recreate The Model
15 | Run the following command in a terminal: `./quantize_mobilenet_v2.sh`
16 |
17 | ### Benchmarking The Model
18 | Run the following command in a terminal: `./benchmark_mobilenet_v2.sh`
19 |
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/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/recreate_model/benchmark_mobilenetv2.sh:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | python3.7 -m venv venv
20 |
21 | source venv/bin/activate
22 | pip install --upgrade pip
23 | pip install -r requirements.txt
24 |
25 | python benchmark_model.py --path tflite/mobilenet_v2_1.0_224_INT8.tflite
26 |
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/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/recreate_model/quantise_mobilenetv2.sh:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | python3.7 -m venv venv
20 |
21 | source venv/bin/activate
22 | pip install --upgrade pip
23 | pip install -r requirements.txt
24 |
25 | wget https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.4_224.tgz
26 | tar -xvf mobilenet_v2_1.4_224.tgz
27 |
28 | python quantise_model.py
29 |
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/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/recreate_model/requirements.txt:
--------------------------------------------------------------------------------
1 | absl-py==0.12.0
2 | astunparse==1.6.3
3 | cachetools==4.2.2
4 | certifi==2021.5.30
5 | chardet==4.0.0
6 | cycler==0.10.0
7 | dm-tree==0.1.6
8 | flatbuffers==1.12
9 | gast==0.3.3
10 | google-auth==1.31.0
11 | google-auth-oauthlib==0.4.4
12 | google-pasta==0.2.0
13 | grpcio==1.32.0
14 | h5py==2.10.0
15 | idna==2.10
16 | keras-nightly==2.5.0.dev2021032900
17 | Keras-Preprocessing==1.1.2
18 | kiwisolver==1.3.1
19 | Markdown==3.3.4
20 | matplotlib==3.4.2
21 | numpy==1.19.5
22 | oauthlib==3.1.1
23 | opt-einsum==3.3.0
24 | Pillow==8.2.0
25 | protobuf==3.17.3
26 | pyasn1==0.4.8
27 | pyasn1-modules==0.2.8
28 | pyparsing==2.4.7
29 | python-dateutil==2.8.1
30 | requests==2.25.1
31 | requests-oauthlib==1.3.0
32 | rsa==4.7.2
33 | six==1.15.0
34 | tensorboard==2.5.0
35 | tensorboard-data-server==0.6.1
36 | tensorboard-plugin-wit==1.8.0
37 | tensorflow==2.4.1
38 | tensorflow-estimator==2.4.0
39 | termcolor==1.1.0
40 | typing-extensions==3.7.4.3
41 | urllib3==1.26.5
42 | Werkzeug==2.0.1
43 | wrapt==1.12.1
44 |
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/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/scripts/process_labels.py:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | import argparse
18 | import ast
19 |
20 | if __name__ == "__main__":
21 | parser = argparse.ArgumentParser(description="Process ImageNet labels.")
22 | parser.add_argument("--path", type=str, required=True)
23 |
24 | args = parser.parse_args()
25 |
26 | with open(args.path, "r") as f:
27 | data = f.read()
28 |
29 | labels = ast.literal_eval(data)
30 |
31 | # Include the background class as there are 1001 classes
32 | class_labels = ["background"]
33 |
34 | for _, l in labels.items():
35 | class_labels.append(l)
36 |
37 | with open("labelmappings.txt", "w") as f:
38 | for l in class_labels:
39 | f.write("{}\n".format(l))
40 |
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1 | benchmark:
2 | ILSVRC 2012:
3 | top_1_accuracy: 0.708
4 | description: MobileNet v2 is an efficient image classification neural network, targeted
5 | for mobile and embedded use cases. This model is trained on the ImageNet dataset
6 | and is quantized to the UINT8 datatype by Google.
7 | license: Apache-2.0
8 | network:
9 | file_size_bytes: 3577760
10 | filename: mobilenet_v2_1.0_224_quantized_1_default_1.tflite
11 | framework: TensorFlow Lite
12 | hash:
13 | algorithm: sha1
14 | value: 275c9649cb395139103b6d15f53011b1b949ad00
15 | provenance: https://tfhub.dev/tensorflow/lite-model/mobilenet_v2_1.0_224_quantized/1/default/1
16 | network_parameters:
17 | input_nodes:
18 | - description: Single 224x224 RGB image with UINT8 values between 0 and 255
19 | example_input:
20 | path: models/image_classification/mobilenet_v2_1.0_224/tflite_uint8/testing_input/input
21 | name: input
22 | shape:
23 | - 1
24 | - 224
25 | - 224
26 | - 3
27 | output_nodes:
28 | - description: Per-class confidence for 1001 ImageNet classes
29 | name: output
30 | shape:
31 | - 1
32 | - 1001
33 | test_output_path: models/image_classification/mobilenet_v2_1.0_224/tflite_uint8/testing_output/output
34 | operators:
35 | TensorFlow Lite:
36 | - ADD
37 | - AVERAGE_POOL_2D
38 | - CONV_2D
39 | - DEPTHWISE_CONV_2D
40 | - RESHAPE
41 | paper: https://arxiv.org/pdf/1801.04381.pdf
42 |
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/models/image_classification/mobilenet_v2_1.0_224/tflite_uint8/get_class_labels.sh:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | wget https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt
20 | python scripts/process_labels.py --path imagenet1000_clsidx_to_labels.txt
21 |
22 | rm imagenet1000_clsidx_to_labels.txt
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/models/image_classification/mobilenet_v2_1.0_224/tflite_uint8/scripts/process_labels.py:
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1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | import argparse
18 | import ast
19 |
20 | if __name__ == "__main__":
21 | parser = argparse.ArgumentParser(description="Process ImageNet labels.")
22 | parser.add_argument("--path", type=str, required=True)
23 |
24 | args = parser.parse_args()
25 |
26 | with open(args.path, "r") as f:
27 | data = f.read()
28 |
29 | labels = ast.literal_eval(data)
30 |
31 | # Include the background class as there are 1001 classes
32 | class_labels = ["background"]
33 |
34 | for _, l in labels.items():
35 | class_labels.append(l)
36 |
37 | with open("labelmappings.txt", "w") as f:
38 | for l in class_labels:
39 | f.write("{}\n".format(l))
40 |
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1 | benchmark:
2 | benchmark_metrics:
3 | accuracy: 93.44%
4 | benchmark_name: Google Speech Commands test set
5 | description: This is a floating point fp32 version of the CNN Large model developed
6 | by Arm, from the Hello Edge paper.
7 | license:
8 | - Apache-2.0
9 | network:
10 | datatype: fp32
11 | file_size_bytes: 1908316
12 | filename: cnn_l.tflite
13 | framework: TensorFlow Lite
14 | hash:
15 | algorithm: sha1
16 | value: e77e0f185dd6b7b9adcb9d867279a6c0a0ecbf02
17 | provenance: https://arxiv.org/abs/1711.07128
18 | training: Trained by Arm
19 | network_parameters:
20 | input_nodes:
21 | - description: The input is a processed MFCCs of shape (1, 490)
22 | example_input:
23 | path: models/keyword_spotting/cnn_large/model_package_tf/model_archive/TFLite/tflite_fp32/testing_input/input
24 | shape:
25 | - 1
26 | - 490
27 | type: fp32
28 | use_case: Random input for model regression.
29 | input_datatype: fp32
30 | name: input
31 | shape:
32 | - 1
33 | - 490
34 | output_nodes:
35 | - description: The probability on 12 keywords.
36 | example_output:
37 | path: models/keyword_spotting/cnn_large/model_package_tf/model_archive/TFLite/tflite_fp32/testing_output/Identity
38 | shape:
39 | - 1
40 | - 12
41 | type: fp32
42 | use_case: output for model regression.
43 | name: Identity
44 | output_datatype: fp32
45 | shape:
46 | - 1
47 | - 12
48 | network_quality:
49 | clustered: false
50 | is_vanilla: true
51 | pruned: false
52 | quality_level: Deployable
53 | quality_level_hero_hw: cortex_m
54 | quantization_default: false
55 | quantization_full: false
56 | recreate: true
57 | operators:
58 | TensorFlow Lite:
59 | - CONV_2D
60 | - FULLY_CONNECTED
61 | - RELU
62 | - RESHAPE
63 | - SOFTMAX
64 | paper: https://arxiv.org/abs/1711.07128
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1 | benchmark:
2 | benchmark_metrics:
3 | accuracy: 92.27%
4 | benchmark_name: Google Speech Commands test set
5 | description: This is a fully quantized int8 version of the CNN Large model developed
6 | by Arm, from the Hello Edge paper.
