├── .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: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_STORE 2 | .vscode -------------------------------------------------------------------------------- /models/anomaly_detection/micronet_large/tflite_int8/ad_large_int8.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:f1cd8dabc12adb91b89d936bdac3eb3ebff6770bf9b9c449cb800d08fcb160a8 3 | size 442000 4 | -------------------------------------------------------------------------------- /models/anomaly_detection/micronet_large/tflite_int8/definition.yaml: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /models/anomaly_detection/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 | touch ./labelmappings.txt 20 | echo "id0" >> labelmappings.txt 21 | echo "id2" >> labelmappings.txt 22 | echo "id4" >> labelmappings.txt 23 | echo "id6" >> labelmappings.txt 24 | -------------------------------------------------------------------------------- /models/anomaly_detection/micronet_large/tflite_int8/testing_input/input/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:f525bddd8c5c521d2ac3e1a8fcf7c28f1fe113f620852121b5498fa0a2f2f617 3 | size 1152 4 | -------------------------------------------------------------------------------- /models/anomaly_detection/micronet_large/tflite_int8/testing_output/Identity/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:9fadbd73ca3b52e1f6c15c857a1681d3b29bb7c1b1ab29cd5ab4e311b0134c59 3 | size 136 4 | -------------------------------------------------------------------------------- /models/anomaly_detection/micronet_medium/tflite_int8/ad_medium_int8.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:5a1265f67367f238c4ccc4c2ccd9c280908a9d5f98a6f88d512c147b1f1309a8 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 | -------------------------------------------------------------------------------- /models/anomaly_detection/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 | touch ./labelmappings.txt 20 | echo "id0" >> labelmappings.txt 21 | echo "id2" >> labelmappings.txt 22 | echo "id4" >> labelmappings.txt 23 | echo "id6" >> labelmappings.txt 24 | -------------------------------------------------------------------------------- /models/anomaly_detection/micronet_medium/tflite_int8/testing_input/input/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:44441994fb518152099ba058456a899e382482e2d95b39ef71b0c7dddbbbcd68 3 | size 1152 4 | -------------------------------------------------------------------------------- /models/anomaly_detection/micronet_medium/tflite_int8/testing_output/Identity/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:31d857b41292cd8c6ed2902e1a0e0dd2da8ffc66ae067bbee25d4e1a336d0e22 3 | size 136 4 | -------------------------------------------------------------------------------- /models/anomaly_detection/micronet_small/tflite_int8/ad_small_int8.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:73db15c7f1b3c2a3bec8431258be9724b269866645aabfe99eba016da35e5d40 3 | size 252848 4 | -------------------------------------------------------------------------------- /models/anomaly_detection/micronet_small/tflite_int8/definition.yaml: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /models/anomaly_detection/micronet_small/tflite_int8/testing_input/input/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:f0f8e659165cc697946051f17c6f98266e89099979dcfe4b04bdd2209779f25e 3 | size 1152 4 | -------------------------------------------------------------------------------- /models/anomaly_detection/micronet_small/tflite_int8/testing_output/Identity/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:7f446574aa5b4dab717d4888aa74010e9d258bf8e9bce120f339c5429a7d0fd7 3 | size 136 4 | -------------------------------------------------------------------------------- /models/experimental/efficientnet_lite0_224/README.md: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /models/experimental/efficientnet_lite0_224/efficientnet_lite0_224.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:31c18ecf5cfb7bddf182e450e4029834271fc70e15b5fae6ddb5995ac2a6de51 3 | size 5422760 4 | -------------------------------------------------------------------------------- /models/experimental/har_cnn/README.md: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /models/experimental/har_cnn/har_int8.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:0ea9fb906272246cdad280e4fbda78c20fa55109b36028988a7f861a8e1c466f 3 | size 747936 4 | -------------------------------------------------------------------------------- /models/experimental/ssd_mobilenet_v3_int8/README.md: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /models/experimental/ssd_mobilenet_v3_int8/ssd_mobilenet_v3_int8.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:673978b17406e6b204ea8a0f2dac814f8b31ec1e9ddf13caa17362359a616fd2 3 | size 2423680 4 | -------------------------------------------------------------------------------- /models/experimental/yolov3_416_416_backbone_mltools_int8/README.md: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /models/experimental/yolov3_416_416_backbone_mltools_int8/yolov3_416_416_backbone_mltools_int8.