diff --git a/datasets/samplemotordatalimerick.py b/datasets/samplemotordatalimerick.py
new file mode 100755
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+++ b/datasets/samplemotordatalimerick.py
@@ -0,0 +1,670 @@
+###################################################################################################
+#
+# Copyright (C) 2024 Analog Devices, Inc. All Rights Reserved.
+# This software is proprietary to Analog Devices, Inc. and its licensors.
+#
+###################################################################################################
+"""
+Classes and functions for the Sample Motor Data Limerick Dataset
+https://github.com/analogdevicesinc/CbM-Datasets
+"""
+import errno
+import math
+import os
+import pickle
+
+import numpy as np
+import torch
+from numpy.fft import fft
+from torch.utils.data import Dataset
+from torchvision import transforms
+
+import git
+import pandas as pd
+import scipy
+from git.exc import GitCommandError
+
+import ai8x
+
+
+class SampleMotorDataLimerick(Dataset):
+    """
+    Sample motor data is collected using SpectraQuest Machinery Fault Simulator.
+    ADXL356 sensor data is used for vibration raw data.
+    For ADXL356 sensor, the sampling frequency was 20kHz and
+    data csv files recorded for 2 sec in X, Y and Z direction.
+    """
+
+    sensor_sr_Hz = 20000
+
+    # Good Bearing, Good Shaft, Balanced Load and Well Aligned
+    healthy_file_identifier = '_GoB_GS_BaLo_WA_'
+
+    cnn_1dinput_len = 256
+
+    num_end_zeros = 10
+    num_start_zeros = 3
+
+    common_dataframe_columns = ["sensor_identifier", "file_identifier", "raw_data_accel_in_g"]
+
+    train_ratio = 0.8
+
+    @staticmethod
+    def sliding_windows_1d(array, window_size, overlap_ratio):
+        """
+        One dimensional array is windowed and returned
+        in window_size length according to overlap ratio.
+        """
+
+        window_overlap = math.ceil(window_size * overlap_ratio)
+
+        slide_amount = window_size - window_overlap
+        num_of_windows = math.floor((len(array) - window_size) / slide_amount) + 1
+
+        result_list = np.zeros((num_of_windows, window_size))
+
+        for i in range(num_of_windows):
+            start_idx = slide_amount * i
+            end_idx = start_idx + window_size
+            result_list[i] = array[start_idx:end_idx]
+
+        return result_list
+
+    @staticmethod
+    def sliding_windows_on_columns_of_2d(array, window_size, overlap_ratio):
+        """
+        Two dimensional array is windowed and returned
+        in window_size length according to overlap ratio.
+        """
+
+        array_len, num_of_cols = array.shape
+
+        window_overlap = math.ceil(window_size * overlap_ratio)
+        slide_amount = window_size - window_overlap
+        num_of_windows = math.floor((array_len - window_size) / slide_amount) + 1
+
+        result_list = np.zeros((num_of_cols, num_of_windows, window_size))
+
+        for i in range(num_of_cols):
+            result_list[i, :, :] = SampleMotorDataLimerick.sliding_windows_1d(
+                array[:, i],
+                window_size, overlap_ratio
+                )
+
+        return result_list
+
+    @staticmethod
+    def split_file_raw_data(file_raw_data, file_raw_data_fs_in_Hz, duration_in_sec, overlap_ratio):
+        """
+        Raw data is split into windowed data.
+        """
+
+        num_of_samples_per_window = int(file_raw_data_fs_in_Hz * duration_in_sec)
+
+        sliding_windows = SampleMotorDataLimerick.sliding_windows_on_columns_of_2d(
+            file_raw_data,
+            num_of_samples_per_window,
+            overlap_ratio
+            )
+
+        return sliding_windows
+
+    def process_file_and_return_signal_windows(self, file_raw_data):
+        """
+        Windowed signals are constructed from 2D raw data.
+        Fast Fourier Transform performed on these signals.