7 | license:
8 | - Apache-2.0
9 | network:
10 | datatype: int8
11 | file_size_bytes: 484600
12 | filename: cnn_l_quantized.tflite
13 | framework: TensorFlow Lite
14 | hash:
15 | algorithm: sha1
16 | value: a61ab748ae8f52f78ab568342db67a792c6ecf34
17 | provenance: https://arxiv.org/abs/1711.07128
18 | training: Trained by Arm
19 | network_parameters:
20 | input_nodes:
21 | - description: The input is a processed MFCCs of shape (1, 490)
22 | example_input:
23 | path: models/keyword_spotting/cnn_large/model_package_tf/model_archive/TFLite/tflite_int8/testing_input/input
24 | shape:
25 | - 1
26 | - 490
27 | type: int8
28 | use_case: Random input for model regression.
29 | input_datatype: int8
30 | name: input
31 | shape:
32 | - 1
33 | - 490
34 | output_nodes:
35 | - description: The probability on 12 keywords.
36 | example_output:
37 | path: models/keyword_spotting/cnn_large/model_package_tf/model_archive/TFLite/tflite_int8/testing_output/Identity
38 | shape:
39 | - 1
40 | - 12
41 | type: int8
42 | use_case: output for model regression.
43 | name: Identity
44 | output_datatype: int8
45 | shape:
46 | - 1
47 | - 12
48 | network_quality:
49 | clustered: false
50 | is_vanilla: true
51 | pruned: false
52 | quality_level: Deployable
53 | quality_level_hero_hw: cortex_m
54 | quantization_default: false
55 | quantization_full: true
56 | recreate: true
57 | operators:
58 | TensorFlow Lite:
59 | - CONV_2D
60 | - FULLY_CONNECTED
61 | - RELU
62 | - RESHAPE
63 | - SOFTMAX
64 | paper: https://arxiv.org/abs/1711.07128
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2 | tensorflow == 2.5.0
3 | tensorflow-model-optimization == 0.6.0
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/models/keyword_spotting/cnn_large/model_package_tf/validation_utils/labels.txt:
--------------------------------------------------------------------------------
1 | _silence_
2 | _unknown_
3 | yes
4 | no
5 | up
6 | down
7 | left
8 | right
9 | on
10 | off
11 | stop
12 | go
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1 | benchmark:
2 | benchmark_metrics:
3 | accuracy: 91.84%
4 | benchmark_name: Google Speech Commands test set
5 | description: This is a floating point fp32 version of the CNN Medium model developed
6 | by Arm, from the Hello Edge paper.
7 | license:
8 | - Apache-2.0
9 | network:
10 | datatype: fp32
11 | file_size_bytes: 717268
12 | filename: cnn_m.tflite
13 | framework: TensorFlow Lite
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16 | value: 0057378e784ccb8fa28abaa972a86988fbecea19
17 | provenance: https://arxiv.org/abs/1711.07128
18 | training: Trained by Arm
19 | network_parameters:
20 | input_nodes:
21 | - description: The input is a processed MFCCs of shape (1, 490)
22 | example_input:
23 | path: models/keyword_spotting/cnn_medium/model_package_tf/model_archive/TFLite/tflite_fp32/testing_input/input
24 | shape:
25 | - 1
26 | - 490
27 | type: fp32
28 | use_case: Random input for model regression.
29 | input_datatype: fp32
30 | name: input
31 | shape:
32 | - 1
33 | - 490
34 | output_nodes:
35 | - description: The probability on 12 keywords.
36 | example_output:
37 | path: models/keyword_spotting/cnn_medium/model_package_tf/model_archive/TFLite/tflite_fp32/testing_output/Identity
38 | shape:
39 | - 1
40 | - 12
41 | type: fp32
42 | use_case: output for model regression.
43 | name: Identity
44 | output_datatype: fp32
45 | shape:
46 | - 1
47 | - 12
48 | network_quality:
49 | clustered: false
50 | is_vanilla: true
51 | pruned: false
52 | quality_level: Deployable
53 | quality_level_hero_hw: cortex_m
54 | quantization_default: false
55 | quantization_full: false
56 | recreate: true
57 | operators:
58 | TensorFlow Lite:
59 | - CONV_2D
60 | - FULLY_CONNECTED
61 | - RELU
62 | - RESHAPE
63 | - SOFTMAX
64 | paper: https://arxiv.org/abs/1711.07128
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3 | Accuracy: 90.47%
4 | benchmark_name: Google Speech Commands test set
5 | description: This is a fully quantized int8 version of the CNN Medium model developed
6 | by Arm, from the Hello Edge paper.
7 | license:
8 | - Apache-2.0
9 | network:
10 | datatype: int8
11 | file_size_bytes: 186064
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17 | provenance: https://arxiv.org/abs/1711.07128
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20 | input_nodes:
21 | - description: The input is a processed MFCCs of shape (1, 490)
22 | example_input:
23 | path: models/keyword_spotting/cnn_medium/model_package_tf/model_archive/TFLite/tflite_int8/testing_input/input
24 | shape:
25 | - 1
26 | - 490
27 | type: int8
28 | use_case: Random input for model regression.
29 | input_datatype: int8
30 | name: input
31 | shape:
32 | - 1
33 | - 490
34 | output_nodes:
35 | - description: The probability on 12 keywords.
36 | example_output:
37 | path: models/keyword_spotting/cnn_medium/model_package_tf/model_archive/TFLite/tflite_int8/testing_output/Identity
38 | shape:
39 | - 1
40 | - 12
41 | type: int8
42 | use_case: output for model regression.
43 | name: Identity
44 | output_datatype: int8
45 | shape:
46 | - 1
47 | - 12
48 | network_quality:
49 | clustered: false
50 | is_vanilla: true
51 | pruned: false
52 | quality_level: Deployable
53 | quality_level_hero_hw: cortex_m
54 | quantization_default: false
55 | quantization_full: true
56 | recreate: true
57 | operators:
58 | TensorFlow Lite:
59 | - CONV_2D
60 | - FULLY_CONNECTED
61 | - RELU
62 | - RESHAPE
63 | - SOFTMAX
64 | paper: https://arxiv.org/abs/1711.07128
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4 | benchmark_name: Google Speech Commands test set
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59 | - CONV_2D
60 | - FULLY_CONNECTED
61 | - RELU
62 | - RESHAPE
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64 | paper: https://arxiv.org/abs/1711.07128
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59 | - CONV_2D
60 | - FULLY_CONNECTED
61 | - RELU
62 | - RESHAPE
63 | - SOFTMAX
64 | paper: https://arxiv.org/abs/1711.07128
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62 | paper: https://arxiv.org/abs/1711.07128
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62 | paper: https://arxiv.org/abs/1711.07128
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3 | Accuracy: 83.93%
4 | benchmark_name: Google Speech Commands test set
5 | description: This is a fully quantized int8 version of the DNN Medium model developed
6 | by Arm, from the Hello Edge paper.
7 | license:
8 | - Apache-2.0
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62 | paper: https://arxiv.org/abs/1711.07128
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8 | - Apache-2.0
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32 | - 1
33 | - 250
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35 | - description: The probability on 12 keywords.
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38 | shape:
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59 | - FULLY_CONNECTED
60 | - RELU
61 | - SOFTMAX
62 | paper: https://arxiv.org/abs/1711.07128
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8 | - Apache-2.0
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62 | paper: https://arxiv.org/abs/1711.07128
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60 | - CONV_2D
61 | - DEPTHWISE_CONV_2D
62 | - FULLY_CONNECTED
63 | - RELU
64 | - RESHAPE
65 | - SOFTMAX
66 | paper: https://arxiv.org/abs/1711.07128
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60 | - CONV_2D
61 | - DEPTHWISE_CONV_2D
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63 | - RELU
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7 | license:
8 | - Apache-2.0
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60 | - CONV_2D
61 | - DEPTHWISE_CONV_2D
62 | - FULLY_CONNECTED
63 | - RELU
64 | - RESHAPE
65 | - SOFTMAX
66 | paper: https://arxiv.org/abs/1711.07128
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60 | - CONV_2D
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66 | paper: https://arxiv.org/abs/1711.07128
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60 | - CONV_2D
61 | - DEPTHWISE_CONV_2D
62 | - FULLY_CONNECTED
63 | - RELU
64 | - RESHAPE
65 | - SOFTMAX
66 | paper: https://arxiv.org/abs/1711.07128
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62 | - FULLY_CONNECTED
63 | - RELU
64 | - RESHAPE
65 | - SOFTMAX
66 | paper: https://arxiv.org/abs/1711.07128
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
1 | benchmark:
2 | benchmark_metrics:
3 | Accuracy: 93.11%
4 | benchmark_name: Google Speech Commands test set
5 | description: This is a fully quantized int8 version of the DS-CNN Small model developed
6 | by Arm, from the Hello Edge paper.