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:361c376dcff92738171f5c48388fbe28d9bb527c8ada1e4d3c3400f36dde8c8a 3 | size 62958128 4 | -------------------------------------------------------------------------------- /models/experimental/yolov3_tiny_int8_pruned_backbone_only/yolov3_tiny_int8_pruned_backbone_only.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:27cf1295b04969bd487d7717750d47d217ae22b5aef9e1b70d61835f23e8fc80 3 | size 8963352 4 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /models/image_classification/mobilenet_v2_1.0_224/tflite_int8/mobilenet_v2_1.0_224_INT8.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:bd8d56b80557a5fca7acf14f61e5339285c02810f7d7afa714b9763fc4b86e95 3 | size 4020936 4 | -------------------------------------------------------------------------------- /models/image_classification/mobilenet_v2_1.0_224/tflite_int8/recreate_model/README.md: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /models/image_classification/mobilenet_v2_1.0_224/tflite_int8/testing_input/tfl.quantize/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:454b7a6fea8ed14e236e831f1d77114a476a348fbbfab2dce5341d7e9e17cc64 3 | size 150656 4 | -------------------------------------------------------------------------------- /models/image_classification/mobilenet_v2_1.0_224/tflite_int8/testing_output/MobilenetV2/Predictions/Reshape_11/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:f1b5d4fce5a6f8ddaf13352229cd862285f6cd0d48b17a7d11ec5881ba499ae7 3 | size 1129 4 | -------------------------------------------------------------------------------- /models/image_classification/mobilenet_v2_1.0_224/tflite_uint8/definition.yaml: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /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 -------------------------------------------------------------------------------- /models/image_classification/mobilenet_v2_1.0_224/tflite_uint8/mobilenet_v2_1.0_224_quantized_1_default_1.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:f08d447cde49b4e0446428aa921aff0a14ea589fa9c5817b31f83128e9a43c1d 3 | size 3577760 4 | -------------------------------------------------------------------------------- /models/image_classification/mobilenet_v2_1.0_224/tflite_uint8/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 | -------------------------------------------------------------------------------- /models/image_classification/mobilenet_v2_1.0_224/tflite_uint8/testing_input/input/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:583c32a4f428df9625b15f3ab271e4f7051848d59dff0f6a6968a20cb86dab99 3 | size 150656 4 | -------------------------------------------------------------------------------- /models/image_classification/mobilenet_v2_1.0_224/tflite_uint8/testing_output/output/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:4eb3a6a11d9bec5b5e80a53e261b97049998e46510899557a54b53f34b3434cf 3 | size 1129 4 | -------------------------------------------------------------------------------- /models/keyword_spotting/cnn_large/model_package_tf/data_processing/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ARM-software/ML-zoo/fec0bb5bcd486eb2839cdb7d437d466067402a6a/models/keyword_spotting/cnn_large/model_package_tf/data_processing/__init__.py -------------------------------------------------------------------------------- /models/keyword_spotting/cnn_large/model_package_tf/model_archive/TFLite/tflite_fp32/cnn_l.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:f1a82f9c75ab57bafccbe9a154454d228c9610bd66cb186a69bab4fcc9958558 3 | size 1908316 4 | -------------------------------------------------------------------------------- /models/keyword_spotting/cnn_large/model_package_tf/model_archive/TFLite/tflite_fp32/definition.yaml: -------------------------------------------------------------------------------- 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 -------------------------------------------------------------------------------- /models/keyword_spotting/cnn_large/model_package_tf/model_archive/TFLite/tflite_fp32/testing_input/input/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:c0d3177ad9e25a08e300d6dab37303348cc99cda9137a0ed98bfe4ecabb4cbe2 3 | size 2088 4 | -------------------------------------------------------------------------------- /models/keyword_spotting/cnn_large/model_package_tf/model_archive/TFLite/tflite_fp32/testing_output/Identity/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:84727ee69c9018fcd7295ca5646c29a982b948ce3abd7c4a9c44c7203c699b24 3 | size 176 4 | -------------------------------------------------------------------------------- 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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 | 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stop 12 | go -------------------------------------------------------------------------------- /models/keyword_spotting/dnn_large/model_package_tf/data_processing/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ARM-software/ML-zoo/fec0bb5bcd486eb2839cdb7d437d466067402a6a/models/keyword_spotting/dnn_large/model_package_tf/data_processing/__init__.py -------------------------------------------------------------------------------- /models/keyword_spotting/dnn_large/model_package_tf/model_archive/TFLite/tflite_fp32/definition.yaml: -------------------------------------------------------------------------------- 1 | benchmark: 2 | benchmark_metrics: 3 | accuracy: 86.65% 4 | benchmark_name: Google Speech Commands test set 5 | description: This is a floating point fp32 version of the DNN 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: 1985048 12 | filename: dnn_l.tflite 13 | framework: TensorFlow Lite 14 | hash: 15 | algorithm: sha1 16 | value: 1ce04d01ed7decc016076a868f22858d8f092942 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/dnn_large/model_package_tf/model_archive/TFLite/tflite_fp32/testing_input/input 24 | shape: 25 | - 1 26 | - 250 27 | type: fp32 28 | use_case: Random input for model regression. 29 | input_datatype: fp32 30 | name: input 31 | shape: 32 | - 1 33 | - 250 34 | output_nodes: 35 | - description: The probability on 12 keywords. 36 | example_output: 37 | path: models/keyword_spotting/dnn_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 | - FULLY_CONNECTED 60 | - RELU 61 | - SOFTMAX 62 | paper: https://arxiv.org/abs/1711.07128 -------------------------------------------------------------------------------- /models/keyword_spotting/dnn_large/model_package_tf/model_archive/TFLite/tflite_fp32/dnn_l.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:dffdcf515fce70988132d98f8007564e0b303d0b463c422f039e2074cb29fc51 3 | size 1985048 4 | -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | benchmark: 2 | benchmark_metrics: 3 | accuracy: 86.01% 4 | benchmark_name: Google Speech Commands test set 5 | description: This is a fully quantized int8 version of the DNN 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: 502272 12 | filename: dnn_l_quantized.tflite 13 | framework: TensorFlow Lite 14 | hash: 15 | algorithm: sha1 16 | value: 2b1ee34e4c87ba6f24092c7457593227099efaf1 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, 250) 22 | example_input: 23 | path: models/keyword_spotting/dnn_large/model_package_tf/model_archive/TFLite/tflite_int8/testing_input/input 24 | shape: 25 | - 1 26 | - 250 27 | type: int8 28 | use_case: Random input for model regression. 29 | input_datatype: int8 30 | name: input 31 | shape: 32 | - 1 33 | - 250 34 | output_nodes: 35 | - description: The probability on 12 keywords. 36 | example_output: 37 | path: models/keyword_spotting/dnn_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 | - FULLY_CONNECTED 60 | - RELU 61 | - SOFTMAX 62 | paper: https://arxiv.org/abs/1711.07128 -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | benchmark: 2 | benchmark_metrics: 3 | accuracy: 84.95% 4 | benchmark_name: Google Speech Commands test set 5 | description: This is a floating point fp32 version of the DNN 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: 797768 12 | filename: dnn_m.tflite 13 | framework: TensorFlow Lite 14 | hash: 15 | algorithm: sha1 16 | value: 3c20c6ee24ee41ed6db968ff58d69f5823c94036 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/dnn_medium/model_package_tf/model_archive/TFLite/tflite_fp32/testing_input/input 24 | shape: 25 | - 1 26 | - 250 27 | type: fp32 28 | use_case: Random input for model regression. 29 | 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processed MFCCs of shape (1, 250) 22 | example_input: 23 | path: models/keyword_spotting/dnn_medium/model_package_tf/model_archive/TFLite/tflite_int8/testing_input/input 24 | shape: 25 | - 1 26 | - 250 27 | type: int8 28 | use_case: Random input for model regression. 29 | input_datatype: int8 30 | name: input 31 | shape: 32 | - 1 33 | - 250 34 | output_nodes: 35 | - description: The probability on 12 keywords. 36 | example_output: 37 | path: models/keyword_spotting/dnn_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 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processed MFCCs of shape (1, 490) 22 | example_input: 23 | path: models/keyword_spotting/ds_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/ds_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 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description: The probability on 12 keywords. 36 | example_output: 37 | path: models/keyword_spotting/ds_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 | - 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 -------------------------------------------------------------------------------- /models/keyword_spotting/ds_cnn_medium/model_package_tf/model_archive/TFLite/tflite_fp32/ds_cnn_m.tflite: 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input is a processed MFCCs of shape (1, 490) 22 | example_input: 23 | path: models/keyword_spotting/ds_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/ds_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 | 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Lite 14 | hash: 15 | algorithm: sha1 16 | value: 8aadd5126bc0d3371c1b834d027c853e794423c1 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_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/ds_cnn_small/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: Hero 53 | quality_level_hero_hw: cortex_m 54 | quantization_default: false 55 | quantization_full: false 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 -------------------------------------------------------------------------------- /models/keyword_spotting/ds_cnn_small/model_package_tf/model_archive/TFLite/tflite_fp32/ds_cnn_s.