7 | license:
8 | - Apache-2.0
9 | network:
10 | datatype: int8
11 | file_size_bytes: 47616
12 | filename: ds_cnn_s_quantized.tflite
13 | framework: TensorFlow Lite
14 | hash:
15 | algorithm: sha1
16 | value: cf24429e86a9647b1632c382894bc68d26d34039
17 | provenance: https://arxiv.org/abs/1711.07128
18 | training: Trained by Arm
19 | network_parameters:
20 | input_nodes:
21 | - description: The input is a processed MFCCs of shape (1, 490)
22 | example_input:
23 | path: models/keyword_spotting/ds_cnn_small/model_package_tf/model_archive/TFLite/tflite_int8/testing_input/input
24 | shape:
25 | - 1
26 | - 490
27 | type: int8
28 | use_case: Random input for model regression.
29 | input_datatype: int8
30 | name: input
31 | shape:
32 | - 1
33 | - 490
34 | output_nodes:
35 | - description: The probability on 12 keywords.
36 | example_output:
37 | path: models/keyword_spotting/ds_cnn_small/model_package_tf/model_archive/TFLite/tflite_int8/testing_output/Identity
38 | shape:
39 | - 1
40 | - 12
41 | type: int8
42 | use_case: output for model regression.
43 | name: Identity
44 | output_datatype: int8
45 | shape:
46 | - 1
47 | - 12
48 | network_quality:
49 | clustered: false
50 | is_vanilla: true
51 | pruned: false
52 | quality_level: Hero
53 | quality_level_hero_hw: cortex_m
54 | quantization_default: false
55 | quantization_full: true
56 | recreate: true
57 | operators:
58 | TensorFlow Lite:
59 | - AVERAGE_POOL_2D
60 | - CONV_2D
61 | - DEPTHWISE_CONV_2D
62 | - FULLY_CONNECTED
63 | - RELU
64 | - RESHAPE
65 | - SOFTMAX
66 | paper: https://arxiv.org/abs/1711.07128
--------------------------------------------------------------------------------
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2 | all_model_checkpoint_paths: "ds_cnn_0.94_ckpt"
3 |
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https://raw.githubusercontent.com/ARM-software/ML-zoo/fec0bb5bcd486eb2839cdb7d437d466067402a6a/models/keyword_spotting/ds_cnn_small/model_package_tf/model_core_utils/__init__.py
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/models/keyword_spotting/ds_cnn_small/model_package_tf/requirements.txt:
--------------------------------------------------------------------------------
1 | numpy == 1.19.5
2 | tensorflow == 2.5.0
3 | tensorflow-model-optimization == 0.6.0
--------------------------------------------------------------------------------
/models/keyword_spotting/ds_cnn_small/model_package_tf/validation_utils/labels.txt:
--------------------------------------------------------------------------------
1 | _silence_
2 | _unknown_
3 | yes
4 | no
5 | up
6 | down
7 | left
8 | right
9 | on
10 | off
11 | stop
12 | go
--------------------------------------------------------------------------------
/models/keyword_spotting/micronet_large/tflite_int8/definition.yaml:
--------------------------------------------------------------------------------
1 | benchmark:
2 | Google Speech Commands test set:
3 | Accuracy: 0.965
4 | description: This is a fully quantized version (asymmetrical int8) of the MicroNet
5 | Large model developed by Arm, from the MicroNets paper. This model is trained on
6 | the 'Google Speech Commands' dataset.
7 | license:
8 | - Apache-2.0
9 | network:
10 | file_size_bytes: 658832
11 | filename: kws_micronet_l.tflite
12 | framework: TensorFlow Lite
13 | hash:
14 | algorithm: sha1
15 | value: 5ac522fadfc7d07e96e72e38c55650514ecef750
16 | provenance: https://arxiv.org/pdf/2010.11267.pdf
17 | network_parameters:
18 | input_nodes:
19 | - description: A one second audio clip, converted to a 2D MFCC computed from a speech
20 | frame of length 40ms and stride 20ms.
21 | example_input:
22 | path: models/keyword_spotting/micronet_large/tflite_int8/testing_input/input
23 | name: input
24 | shape:
25 | - 1
26 | - 49
27 | - 10
28 | - 1
29 | output_nodes:
30 | - description: The probability on 12 keywords.
31 | name: Identity
32 | shape:
33 | - 1
34 | - 12
35 | test_output_path: models/keyword_spotting/micronet_large/tflite_int8/testing_output/Identity
36 | operators:
37 | TensorFlow Lite:
38 | - AVERAGE_POOL_2D
39 | - CONV_2D
40 | - DEPTHWISE_CONV_2D
41 | - RELU6
42 | - RESHAPE
43 | paper: https://arxiv.org/pdf/2010.11267.pdf
44 |
--------------------------------------------------------------------------------
/models/keyword_spotting/micronet_large/tflite_int8/get_class_labels.sh:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | wget https://raw.githubusercontent.com/ARM-software/ML-KWS-for-MCU/e9cf319e9aa2ff71d433e111477dd95329fb94cb/Pretrained_models/labels.txt
20 | mv labels.txt labelmappings.txt
--------------------------------------------------------------------------------
/models/keyword_spotting/micronet_large/tflite_int8/kws_micronet_l.tflite:
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4 |
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/models/keyword_spotting/micronet_medium/tflite_int8/definition.yaml:
--------------------------------------------------------------------------------
1 | benchmark:
2 | Google Speech Commands test set:
3 | Accuracy: 0.958
4 | description: This is a fully quantized version (asymmetrical int8) of the MicroNet
5 | Medium model developed by Arm, from the MicroNets paper. This model is trained on
6 | the 'Google Speech Commands' dataset.
7 | license:
8 | - Apache-2.0
9 | network:
10 | file_size_bytes: 181968
11 | filename: kws_micronet_m.tflite
12 | framework: TensorFlow Lite
13 | hash:
14 | algorithm: sha1
15 | value: fd03a6b24548ea99cf487dbd682937df5718cef1
16 | provenance: https://arxiv.org/pdf/2010.11267.pdf
17 | network_parameters:
18 | input_nodes:
19 | - description: A one second audio clip, converted to a 2D MFCC computed from a speech
20 | frame of length 40ms and stride 20ms.
21 | example_input:
22 | path: models/keyword_spotting/micronet_medium/tflite_int8/testing_input/input
23 | name: input
24 | shape:
25 | - 1
26 | - 49
27 | - 10
28 | - 1
29 | output_nodes:
30 | - description: The probability on 12 keywords.
31 | name: Identity
32 | shape:
33 | - 1
34 | - 12
35 | test_output_path: models/keyword_spotting/micronet_medium/tflite_int8/testing_output/Identity
36 | operators:
37 | TensorFlow Lite:
38 | - AVERAGE_POOL_2D
39 | - CONV_2D
40 | - DEPTHWISE_CONV_2D
41 | - RELU6
42 | - RESHAPE
43 | paper: https://arxiv.org/pdf/2010.11267.pdf
44 |
--------------------------------------------------------------------------------
/models/keyword_spotting/micronet_medium/tflite_int8/get_class_labels.sh:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | wget https://raw.githubusercontent.com/ARM-software/ML-KWS-for-MCU/e9cf319e9aa2ff71d433e111477dd95329fb94cb/Pretrained_models/labels.txt
20 | mv labels.txt labelmappings.txt
--------------------------------------------------------------------------------
/models/keyword_spotting/micronet_medium/tflite_int8/kws_micronet_m.tflite:
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/models/keyword_spotting/micronet_small/tflite_int8/definition.yaml:
--------------------------------------------------------------------------------
1 | benchmark:
2 | Google Speech Commands test set:
3 | Accuracy: 0.953
4 | description: This is a fully quantized version (asymmetrical int8) of the MicroNet
5 | Small model developed by Arm, from the MicroNets paper. This model is trained on
6 | the 'Google Speech Commands' dataset.
7 | license:
8 | - Apache-2.0
9 | network:
10 | file_size_bytes: 114512
11 | filename: kws_micronet_s.tflite
12 | framework: TensorFlow Lite
13 | hash:
14 | algorithm: sha1
15 | value: d13a25dbe34a0f2758879b9d3e7c7bad94c68ec7
16 | provenance: https://arxiv.org/pdf/2010.11267.pdf
17 | network_parameters:
18 | input_nodes:
19 | - description: A one second audio clip, converted to a 2D MFCC computed from a speech
20 | frame of length 40ms and stride 20ms.
21 | example_input:
22 | path: models/keyword_spotting/micronet_small/tflite_int8/testing_input/input
23 | name: input
24 | shape:
25 | - 1
26 | - 49
27 | - 10
28 | - 1
29 | output_nodes:
30 | - description: The probability on 12 keywords.