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:0d302f1f2c53c1344edcde850e28130c0877b60e1567db977292239a9391f59b 3 | size 98756 4 | -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | benchmark: 2 | benchmark_metrics: 3 | Accuracy: 93.39% 4 | benchmark_name: Google Speech Commands test set 5 | description: This is a fully quantized int16 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: int16 11 | file_size_bytes: 55392 12 | filename: ds_cnn_s_quantized_int16.tflite 13 | framework: TensorFlow Lite 14 | hash: 15 | algorithm: sha1 16 | value: e82c7d645bec3dec580a096de0a297c6dd9a6463 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_int16/testing_input 24 | shape: 25 | - 1 26 | - 490 27 | type: int16 28 | use_case: Random input for model regression. 29 | input_datatype: int16 30 | name: serving_default_input:0 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_int16/testing_output 38 | shape: 39 | - 1 40 | - 12 41 | type: int16 42 | use_case: output for model regression. 43 | name: StatefulPartitionedCall:0 44 | output_datatype: int16 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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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 -------------------------------------------------------------------------------- /models/keyword_spotting/ds_cnn_small/model_package_tf/model_archive/TFLite/tflite_int8/ds_cnn_s_quantized.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:0f235d3602308653fcdb50d73462080dec73f21473b4856f05f307d0eb24ca1a 3 | size 47616 4 | -------------------------------------------------------------------------------- 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/models/keyword_spotting/ds_cnn_small/model_package_tf/model_core_utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ARM-software/ML-zoo/fec0bb5bcd486eb2839cdb7d437d466067402a6a/models/keyword_spotting/ds_cnn_small/model_package_tf/model_core_utils/__init__.py -------------------------------------------------------------------------------- /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: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:15723bcc7d73599546cfebd289632799393e329df84bba883205b08b666a1aa3 3 | size 658832 4 | -------------------------------------------------------------------------------- /models/keyword_spotting/micronet_large/tflite_int8/testing_input/input/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:babed7a966e0901d59301080947e3f3b3a00d397ab2171bc2e917e1d67e38369 3 | size 618 4 | -------------------------------------------------------------------------------- /models/keyword_spotting/micronet_large/tflite_int8/testing_output/Identity/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:3fa8e00b4d2adba3f9a9b1d239bb585d174a0c76292b9464d139165fea328935 3 | size 140 4 | -------------------------------------------------------------------------------- /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: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:c1feed3af5dac44de7477fb4161670ba18a3fc06039e4a14da41b1c4dd454cb4 3 | size 181968 4 | -------------------------------------------------------------------------------- /models/keyword_spotting/micronet_medium/tflite_int8/testing_input/input/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:afac928cb5a966030eac08a0943c169fe5e80d0ba0d5e97986c90aba1afd3fe2 3 | size 618 4 | -------------------------------------------------------------------------------- /models/keyword_spotting/micronet_medium/tflite_int8/testing_output/Identity/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:302e3559ac7e867b477247d7625bc2408d6c27c27092c8b95f5e821a55652b72 3 | size 140 4 | -------------------------------------------------------------------------------- /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 -------------------------------------------------------------------------------- /models/keyword_spotting/micronet_small/tflite_int8/kws_micronet_s.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:0b04ee059edf9e392bb9b744a051f19818a662453b0fb4af825267f1a1937dae 3 | size 114512 4 | -------------------------------------------------------------------------------- /models/keyword_spotting/micronet_small/tflite_int8/testing_input/input/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:854f466a4358f532f08ee5bfce2f2163b12ae6092b82545033a0e725d620988d 3 | size 618 4 | -------------------------------------------------------------------------------- /models/keyword_spotting/micronet_small/tflite_int8/testing_output/Identity/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:55e557d2c13ed59502f5b98265743347232c8402a38f133af44e046fc90824c9 3 | size 140 4 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /models/noise_suppression/RNNoise/tflite_int8/rnnoise_INT8.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:9c582545b7c13af44616c44b654f4fe721aa2585630b0ca173ca3589f6f11c2c 3 | size 113472 4 | -------------------------------------------------------------------------------- /models/noise_suppression/RNNoise/tflite_int8/testing_input/denoise_gru_prev_state_int8/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:427d58fb50aecd53ecb22ca6d0de8a068ae3e1a1f2fb8b28126267a3c4076c48 3 | size 224 4 | -------------------------------------------------------------------------------- /models/noise_suppression/RNNoise/tflite_int8/testing_input/main_input_int8/0.npy: 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sha256:3050901964cb681bc57a8eb484d63e7e264197d4b00840017e27b1ecad83ae57 3 | size 152 4 | -------------------------------------------------------------------------------- /models/noise_suppression/RNNoise/tflite_int8/testing_output/Identity_1_int8/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:ee0d3f36d43246b80e74431cab8b85917082a845c03c922b77887a99739d5789 3 | size 150 4 | -------------------------------------------------------------------------------- /models/noise_suppression/RNNoise/tflite_int8/testing_output/Identity_2_int8/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:605d758f172a3bcd4c11d13dcd1df8232b5cd8eac02344716f3d017e18497a9d 3 | size 176 4 | -------------------------------------------------------------------------------- /models/noise_suppression/RNNoise/tflite_int8/testing_output/Identity_3_int8/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:6df79eec775860d81783c16a602214e5e5906a7462751346ff84bf593c308c04 3 | size 152 4 | -------------------------------------------------------------------------------- /models/noise_suppression/RNNoise/tflite_int8/testing_output/Identity_4_int8/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:4ede4d14fb1c3ab0e6dac0bf029cdf516320032909f3b02a1e82eb15a2aaae97 3 | size 129 4 | -------------------------------------------------------------------------------- /models/noise_suppression/RNNoise/tflite_int8/testing_output/Identity_int8/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:632c56e71c9d4f22079f03d0cdc70321ba6116fe76a64fee695a80b4462eb876 3 | size 224 4 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /models/object_detection/ssd_mobilenet_v1/tflite_fp32/ssd_mobilenet_v1.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:d15f8495fce308b774fd25cf4584e6f682f2d9d1b7cb6aa81cea2d784874c51b 3 | size 27286108 4 | -------------------------------------------------------------------------------- /models/object_detection/ssd_mobilenet_v1/tflite_fp32/testing_input/normalized_input_image_tensor/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:456fcc6f38328a8ac0ee1fec4ce207bd07a5b8e9d617fdfc7f21aa349c1fa513 3 | size 1080128 4 | -------------------------------------------------------------------------------- /models/object_detection/ssd_mobilenet_v1/tflite_fp32/testing_output/TFLite_Detection_PostProcess/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:872a8069eaa4ed570e788cb6665e71727f0795d5f9330363936744d8e08bb978 3 | size 288 4 | -------------------------------------------------------------------------------- /models/object_detection/ssd_mobilenet_v1/tflite_fp32/testing_output/TFLite_Detection_PostProcess:1/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:705b6eb67e7bb1f2a845b4e959781a9849fff07d4f113f287145b6f606f50fc0 3 | size 168 4 | -------------------------------------------------------------------------------- /models/object_detection/ssd_mobilenet_v1/tflite_fp32/testing_output/TFLite_Detection_PostProcess:2/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:377a64daa12a3d06de3cd1115168337fc22e22457b504d8acc6aef558d47ec40 3 | size 168 4 | -------------------------------------------------------------------------------- /models/object_detection/ssd_mobilenet_v1/tflite_fp32/testing_output/TFLite_Detection_PostProcess:3/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:6797b3283f2e3d8b8b776514e53a9c60d580bc8a97497d7849a56c45b1dc6903 3 | size 132 4 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /models/object_detection/ssd_mobilenet_v1/tflite_int8/ssd_mobilenet_v1.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:107dda1b176518ffaf9b41466329d9f957dd64dc0838f1d376f9d2c6893b3bad 3 | size 7311392 4 | -------------------------------------------------------------------------------- /models/object_detection/ssd_mobilenet_v1/tflite_int8/testing_input/tfl.quantize/0.npy: 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sha256:1980fa23ff8319de9454a7cdda7ff43cc72ceb5435a461b9a1c1ecdff6115858 3 | size 528 4 | -------------------------------------------------------------------------------- /models/object_detection/ssd_mobilenet_v1/tflite_int8/testing_output/TFLite_Detection_PostProcess:03/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:9ae7e74b80dc854442b92876896a296e4a216aa6b4cbfa188e4ee3b1ee201989 3 | size 528 4 | -------------------------------------------------------------------------------- /models/object_detection/ssd_mobilenet_v1/tflite_int8/testing_output/TFLite_Detection_PostProcess:04/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:4396be9607d0a994dba58eb282e281a00da2c554c420229e52b8900d8ac701b1 3 | size 132 4 | -------------------------------------------------------------------------------- /models/object_detection/ssd_mobilenet_v1/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 | 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 | -------------------------------------------------------------------------------- /models/object_detection/ssd_mobilenet_v1/tflite_uint8/ssd_mobilenet_v1.