31 | name: Identity
32 | shape:
33 | - 1
34 | - 12
35 | test_output_path: models/keyword_spotting/micronet_small/tflite_int8/testing_output/Identity
36 | operators:
37 | TensorFlow Lite:
38 | - AVERAGE_POOL_2D
39 | - CONV_2D
40 | - DEPTHWISE_CONV_2D
41 | - RELU6
42 | - RESHAPE
43 | paper: https://arxiv.org/pdf/2010.11267.pdf
44 |
--------------------------------------------------------------------------------
/models/keyword_spotting/micronet_small/tflite_int8/get_class_labels.sh:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | wget https://raw.githubusercontent.com/ARM-software/ML-KWS-for-MCU/e9cf319e9aa2ff71d433e111477dd95329fb94cb/Pretrained_models/labels.txt
20 | mv labels.txt labelmappings.txt
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/models/noise_suppression/RNNoise/tflite_int8/recreate_model/get_data.sh:
--------------------------------------------------------------------------------
1 | #!/usr/bin/env bash
2 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
3 | #
4 | # SPDX-License-Identifier: Apache-2.0
5 | #
6 | # Licensed under the Apache License, Version 2.0 (the License); you may
7 | # not use this file except in compliance with the License.
8 | # You may obtain a copy of the License at
9 | #
10 | # www.apache.org/licenses/LICENSE-2.0
11 | #
12 | # Unless required by applicable law or agreed to in writing, software
13 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
14 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
15 | # See the License for the specific language governing permissions and
16 | # limitations under the License.
17 |
18 | wget https://datashare.ed.ac.uk/bitstream/handle/10283/2791/clean_testset_wav.zip
19 | wget https://datashare.ed.ac.uk/bitstream/handle/10283/2791/clean_trainset_56spk_wav.zip
20 | wget https://datashare.ed.ac.uk/bitstream/handle/10283/2791/noisy_testset_wav.zip
21 | wget https://datashare.ed.ac.uk/bitstream/handle/10283/2791/noisy_trainset_56spk_wav.zip
22 |
23 | unzip clean_testset_wav.zip
24 | unzip noisy_testset_wav.zip
25 | unzip clean_trainset_56spk_wav.zip
26 | unzip noisy_trainset_56spk_wav.zip
--------------------------------------------------------------------------------
/models/noise_suppression/RNNoise/tflite_int8/recreate_model/requirements.txt:
--------------------------------------------------------------------------------
1 | tensorflow==2.4.3
2 | h5py==3.1.0
3 | numpy==1.19.5
4 | librosa==0.8.1
5 | SoundFile==0.10.3.post1
6 | pesq==0.0.3
--------------------------------------------------------------------------------
/models/noise_suppression/RNNoise/tflite_int8/recreate_model/train_and_quantise_model.sh:
--------------------------------------------------------------------------------
1 | #!/usr/bin/env bash
2 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
3 | #
4 | # SPDX-License-Identifier: Apache-2.0
5 | #
6 | # Licensed under the Apache License, Version 2.0 (the License); you may
7 | # not use this file except in compliance with the License.
8 | # You may obtain a copy of the License at
9 | #
10 | # www.apache.org/licenses/LICENSE-2.0
11 | #
12 | # Unless required by applicable law or agreed to in writing, software
13 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
14 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
15 | # See the License for the specific language governing permissions and
16 | # limitations under the License.
17 |
18 | python3 -m venv venv
19 |
20 | source venv/bin/activate
21 | pip install --upgrade pip
22 | pip install -r requirements.txt
23 |
24 | python train.py --train_data_h5=./train.h5 --test_data_h5=./test.h5
25 | python convert.py --ckpt_path=./ckpts/120 --h5_path=./test.h5
26 |
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/models/noise_suppression/RNNoise/tflite_int8/rnnoise_INT8.tflite:
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4 |
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/models/object_detection/ssd_mobilenet_v1/tflite_fp32/get_class_labels.sh:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | git clone --depth 1 https://github.com/tensorflow/models.git ./tf_models
20 | cp tf_models/research/object_detection/data/mscoco_label_map.pbtxt .
21 |
22 | python scripts/export_labels.py --path mscoco_label_map.pbtxt --num_classes 90
23 | tr -d \" < temp.txt > labelmapping.txt
24 | rm -rf temp.txt mscoco_label_map.pbtxt
25 | rm -rf ./tf_models
26 |
--------------------------------------------------------------------------------
/models/object_detection/ssd_mobilenet_v1/tflite_fp32/recreate_model/README.md:
--------------------------------------------------------------------------------
1 | # SSD MobileNet v1 FP32 Model Re-Creation
2 | This folder contains a script that allows for the model to be re-created from scratch.
3 |
4 | ## Requirements
5 | The script in this folder requires that the following must be installed:
6 | - Python 3.7
7 | - protoc
8 |
9 | ## Running The Script
10 | To run the script, run the following in a terminal: `./recreate_model.sh`
--------------------------------------------------------------------------------
/models/object_detection/ssd_mobilenet_v1/tflite_fp32/recreate_model/recreate_model.sh:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | python3.7 -m venv venv
20 | source venv/bin/activate
21 |
22 | pip install --upgrade pip
23 | pip install -r requirements.txt
24 |
25 | git clone https://github.com/tensorflow/models.git
26 | pushd models/research
27 |
28 | export PYTHONPATH=`pwd`:`pwd`/slim:$PYTHONPATH
29 | protoc object_detection/protos/*.proto --python_out=.
30 |
31 | wget http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz
32 | tar -xvf ssd_mobilenet_v1_coco_2018_01_28.tar.gz
33 |
34 | python object_detection/export_tflite_ssd_graph.py --pipeline_config_path=object_detection/samples/configs/ssd_mobilenet_v1_coco.config --trained_checkpoint_prefix=ssd_mobilenet_v1_coco_2018_01_28/model.ckpt --output_directory=. --add_postprocessing_op=true
35 | tflite_convert --graph_def_file=tflite_graph.pb --output_file=ssd_mobilenet_v1.tflite --input_shapes=1,300,300,3 --input_arrays=normalized_input_image_tensor --output_arrays=TFLite_Detection_PostProcess,TFLite_Detection_PostProcess:1,TFLite_Detection_PostProcess:2,TFLite_Detection_PostProcess:3 --change_concat_input_ranges=false --allow_custom_ops
36 |
37 | mv ssd_mobilenet_v1.tflite ../..
38 |
39 | popd
40 |
--------------------------------------------------------------------------------
/models/object_detection/ssd_mobilenet_v1/tflite_fp32/recreate_model/requirements.txt:
--------------------------------------------------------------------------------
1 | absl-py==0.11.0
2 | astor==0.8.1
3 | cached-property==1.5.2
4 | certifi==2020.6.20
5 | cycler==0.10.0
6 | gast==0.2.2
7 | google-pasta==0.2.0
8 | grpcio==1.33.2
9 | h5py==3.0.0
10 | importlib-metadata==2.0.0
11 | Keras-Applications==1.0.8
12 | Keras-Preprocessing==1.1.2
13 | kiwisolver==1.3.1
14 | Markdown==3.3.3
15 | matplotlib==3.3.2
16 | mock==4.0.2
17 | numpy==1.18.5
18 | opt-einsum==3.3.0
19 | Pillow==8.1.2
20 | protobuf==3.13.0
21 | pyparsing==2.4.7
22 | python-dateutil==2.8.1
23 | scipy==1.5.4
24 | six==1.15.0
25 | tensorboard==1.15.0
26 | tensorflow==1.15.4
27 | tensorflow-estimator==1.15.1
28 | termcolor==1.1.0
29 | tf-slim==1.1.0
30 | Werkzeug==1.0.1
31 | wrapt==1.12.1
32 | zipp==3.4.0
33 |
--------------------------------------------------------------------------------
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/models/object_detection/ssd_mobilenet_v1/tflite_int8/get_class_labels.sh:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | git clone --depth 1 https://github.com/tensorflow/models.git ./tf_models
20 | cp tf_models/research/object_detection/data/mscoco_label_map.pbtxt .
21 |
22 | python scripts/export_labels.py --path mscoco_label_map.pbtxt --num_classes 90
23 | tr -d \" < temp.txt > labelmapping.txt
24 | rm -rf temp.txt mscoco_label_map.pbtxt
25 | rm -rf ./tf_models
26 |
--------------------------------------------------------------------------------
/models/object_detection/ssd_mobilenet_v1/tflite_int8/recreate_model/README.md:
--------------------------------------------------------------------------------
1 | # SSD MobileNet v1 INT8 Re-Creation
2 | This folder contains scripts that allow you to re-create the model and benchmark it's performance.