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:d56016cac6056ce85ea551c360f58ada860b5016b8fa5fc60e87450a0bf5790a 3 | size 6898880 4 | -------------------------------------------------------------------------------- /models/object_detection/ssd_mobilenet_v1/tflite_uint8/testing_input/normalized_input_image_tensor/0.npy: 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sha256:364efcd04b7ab180a0c229ab0e329577f1decd44d43aff6bbbff8fb1b28b4f94 3 | size 168 4 | -------------------------------------------------------------------------------- /models/object_detection/ssd_mobilenet_v1/tflite_uint8/testing_output/TFLite_Detection_PostProcess:2/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:28d5f511907e2bd708c9b9620c685d3c1736eea80b6789d27fa92e06c0ffa8a7 3 | size 168 4 | -------------------------------------------------------------------------------- /models/object_detection/ssd_mobilenet_v1/tflite_uint8/testing_output/TFLite_Detection_PostProcess:3/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:4396be9607d0a994dba58eb282e281a00da2c554c420229e52b8900d8ac701b1 3 | size 132 4 | -------------------------------------------------------------------------------- /models/object_detection/yolo_v3_tiny/tflite_fp32/definition.yaml: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /models/object_detection/yolo_v3_tiny/tflite_fp32/recreate_model/requirements.txt: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /models/object_detection/yolo_v3_tiny/tflite_fp32/testing_input/inputs/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:76f3ef8e905eed2f85d2c4a9b2bb4d83dbbc69f37880e3794a3fec100d684930 3 | size 2076800 4 | -------------------------------------------------------------------------------- /models/object_detection/yolo_v3_tiny/tflite_fp32/testing_output/output_boxes/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:a6f95a898a8976b57bea2e48a48ffd742c5887049339092ac0389aabc5bf6797 3 | size 862028 4 | -------------------------------------------------------------------------------- /models/object_detection/yolo_v3_tiny/tflite_fp32/yolo_v3_tiny_darknet_fp32.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:e247d90cbd4fd682b7ed4b8b4d5a905361e9c8c285825f52b41e10bc012f8cd6 3 | size 35455980 4 | -------------------------------------------------------------------------------- /models/speech_recognition/tiny_wav2letter/tflite_int8/demo_input/84-121550-0000.flac: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ARM-software/ML-zoo/fec0bb5bcd486eb2839cdb7d437d466067402a6a/models/speech_recognition/tiny_wav2letter/tflite_int8/demo_input/84-121550-0000.flac -------------------------------------------------------------------------------- /models/speech_recognition/tiny_wav2letter/tflite_int8/recreate_code/.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 7 | -------------------------------------------------------------------------------- /models/speech_recognition/tiny_wav2letter/tflite_int8/recreate_code/README.md: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /models/speech_recognition/tiny_wav2letter/tflite_int8/recreate_code/__pycache__/load_mfccs.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ARM-software/ML-zoo/fec0bb5bcd486eb2839cdb7d437d466067402a6a/models/speech_recognition/tiny_wav2letter/tflite_int8/recreate_code/__pycache__/load_mfccs.cpython-36.pyc -------------------------------------------------------------------------------- /models/speech_recognition/tiny_wav2letter/tflite_int8/recreate_code/__pycache__/tinywav2letter.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ARM-software/ML-zoo/fec0bb5bcd486eb2839cdb7d437d466067402a6a/models/speech_recognition/tiny_wav2letter/tflite_int8/recreate_code/__pycache__/tinywav2letter.cpython-36.pyc -------------------------------------------------------------------------------- /models/speech_recognition/tiny_wav2letter/tflite_int8/recreate_code/__pycache__/train_model.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ARM-software/ML-zoo/fec0bb5bcd486eb2839cdb7d437d466067402a6a/models/speech_recognition/tiny_wav2letter/tflite_int8/recreate_code/__pycache__/train_model.cpython-36.pyc -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /models/speech_recognition/tiny_wav2letter/tflite_int8/recreate_code/requirements.txt: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /models/speech_recognition/tiny_wav2letter/tflite_int8/recreate_code/saved_models/pruned_tiny_wav2letter/saved_model.pb: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:081d7d86e2b6fd788ca37b9566213560140f595d7702a2126b5a4b895d00cc9e 3 | size 206996 4 | -------------------------------------------------------------------------------- /models/speech_recognition/tiny_wav2letter/tflite_int8/recreate_code/saved_models/pruned_tiny_wav2letter/variables/variables.data-00000-of-00001: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:df3647d94be8c5feb098f69122e22007b11873bddb11bdc606112576d4588d4f 3 | size 15644464 4 | 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| size 3997112 4 | -------------------------------------------------------------------------------- /models/speech_recognition/tiny_wav2letter/tflite_pruned_int8/demo_input/84-121550-0000.flac: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ARM-software/ML-zoo/fec0bb5bcd486eb2839cdb7d437d466067402a6a/models/speech_recognition/tiny_wav2letter/tflite_pruned_int8/demo_input/84-121550-0000.flac -------------------------------------------------------------------------------- /models/speech_recognition/tiny_wav2letter/tflite_pruned_int8/recreate_code/.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 7 | -------------------------------------------------------------------------------- /models/speech_recognition/tiny_wav2letter/tflite_pruned_int8/recreate_code/README.md: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /models/speech_recognition/tiny_wav2letter/tflite_pruned_int8/recreate_code/__pycache__/load_mfccs.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ARM-software/ML-zoo/fec0bb5bcd486eb2839cdb7d437d466067402a6a/models/speech_recognition/tiny_wav2letter/tflite_pruned_int8/recreate_code/__pycache__/load_mfccs.cpython-36.pyc -------------------------------------------------------------------------------- /models/speech_recognition/tiny_wav2letter/tflite_pruned_int8/recreate_code/__pycache__/tinywav2letter.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ARM-software/ML-zoo/fec0bb5bcd486eb2839cdb7d437d466067402a6a/models/speech_recognition/tiny_wav2letter/tflite_pruned_int8/recreate_code/__pycache__/tinywav2letter.cpython-36.pyc -------------------------------------------------------------------------------- /models/speech_recognition/tiny_wav2letter/tflite_pruned_int8/recreate_code/__pycache__/train_model.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ARM-software/ML-zoo/fec0bb5bcd486eb2839cdb7d437d466067402a6a/models/speech_recognition/tiny_wav2letter/tflite_pruned_int8/recreate_code/__pycache__/train_model.cpython-36.pyc -------------------------------------------------------------------------------- /models/speech_recognition/tiny_wav2letter/tflite_pruned_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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /models/speech_recognition/tiny_wav2letter/tflite_pruned_int8/recreate_code/requirements.txt: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /models/speech_recognition/tiny_wav2letter/tflite_pruned_int8/recreate_code/saved_models/pruned_tiny_wav2letter/saved_model.pb: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:081d7d86e2b6fd788ca37b9566213560140f595d7702a2126b5a4b895d00cc9e 3 | size 206996 4 | -------------------------------------------------------------------------------- /models/speech_recognition/tiny_wav2letter/tflite_pruned_int8/recreate_code/saved_models/pruned_tiny_wav2letter/variables/variables.data-00000-of-00001: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:df3647d94be8c5feb098f69122e22007b11873bddb11bdc606112576d4588d4f 3 | size 15644464 4 | 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sha256:4487c1f55e60b0824939f9ae0bce9522489700a8d9bcb1830f17822d4a5c0041 3 | size 3997112 4 | -------------------------------------------------------------------------------- /models/speech_recognition/wav2letter/tflite_int8/definition.yaml: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /models/speech_recognition/wav2letter/tflite_int8/testing_input/input_2_int8/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:c05d4b7c3b564502916b783317aae1abd53c952903e193fa1a21bf0d0e98093e 3 | size 11672 4 | -------------------------------------------------------------------------------- /models/speech_recognition/wav2letter/tflite_int8/testing_output/Identity_int8/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:f8e6e582c64513166ea199fbb4c21cd936fc9d969917091f659880191922b7f2 3 | size 4420 4 | -------------------------------------------------------------------------------- /models/speech_recognition/wav2letter/tflite_int8/wav2letter_int8.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:81ed9280856ca6b2b3db3f822ec7375fd71f89b9c3c7903409d5e7857caf11a7 3 | size 23815520 4 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /models/speech_recognition/wav2letter/tflite_pruned_int8/recreate_model/weights/wav2letter.h5: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:a2cfc6ed8c9a1b97b2b9c52c08e0ffad5eb811d0548216276d1d45c169e81262 3 | size 94420016 4 | -------------------------------------------------------------------------------- /models/speech_recognition/wav2letter/tflite_pruned_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 | -------------------------------------------------------------------------------- /models/speech_recognition/wav2letter/tflite_pruned_int8/testing_input/input_4/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:61ad67cf696943bf6779d520979a657a785fed2322863ba3f693f616dc49917d 3 | size 11672 4 | -------------------------------------------------------------------------------- /models/speech_recognition/wav2letter/tflite_pruned_int8/testing_output/Identity/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:42a1ed1500282b9cc1ebb88f0658af5a5e3e6189985c97c2ad7521ed9789a458 3 | size 4420 4 | -------------------------------------------------------------------------------- /models/speech_recognition/wav2letter/tflite_pruned_int8/wav2letter_pruned_int8.