3 |
4 | ## Requirements
5 | The scripts in this folder requires that the following must be installed:
6 | - Python 3.7
7 | - protoc
8 |
9 | ## Running The Script
10 | ### Recreate The Model
11 | Run the following command in a terminal: `./quantize_ssd_mobilenet_v1.sh`
12 |
13 | ### Benchmarking The Model
14 | Run the following command in a terminal: `./benchmark_ssd_mobilenet_v1.sh`
15 |
--------------------------------------------------------------------------------
/models/object_detection/ssd_mobilenet_v1/tflite_int8/recreate_model/benchmark_ssd_mobilenet_v1.sh:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 | wget -nc http://images.cocodataset.org/annotations/annotations_trainval2017.zip
19 | unzip -n annotations_trainval2017.zip
20 |
21 | python3.7 -m venv venv
22 |
23 | source venv/bin/activate
24 | pip install --upgrade pip
25 | pip install -r requirements.txt
26 |
27 | pip install tensorflow==2.5.0
28 |
29 | python benchmark_model.py --path ssd_mobilenet_v1.tflite
30 |
--------------------------------------------------------------------------------
/models/object_detection/ssd_mobilenet_v1/tflite_int8/recreate_model/quantize_ssd_mobilenet_v1.sh:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 | python3.7 -m venv venv
19 |
20 | source venv/bin/activate
21 | pip install --upgrade pip
22 | pip install -r requirements.txt
23 |
24 | git clone https://github.com/tensorflow/models.git
25 |
26 | pushd models/research
27 | export PYTHONPATH=`pwd`:`pwd`/slim:$PYTHONPATH
28 | protoc object_detection/protos/*.proto --python_out=.
29 | popd
30 |
31 | pushd models/
32 | wget http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz
33 | tar -xvf ssd_mobilenet_v1_coco_2018_01_28.tar.gz
34 |
35 | mkdir ssd_tflite
36 | python research/object_detection/export_tflite_ssd_graph.py \
37 | --pipeline_config_path ssd_mobilenet_v1_coco_2018_01_28/pipeline.config \
38 | --trained_checkpoint_prefix ssd_mobilenet_v1_coco_2018_01_28/model.ckpt \
39 | --output_directory ssd_tflite/ \
40 | --max_detections=100 \
41 | --add_postprocessing_op=true
42 |
43 | mv ssd_tflite/ ..
44 | popd
45 |
46 | pip install tensorflow==2.5.0
47 | python quantize_model.py
48 |
--------------------------------------------------------------------------------
/models/object_detection/ssd_mobilenet_v1/tflite_int8/recreate_model/requirements.txt:
--------------------------------------------------------------------------------
1 | absl-py==0.12.0
2 | astor==0.8.1
3 | astunparse==1.6.3
4 | attrs==21.2.0
5 | cached-property==1.5.2
6 | cachetools==4.2.2
7 | certifi==2021.5.30
8 | chardet==4.0.0
9 | cycler==0.10.0
10 | Cython==0.29.23
11 | dill==0.3.4
12 | flatbuffers==1.12
13 | future==0.18.2
14 | gast==0.2.2
15 | google-auth==1.32.0
16 | google-auth-oauthlib==0.4.4
17 | google-pasta==0.2.0
18 | googleapis-common-protos==1.53.0
19 | grpcio==1.34.1
20 | h5py==3.1.0
21 | idna==2.10
22 | importlib-metadata==4.5.0
23 | importlib-resources==5.1.4
24 | Keras-Applications==1.0.8
25 | keras-nightly==2.5.0.dev2021032900
26 | Keras-Preprocessing==1.1.2
27 | kiwisolver==1.3.1
28 | Markdown==3.3.4
29 | matplotlib==3.4.2
30 | numpy==1.19.5
31 | oauthlib==3.1.1
32 | opt-einsum==3.3.0
33 | Pillow==8.2.0
34 | promise==2.3
35 | protobuf==3.17.3
36 | pyasn1==0.4.8
37 | pyasn1-modules==0.2.8
38 | pycocotools==2.0.2
39 | pyparsing==2.4.7
40 | python-dateutil==2.8.1
41 | requests==2.25.1
42 | requests-oauthlib==1.3.0
43 | rsa==4.7.2
44 | scipy==1.7.0
45 | six==1.15.0
46 | tensorboard==1.15.0
47 | tensorboard-data-server==0.6.1
48 | tensorboard-plugin-wit==1.8.0
49 | tensorflow==1.15.0
50 | tensorflow-datasets==4.3.0
51 | tensorflow-estimator==1.15.1
52 | tensorflow-metadata==1.1.0
53 | termcolor==1.1.0
54 | tf-slim==1.1.0
55 | tqdm==4.61.1
56 | typing-extensions==3.7.4.3
57 | urllib3==1.26.5
58 | Werkzeug==2.0.1
59 | wrapt==1.12.1
60 | zipp==3.4.1
61 |
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1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | git clone --depth 1 https://github.com/tensorflow/models.git ./tf_models
20 | cp tf_models/research/object_detection/data/mscoco_label_map.pbtxt .
21 |
22 | python scripts/export_labels.py --path mscoco_label_map.pbtxt --num_classes 90
23 | tr -d \" < temp.txt > labelmapping.txt
24 | rm -rf temp.txt mscoco_label_map.pbtxt
25 | rm -rf ./tf_models
26 |
--------------------------------------------------------------------------------
/models/object_detection/ssd_mobilenet_v1/tflite_uint8/recreate_model/README.md:
--------------------------------------------------------------------------------
1 | # SSD MobileNet v1 UINT8 Model Re-Creation
2 | This folder contains a script that allows for the model to be re-created from scratch.
3 |
4 | ## Requirements
5 | The script in this folder requires that the following must be installed:
6 | - Python 3.7
7 |
8 | ## Running The Script
9 | To run the script, run the following in a terminal: `./recreate_model.sh`
--------------------------------------------------------------------------------
/models/object_detection/ssd_mobilenet_v1/tflite_uint8/recreate_model/recreate_model.sh:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | python3.7 -m venv venv
20 | source venv/bin/activate
21 |
22 | pip install -r requirements.txt
23 |
24 | wget http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18.tar.gz
25 | tar -xvf ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18.tar.gz
26 |
27 | pushd ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18
28 |
29 | tflite_convert --graph_def_file=tflite_graph.pb --output_file=ssd_mobilenet_v1.tflite --input_shapes=1,300,300,3 --input_arrays=normalized_input_image_tensor --output_arrays=TFLite_Detection_PostProcess,TFLite_Detection_PostProcess:1,TFLite_Detection_PostProcess:2,TFLite_Detection_PostProcess:3 --change_concat_input_ranges=false --allow_custom_ops --inference_type=QUANTIZED_UINT8 --mean_values=128 --std_dev_values=128
30 | mv ssd_mobilenet_v1.tflite ..
31 |
32 | popd
33 |
--------------------------------------------------------------------------------
/models/object_detection/ssd_mobilenet_v1/tflite_uint8/recreate_model/requirements.txt:
--------------------------------------------------------------------------------
1 | absl-py==0.11.0
2 | astor==0.8.1
3 | cached-property==1.5.2
4 | gast==0.2.2
5 | google-pasta==0.2.0
6 | grpcio==1.33.2
7 | h5py==3.0.0
8 | importlib-metadata==2.0.0
9 | Keras-Applications==1.0.8
10 | Keras-Preprocessing==1.1.2
11 | Markdown==3.3.3
12 | numpy==1.18.5
13 | opt-einsum==3.3.0
14 | protobuf==3.13.0
15 | six==1.15.0
16 | tensorboard==1.15.0
17 | tensorflow==1.15.4
18 | tensorflow-estimator==1.15.1
19 | termcolor==1.1.0
20 | Werkzeug==1.0.1
21 | wrapt==1.12.1
22 | zipp==3.4.0
23 |
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1 | benchmark:
2 | MS COCO Validation:
3 | mAP: 0.331
4 | description: Yolo v3 Tiny is a object detection network, that localizes and identifies
5 | objects in an input image. This is a floating point version that takes a 416x416
6 | input image and outputs detections for this image. This model is generated using
7 | the weights from the [https://pjreddie.com/darknet/yolo/](YOLO website).
8 | license:
9 | - Apache-2.0
10 | network:
11 | file_size_bytes: 35455980
12 | filename: yolo_v3_tiny_darknet_fp32.tflite
13 | framework: TensorFlow Lite
14 | hash:
15 | algorithm: sha1
16 | value: b38f7be6856eed4466493bdc86be1879f4b743fb
17 | provenance: https://pjreddie.com/media/files/yolov3-tiny.weights & https://github.com/mystic123/tensorflow-yolo-v3
18 | network_parameters:
19 | input_nodes:
20 | - description: A 416x416 floating point input image.