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:1acd36de9271fbb1ca5b1c3c5c36379da8a77563923a4d6dee77a7dac6ffc2a8 3 | size 23766192 4 | -------------------------------------------------------------------------------- /models/superresolution/SESR/tflite_int8/SESR_1080p_to_4K_withD2S_full_int8.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:2b63f839927a66e39d5b069a0454bf93bd0cbd453a162b2cd99c21d746814ac9 3 | size 23680 4 | -------------------------------------------------------------------------------- /models/superresolution/SESR/tflite_int8/testing_input/net_input/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:b82b278138ba3809419216fa5aca128055df5cff8f6cd09a2befdd32b94ea270 3 | size 2073728 4 | -------------------------------------------------------------------------------- /models/superresolution/SESR/tflite_int8/testing_output/net_output/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:eaeeaf7bb4689df05938816588bfba28d2c565ed1a3f0e3bd378246b02344a28 3 | size 8294528 4 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /models/visual_wake_words/micronet_vww2/tflite_int8/testing_input/input/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:24c51ae0493194c187c8011eec93c265b64961c60adf76de7510e17ba4832110 3 | size 2628 4 | -------------------------------------------------------------------------------- /models/visual_wake_words/micronet_vww2/tflite_int8/testing_output/Identity/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:29a9060fc3fec7b0376ffa6cb10b78c1a0660c3905d39f454dc192bf75ddd096 3 | size 130 4 | -------------------------------------------------------------------------------- /models/visual_wake_words/micronet_vww2/tflite_int8/vww2_50_50_INT8.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:0c295ece396e33d625c0a3726f1d1f9a7b879612c05a973d0cbf5edf43bf324c 3 | size 280384 4 | -------------------------------------------------------------------------------- /models/visual_wake_words/micronet_vww3/tflite_int8/definition.yaml: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /models/visual_wake_words/micronet_vww3/tflite_int8/testing_input/input/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:70994bc48af9434e544435eddf3346bbf630b7a1aab30090a7ab3371699973be 3 | size 16512 4 | -------------------------------------------------------------------------------- /models/visual_wake_words/micronet_vww3/tflite_int8/testing_output/Identity/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:e325f637384ed04a32e3f97a847e935f5ae0397c7b60968bac56539a1b1569ff 3 | size 130 4 | -------------------------------------------------------------------------------- /models/visual_wake_words/micronet_vww3/tflite_int8/vww3_128_128_INT8.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:121956b236b8567a723179e1a53ad4baf5253199b7b5be0268260814d9901d2c 3 | size 542400 4 | -------------------------------------------------------------------------------- /models/visual_wake_words/micronet_vww4/tflite_int8/definition.yaml: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /models/visual_wake_words/micronet_vww4/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 | -------------------------------------------------------------------------------- /models/visual_wake_words/micronet_vww4/tflite_int8/testing_input/input/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:79f1c54d387f84e3d10bda5299d8eefdc0a684a95e9cf463152414335cb44700 3 | size 16512 4 | -------------------------------------------------------------------------------- /models/visual_wake_words/micronet_vww4/tflite_int8/testing_output/Identity/0.npy: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:b178447c66875181eb7835c46eb9fd8e73069e0c8a12f5ed9529c7bb2c9f25ea 3 | size 130 4 | -------------------------------------------------------------------------------- /models/visual_wake_words/micronet_vww4/tflite_int8/vww4_128_128_INT8.tflite: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:5e76364e80c45776b735563679d45f611cab7ce7fef2ec4e2db088afe009ccae 3 | size 490336 4 | -------------------------------------------------------------------------------- /tutorials/transformer_tutorials/README.md: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /tutorials/transformer_tutorials/install.sh: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /tutorials/transformer_tutorials/requirements.txt: -------------------------------------------------------------------------------- 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 | --------------------------------------------------------------------------------