21 | example_input:
22 | path: models/object_detection/yolo_v3_tiny/tflite_fp32/testing_input/inputs
23 | name: inputs
24 | shape:
25 | - 1
26 | - 416
27 | - 416
28 | - 3
29 | output_nodes:
30 | - description: A 1xNx85 map of predictions, where the first 4 entries of the 3rd
31 | dimension are the bounding box coordinates and the 5th is the confidence. The
32 | remaining entries are softmax scores for each class.
33 | name: output_boxes
34 | shape:
35 | - 1
36 | - 2535
37 | - 85
38 | test_output_path: models/object_detection/yolo_v3_tiny/tflite_fp32/testing_output/output_boxes
39 | operators:
40 | TensorFlow Lite:
41 | - ADD
42 | - CONCATENATION
43 | - CONV_2D
44 | - EXP
45 | - LOGISTIC
46 | - MAXIMUM
47 | - MAX_POOL_2D
48 | - MUL
49 | - RESHAPE
50 | - RESIZE_NEAREST_NEIGHBOR
51 | - SPLIT_V
52 | - SUB
53 | paper: https://arxiv.org/abs/1804.02767
54 |
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/models/object_detection/yolo_v3_tiny/tflite_fp32/get_class_labels.sh:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | wget https://raw.githubusercontent.com/pjreddie/darknet/master/data/coco.names
20 | mv coco.names labelmappings.txt
--------------------------------------------------------------------------------
/models/object_detection/yolo_v3_tiny/tflite_fp32/recreate_model/README.md:
--------------------------------------------------------------------------------
1 | # YOLO v3 Tiny FP32 Model Re-Creation
2 | This folder contains a script that allows for the model to be re-created from scratch.
3 |
4 | ## Requirements
5 | The script in this folder requires that the following must be installed:
6 | - Python 3.6
7 |
8 | ## Running The Script
9 | To run the script, run the following in a terminal: `./recreate_model.sh`
--------------------------------------------------------------------------------
/models/object_detection/yolo_v3_tiny/tflite_fp32/recreate_model/recreate_model.sh:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | python3.6 -m venv venv
20 | source venv/bin/activate
21 |
22 | pip install --upgrade pip
23 | pip install -r requirements.txt
24 |
25 | git clone https://github.com/mystic123/tensorflow-yolo-v3
26 | pushd tensorflow-yolo-v3
27 |
28 | wget https://raw.githubusercontent.com/pjreddie/darknet/master/data/coco.names
29 | wget https://pjreddie.com/media/files/yolov3-tiny.weights
30 |
31 | python convert_weights_pb.py --class_names coco.names --weights_file yolov3-tiny.weights --data_format NHWC --tiny
32 |
33 | pip install tensorflow==1.15.0
34 |
35 | tflite_convert --graph_def_file=frozen_darknet_yolov3_model.pb --output_file=yolo_v3_tiny_darknet_fp32.tflite --input_shapes=1,416,416,3 --input_arrays=inputs --output_arrays=output_boxes
36 | mv yolo_v3_tiny_darknet_fp32.tflite ..
37 |
38 | popd
39 |
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/models/object_detection/yolo_v3_tiny/tflite_fp32/recreate_model/requirements.txt:
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1 | absl-py==0.11.0
2 | astor==0.8.1
3 | cached-property==1.5.2
4 | gast==0.4.0
5 | grpcio==1.33.2
6 | h5py==3.1.0
7 | importlib-metadata==2.0.0
8 | Keras-Applications==1.0.8
9 | Keras-Preprocessing==1.1.2
10 | Markdown==3.3.3
11 | numpy==1.19.4
12 | Pillow==8.1.2
13 | protobuf==3.14.0
14 | six==1.15.0
15 | tensorboard==1.11.0
16 | tensorflow==1.11.0
17 | termcolor==1.1.0
18 | Werkzeug==1.0.1
19 | zipp==3.4.0
20 |
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2 |
3 |
4 |
5 |
6 |
7 |
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/models/speech_recognition/tiny_wav2letter/tflite_int8/recreate_code/README.md:
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1 | # Tiny Wav2letter FP32/INT8/INT8_Pruned Model Re-Creation
2 | This folder contains a script that allows for the model to be re-created from scratch.
3 | ## Datasets
4 | Tiny Wav2Letter was trianed on both LibriSpeech dataset hosted on OpenSLR and fluent-speech-corpus dataset hosted on Kaggle.
5 | Please note that fluent-speech-corpus dataset hosted on [Kaggle](https://www.kaggle.com/tommyngx/fluent-speech-corpus) is a licensed dataset.
6 | ## Requirements
7 | The script in this folder requires that the following must be installed:
8 | - Python 3.6
9 | - Create new dir: fluent_speech_commands_dataset
10 | - (LICENSED DATASET!!) Download and extract fluent-speech-corpus from: https://www.kaggle.com/tommyngx/fluent-speech-corpus to fluent_speech_commands_dataset dir
11 |
12 | ## Running The Script
13 | To run the script, run the following in a terminal: `./recreate_model.sh`
14 |
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/models/speech_recognition/tiny_wav2letter/tflite_int8/recreate_code/evaluate_saved_weights.py:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | import argparse
18 |
19 | import tensorflow as tf
20 |
21 | from tinywav2letter import get_metrics, create_tinywav2letter
22 | from train_model import get_data
23 |
24 | def evaluate_saved_weights(args, pruned = False):
25 |
26 | model = create_tinywav2letter(batch_size = args.batch_size)
27 |
28 | model.load_weights('weights/tiny_wav2letter' + pruned * "_pruned" + '_weights.h5')
29 |
30 | opt = tf.keras.optimizers.Adam()
31 | model.compile(loss=get_metrics("loss"), metrics=[get_metrics("ler")], optimizer=opt)
32 |
33 | (reduced_validation_data, reduced_validation_num_steps) = get_data(args, "val_reduced_size", args.batch_size)
34 |
35 | model.evaluate(reduced_validation_data)
36 |
37 |
38 | if __name__ == "__main__":
39 | parser = argparse.ArgumentParser(allow_abbrev=False)
40 |
41 | parser.add_argument(
42 | "--batch_size",
43 | dest="batch_size",
44 | type=int,
45 | required=False,
46 | default=32,
47 | help="batch size wanted when creating model",
48 | )
49 |
50 | args = parser.parse_args()
51 | evaluate_saved_weights(args)
52 |
53 |
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/models/speech_recognition/tiny_wav2letter/tflite_int8/recreate_code/recreate_model.sh:
--------------------------------------------------------------------------------
1 | python3 -m venv env
2 | source env/bin/activate
3 |
4 | pip install -r requirements.txt
5 | python preprocessing.py
6 | python train_model.py --with_baseline --baseline_epochs 30 --with_finetuning --finetuning_epochs 10 --with_fluent_speech --fluent_speech_epochs 30
7 | python prune_and_quantise_model.py --prune --sparsity 0.5 --finetuning_epochs 10
8 | python prune_and_quantise_model.py --sparsity 0.5 --finetuning_epochs 10
9 |
10 |
11 |
12 |
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/models/speech_recognition/tiny_wav2letter/tflite_int8/recreate_code/requirements.txt:
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1 | librosa==0.8.1
2 | numpy==1.19.5
3 | tensorboard==2.6.0
4 | tensorboard-data-server==0.6.1
5 | tensorboard-plugin-profile==2.5.0
6 | tensorboard-plugin-wit==1.8.0
7 | tensorflow==2.4.1
8 | tensorflow-model-optimization==0.6.0
9 | tqdm
10 | jiwer==2.3.0
11 |
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/models/speech_recognition/tiny_wav2letter/tflite_pruned_int8/recreate_code/README.md:
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1 | # Tiny Wav2letter FP32/INT8/INT8_Pruned Model Re-Creation
2 | This folder contains a script that allows for the model to be re-created from scratch.
3 | ## Datasets
4 | Tiny Wav2Letter was trianed on both LibriSpeech dataset hosted on OpenSLR and fluent-speech-corpus dataset hosted on Kaggle.
5 | Please note that fluent-speech-corpus dataset hosted on [Kaggle](https://www.kaggle.com/tommyngx/fluent-speech-corpus) is a licensed dataset.
6 | ## Requirements
7 | The script in this folder requires that the following must be installed:
8 | - Python 3.6
9 | - Create new dir: fluent_speech_commands_dataset
10 | - (LICENSED DATASET!!) Download and extract fluent-speech-corpus from: https://www.kaggle.com/tommyngx/fluent-speech-corpus to fluent_speech_commands_dataset dir
11 |
12 | ## Running The Script
13 | To run the script, run the following in a terminal: `./recreate_model.sh`
14 |
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/models/speech_recognition/tiny_wav2letter/tflite_pruned_int8/recreate_code/evaluate_saved_weights.py:
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1 | # Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | import argparse
18 |
19 | import tensorflow as tf
20 |
21 | from tinywav2letter import get_metrics, create_tinywav2letter
22 | from train_model import get_data
23 |
24 | def evaluate_saved_weights(args, pruned = False):
25 |
26 | model = create_tinywav2letter(batch_size = args.batch_size)
27 |
28 | model.load_weights('weights/tiny_wav2letter' + pruned * "_pruned" + '_weights.h5')
29 |
30 | opt = tf.keras.optimizers.Adam()
31 | model.compile(loss=get_metrics("loss"), metrics=[get_metrics("ler")], optimizer=opt)
32 |
33 | (reduced_validation_data, reduced_validation_num_steps) = get_data(args, "val_reduced_size", args.batch_size)
34 |
35 | model.evaluate(reduced_validation_data)
36 |
37 |
38 | if __name__ == "__main__":
39 | parser = argparse.ArgumentParser(allow_abbrev=False)
40 |
41 | parser.add_argument(
42 | "--batch_size",
43 | dest="batch_size",
44 | type=int,
45 | required=False,
46 | default=32,
47 | help="batch size wanted when creating model",
48 | )
49 |
50 | args = parser.parse_args()
51 | evaluate_saved_weights(args)
52 |
53 |
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/models/speech_recognition/tiny_wav2letter/tflite_pruned_int8/recreate_code/recreate_model.sh:
--------------------------------------------------------------------------------
1 | python3 -m venv env
2 | source env/bin/activate
3 |
4 | pip install -r requirements.txt
5 | python preprocessing.py
6 | python train_model.py --with_baseline --baseline_epochs 30 --with_finetuning --finetuning_epochs 10 --with_fluent_speech --fluent_speech_epochs 30
7 | python prune_and_quantise_model.py --prune --sparsity 0.5 --finetuning_epochs 10
8 | python prune_and_quantise_model.py --sparsity 0.5 --finetuning_epochs 10
9 |
10 |
11 |
12 |
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/models/speech_recognition/tiny_wav2letter/tflite_pruned_int8/recreate_code/requirements.txt:
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1 | librosa==0.8.1
2 | numpy==1.19.5
3 | tensorboard==2.6.0
4 | tensorboard-data-server==0.6.1
5 | tensorboard-plugin-profile==2.5.0
6 | tensorboard-plugin-wit==1.8.0
7 | tensorflow==2.4.1
8 | tensorflow-model-optimization==0.6.0
9 | tqdm
10 | jiwer==2.3.0
11 |
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--------------------------------------------------------------------------------
1 | benchmark:
2 | LibriSpeech:
3 | LER: 0.0877
4 | description: Wav2letter is a convolutional speech recognition neural network. This
5 | implementation was created by Arm and quantized to the INT8 datatype.
6 | license:
7 | - Apache-2.0
8 | network:
9 | file_size_bytes: 23815520
10 | filename: wav2letter_int8.tflite
11 | framework: TensorFlow Lite
12 | hash:
13 | algorithm: sha1
14 | value: 481b7621801363b64dca2cc02b661b26866af76c
15 | provenance: https://github.com/ARM-software/ML-zoo/tree/master/models/speech_recognition/wav2letter/tflite_int8
16 | network_parameters:
17 | input_nodes:
18 | - description: Speech converted to MFCCs and quantized to INT8.
19 | example_input:
20 | path: models/speech_recognition/wav2letter/tflite_int8/testing_input/input_2_int8
21 | name: input_2_int8
22 | shape:
23 | - 1
24 | - 296
25 | - 39
26 | output_nodes:
27 | - description: A tensor of time and class probabilities, that represents the probability
28 | of each class at each timestep. Should be passed to a decoder. For example ctc_beam_search_decoder.
29 | name: Identity_int8
30 | shape:
31 | - 1
32 | - 1
33 | - 148
34 | - 29
35 | test_output_path: models/speech_recognition/wav2letter/tflite_int8/testing_output/Identity_int8
36 | operators:
37 | TensorFlow Lite:
38 | - CONV_2D
39 | - LEAKY_RELU
40 | - RESHAPE
41 | - SOFTMAX
42 | paper: https://arxiv.org/abs/1609.03193
43 |
--------------------------------------------------------------------------------
/models/speech_recognition/wav2letter/tflite_int8/get_class_labels.sh:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | python scripts/create_labels.py
20 |
--------------------------------------------------------------------------------
/models/speech_recognition/wav2letter/tflite_int8/scripts/create_labels.py:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | def get_label_dict():
18 | alphabet = "abcdefghijklmnopqrstuvwxyz' @"
19 | return [c for c in alphabet]
20 |
21 | if __name__ == "__main__":
22 | labels = get_label_dict()
23 |
24 | with open("labelmappings.txt", "w") as f:
25 | for l in labels:
26 | f.write('{}\n'.format(l))
27 |
--------------------------------------------------------------------------------
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/models/speech_recognition/wav2letter/tflite_pruned_int8/definition.yaml:
--------------------------------------------------------------------------------
1 | benchmark:
2 | LibriSpeech:
3 | LER: 0.0783
4 | description: Wav2letter is a convolutional speech recognition neural network. This
5 | implementation was created by Arm, pruned to 50% sparisty, fine-tuned and quantized
6 | using the TensorFlow Model Optimization Toolkit.
7 | license:
8 | - Apache-2.0
9 | network:
10 | file_size_bytes: 23766192
11 | filename: wav2letter_pruned_int8.tflite
12 | framework: TensorFlow Lite
13 | hash:
14 | algorithm: sha1
15 | value: 1771d122ba1ed9354188491e6efbcbd31cc8ba69
16 | provenance: https://github.com/ARM-software/ML-zoo/tree/master/models/speech_recognition/wav2letter/tflite_pruned_int8
17 | network_parameters:
18 | input_nodes:
19 | - description: Speech converted to MFCCs and quantized to INT8
20 | example_input:
21 | path: models/speech_recognition/wav2letter/tflite_pruned_int8/testing_input/input_4
22 | name: input_4
23 | shape:
24 | - 1
25 | - 296
26 | - 39
27 | type: int8
28 | output_nodes:
29 | - description: A tensor of (batch, time, class probabilities) that represents the
30 | probability of each class at each timestep. Should be passed to a decoder e.g.
31 | ctc_beam_search_decoder.
32 | name: Identity
33 | shape:
34 | - 1
35 | - 1
36 | - 148
37 | - 29
38 | test_output_path: models/speech_recognition/wav2letter/tflite_pruned_int8/testing_output/Identity
39 | operators:
40 | TensorFlow Lite:
41 | - CONV_2D
42 | - RESHAPE
43 | - LEAKY_RELU
44 | - SOFTMAX
45 | paper: https://arxiv.org/abs/1609.03193
46 |
--------------------------------------------------------------------------------
/models/speech_recognition/wav2letter/tflite_pruned_int8/get_class_labels.sh:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | python scripts/create_labels.py
20 |
--------------------------------------------------------------------------------
/models/speech_recognition/wav2letter/tflite_pruned_int8/recreate_model/README.md:
--------------------------------------------------------------------------------
1 | # Wav2letter Pruned int8 Model Re-Creation
2 | This folder contains a script that allows for the model to be re-created from scratch.
3 |
4 | ## Requirements
5 | The script in this folder requires that the following must be installed:
6 | - Python 3.6
7 | - Cuda 11.2
8 | - Sox
9 |
10 | ## Running The Script
11 | To run the script, run the following in a terminal: `./recreate_model.sh`
--------------------------------------------------------------------------------
/models/speech_recognition/wav2letter/tflite_pruned_int8/recreate_model/recreate_model.sh:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | python3.6 -m venv python_env
20 |
21 | source python_env/bin/activate
22 |
23 | pip install --upgrade pip
24 | pip install -r requirements.txt
25 |
26 | # Download and build dataset
27 | if [ ! -d "${HOME}/DeepSpeech" ] ; then
28 | git clone https://github.com/mozilla/DeepSpeech.git ${HOME}/DeepSpeech
29 | fi
30 | PYTHONPATH=${HOME}/DeepSpeech/training python ${HOME}/DeepSpeech/bin/import_librivox.py ${HOME}/librispeech
31 |
32 | python prune_quantize_model.py --data_dir ${HOME}/librispeech
33 |
--------------------------------------------------------------------------------
/models/speech_recognition/wav2letter/tflite_pruned_int8/recreate_model/requirements.txt:
--------------------------------------------------------------------------------
1 | tensorflow==2.5
2 | pandas==1.0.5
3 | librosa
4 | tensorflow_model_optimization
5 | tqdm
6 | progressbar2
7 | sox==1.3.7
8 |
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | def get_label_dict():
18 | alphabet = "abcdefghijklmnopqrstuvwxyz' @"
19 | return [c for c in alphabet]
20 |
21 | if __name__ == "__main__":
22 | labels = get_label_dict()
23 |
24 | with open("labelmappings.txt", "w") as f:
25 | for l in labels:
26 | f.write('{}\n'.format(l))
27 |
--------------------------------------------------------------------------------
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/models/visual_wake_words/micronet_vww2/tflite_int8/definition.yaml:
--------------------------------------------------------------------------------
1 | benchmark:
2 | Visual Wake Words:
3 | accuracy: 0.768
4 | description: 'This is a fully quantized version (asymmetrical int8) of the MicroNet
5 | VWW-2 model developed by Arm, from the MicroNets paper. It is trained on the ''Visual
6 | Wake Words'' dataset, more information can be found here: https://arxiv.org/pdf/1906.05721.pdf.'
7 | license:
8 | - Apache-2.0
9 | network:
10 | file_size_bytes: 280384
11 | filename: vww2_50_50_INT8.tflite
12 | framework: TensorFlow Lite
13 | hash:
14 | algorithm: sha1
15 | value: 5d887ca438c0a7feeed3c8c22dce99b55565c8ea
16 | provenance: https://arxiv.org/pdf/2010.11267.pdf
17 | network_parameters:
18 | input_nodes:
19 | - description: A 50x50 input image.
20 | example_input:
21 | path: models/visual_wake_words/micronet_vww2/tflite_int8/testing_input/input
22 | name: input
23 | shape:
24 | - 1
25 | - 50
26 | - 50
27 | - 1
28 | output_nodes:
29 | - description: Per-class confidence across the two classes (0=no person present,
30 | 1=person present).
31 | name: Identity
32 | shape:
33 | - 1
34 | - 2
35 | test_output_path: models/visual_wake_words/micronet_vww2/tflite_int8/testing_output/Identity
36 | operators:
37 | TensorFlow Lite:
38 | - ADD
39 | - AVERAGE_POOL_2D
40 | - CONV_2D
41 | - DEPTHWISE_CONV_2D
42 | - PAD
43 | - RELU6
44 | - RESHAPE
45 | paper: https://arxiv.org/pdf/2010.11267.pdf
46 |
--------------------------------------------------------------------------------
/models/visual_wake_words/micronet_vww2/tflite_int8/get_class_labels.sh:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | touch ./labelmappings.txt
20 | echo "Not Person" >> labelmappings.txt
21 | echo "Person" >> labelmappings.txt
22 |
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1 | benchmark:
2 | Visual Wake Words:
3 | Accuracy: 0.855
4 | description: 'This is a fully quantized version (asymmetrical int8) of the MicroNet
5 | VWW-3 model developed by Arm, from the MicroNets paper. It is trained on the ''Visual
6 | Wake Words'' dataset, more information can be found here: https://arxiv.org/pdf/1906.05721.pdf.'
7 | license:
8 | - Apache-2.0
9 | network:
10 | file_size_bytes: 542400
11 | filename: vww3_128_128_INT8.tflite
12 | framework: TensorFlow Lite
13 | hash:
14 | algorithm: sha1
15 | value: 1d739f1a0401f7959cdd8af5f281c46dcd2188eb
16 | provenance: https://arxiv.org/pdf/2010.11267.pdf
17 | network_parameters:
18 | input_nodes:
19 | - description: A 128x128 input image.
20 | example_input:
21 | path: models/visual_wake_words/micronet_vww3/tflite_int8/testing_input/input
22 | name: input
23 | shape:
24 | - 1
25 | - 128
26 | - 128
27 | - 1
28 | output_nodes:
29 | - description: Per-class confidence across the two classes (0=no person present,
30 | 1=person present).
31 | name: Identity
32 | shape:
33 | - 1
34 | - 2
35 | test_output_path: models/visual_wake_words/micronet_vww3/tflite_int8/testing_output/Identity
36 | operators:
37 | TensorFlow Lite:
38 | - ADD
39 | - AVERAGE_POOL_2D
40 | - CONV_2D
41 | - DEPTHWISE_CONV_2D
42 | - PAD
43 | - RELU6
44 | - RESHAPE
45 | paper: https://arxiv.org/pdf/2010.11267.pdf
46 |
--------------------------------------------------------------------------------
/models/visual_wake_words/micronet_vww3/tflite_int8/get_class_labels.sh:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | touch ./labelmappings.txt
20 | echo "Not Person" >> labelmappings.txt
21 | echo "Person" >> labelmappings.txt
22 |
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/models/visual_wake_words/micronet_vww4/tflite_int8/definition.yaml:
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1 | benchmark:
2 | Visual Wake Words:
3 | Accuracy: 0.822
4 | description: 'This is a fully quantized version (asymmetrical int8) of the MicroNet
5 | VWW-4 model developed by Arm, from the MicroNets paper. It is trained on the ''Visual
6 | Wake Words'' dataset, more information can be found here: https://arxiv.org/pdf/1906.05721.pdf.'
7 | license:
8 | - Apache-2.0
9 | network:
10 | file_size_bytes: 490336
11 | filename: vww4_128_128_INT8.tflite
12 | framework: TensorFlow Lite
13 | hash:
14 | algorithm: sha1
15 | value: b2d7f5fab21ae0934b76637348041a0aa5113ef6
16 | provenance: https://arxiv.org/pdf/2010.11267.pdf
17 | network_parameters:
18 | input_nodes:
19 | - description: A 128x128 input image.
20 | example_input:
21 | path: models/visual_wake_words/micronet_vww4/tflite_int8/testing_input/input
22 | name: input
23 | shape:
24 | - 1
25 | - 128
26 | - 128
27 | - 1
28 | output_nodes:
29 | - description: Per-class confidence across the two classes (0=no person present,
30 | 1=person present).
31 | name: Identity
32 | shape:
33 | - 1
34 | - 2
35 | test_output_path: models/visual_wake_words/micronet_vww4/tflite_int8/testing_output/Identity
36 | operators:
37 | TensorFlow Lite:
38 | - ADD
39 | - AVERAGE_POOL_2D
40 | - CONV_2D
41 | - DEPTHWISE_CONV_2D
42 | - PAD
43 | - RELU6
44 | - RESHAPE
45 | paper: https://arxiv.org/pdf/2010.11267.pdf
46 |
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/models/visual_wake_words/micronet_vww4/tflite_int8/get_class_labels.sh:
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1 | # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
2 | #
3 | # SPDX-License-Identifier: Apache-2.0
4 | #
5 | # Licensed under the Apache License, Version 2.0 (the License); you may
6 | # not use this file except in compliance with the License.
7 | # You may obtain a copy of the License at
8 | #
9 | # www.apache.org/licenses/LICENSE-2.0
10 | #
11 | # Unless required by applicable law or agreed to in writing, software
12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT
13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 | # See the License for the specific language governing permissions and
15 | # limitations under the License.
16 |
17 | #!/usr/bin/env bash
18 |
19 | touch ./labelmappings.txt
20 | echo "Not Person" >> labelmappings.txt
21 | echo "Person" >> labelmappings.txt
22 |
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/models/visual_wake_words/micronet_vww4/tflite_int8/testing_input/input/0.npy:
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/tutorials/transformer_tutorials/README.md:
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1 | # Transformer Tutorials
2 |
3 | Jupiter notebooks showing how to quantise and compress toy transformer encoder and encoder-decoder models.
4 |
5 | # Tutorials
6 |
7 | * ViT_PCQAT.ipynb - Shows PCQAT using TFMOT for transformer encoder models.
8 | * ViT_2x4-PQAT.ipynb - Shows new 2x4 pruning and QAT with TFMOT for transformer encoder models.
9 | * translation.ipynb - Shows how to do QAT with TFMOT for encoder-decoder models.
10 | * translation_PQAT.ipynb - Shows how to apply pruning & QAT with TFMOT for encoder-decoder models.
11 |
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/tutorials/transformer_tutorials/install.sh:
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1 | #!/usr/bin/env bash
2 |
3 | #Creates virtual environment and installs dependencies
4 |
5 | python3 -m venv ./venv
6 | source ./venv/bin/activate
7 | pip install --upgrade pip
8 | pip install -r requirements.txt
9 |
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/tutorials/transformer_tutorials/requirements.txt:
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1 | notebook
2 | tensorflow>=2.6,<2.8
3 | tensorflow_model_optimization==0.7.0
4 | nltk==3.6.5
5 | flatbuffers==1.12.0
6 |
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