+        """
+
+        new_sampling_rate = int(self.selected_sensor_sr / self.downsampling_ratio)
+
+        file_raw_data_sampled = scipy.signal.decimate(file_raw_data,
+                                                      self.downsampling_ratio, axis=0)
+
+        file_raw_data_windows = SampleMotorDataLimerick.split_file_raw_data(
+            file_raw_data_sampled,
+            new_sampling_rate,
+            self.signal_duration_in_sec,
+            self.overlap_ratio
+            )
+
+        # First dimension: 3
+        # Second dimension: number of windows
+        # Third dimension: Window for self.duration_in_sec. 1000 samples for default settings
+        num_features = file_raw_data_windows.shape[0]
+        num_windows = file_raw_data_windows.shape[1]
+
+        fft_output_window_size = SampleMotorDataLimerick.cnn_1dinput_len
+
+        file_cnn_signals = np.zeros((num_features, num_windows, fft_output_window_size))
+
+        # Perform FFT on each window () for each feature
+        for window in range(num_windows):
+            for feature in range(num_features):
+
+                signal_for_fft = file_raw_data_windows[feature, window, :]
+
+                fft_out = abs(fft(signal_for_fft))
+                fft_out = fft_out[:fft_output_window_size]
+
+                fft_out[:SampleMotorDataLimerick.num_start_zeros] = 0
+                fft_out[-SampleMotorDataLimerick.num_end_zeros:] = 0
+
+                file_cnn_signals[feature, window, :] = fft_out
+
+            file_cnn_signals[:, window, :] = file_cnn_signals[:, window, :] / \
+                np.sqrt(np.power(file_cnn_signals[:, window, :], 2).sum())
+
+        # Reshape from (num_features, num_windows, window_size) into:
+        # (num_windows, num_features, window_size)
+        file_cnn_signals = file_cnn_signals.transpose([1, 0, 2])
+
+        return file_cnn_signals
+
+    @staticmethod
+    def create_common_empty_df():
+        """
+        Create empty dataframe
+        """
+        df = pd.DataFrame(columns=SampleMotorDataLimerick.common_dataframe_columns)
+        return df
+
+    @staticmethod
+    def parse_ADXL356C_and_return_common_df_row(file_full_path):
+        """
+        Columns added just for readability can return raw data np array as well,
+        can also add file identifier
+        """
+        df_raw = pd.read_csv(file_full_path, sep=';', header=None)
+        df_raw.rename(
+            columns={0: 'Time', 1: 'Voltage_x', 2: 'Voltage_y',
+                     3: 'Voltage_z', 4: 'x', 5: 'y', 6: 'z'},
+            inplace=True
+            )
+        ss_vibr_x1 = df_raw.iloc[0]['x']
+        ss_vibr_y1 = df_raw.iloc[0]['y']
+        ss_vibr_z1 = df_raw.iloc[0]['z']
+        df_raw["Acceleration_x (g)"] = 50 * (df_raw["Voltage_x"] - ss_vibr_x1)
+        df_raw["Acceleration_y (g)"] = 50 * (df_raw["Voltage_y"] - ss_vibr_y1)
+        df_raw["Acceleration_z (g)"] = 50 * (df_raw["Voltage_z"] - ss_vibr_z1)
+
+        raw_data = df_raw[["Acceleration_x (g)", "Acceleration_y (g)", "Acceleration_z (g)"]]
+        raw_data = raw_data.to_numpy()
+        return ['ADXL356C', os.path.basename(file_full_path).split('/')[-1], raw_data]
+
+    def __makedir_exist_ok(self, dirpath):
+        try:
+            os.makedirs(dirpath)
+        except OSError as e:
+            if e.errno == errno.EEXIST:
+                pass
+            else:
+                raise
+
+    def __init__(self, root, d_type, transform=None,
+                 download=True,
+                 downsampling_ratio=2,
+                 signal_duration_in_sec=0.25,
+                 overlap_ratio=0.75,
+                 eval_mode=False,
+                 label_as_signal=True,
+                 random_or_speed_split=True,
+                 accel_in_second_dim=True):
+
+        if d_type not in ('test', 'train'):
+            raise ValueError(
+                "d_type can only be set to 'test' or 'train'"
+                )
+
+        if not isinstance(downsampling_ratio, int) or downsampling_ratio < 1:
+            raise ValueError(
+                "downsampling_ratio can only be set to an integer value greater than 0"
+                )
+
+        self.selected_sensor_sr = SampleMotorDataLimerick.sensor_sr_Hz
+
+        self.root = root
+        self.d_type = d_type
+        self.transform = transform
+        self.download = download
+
+        self.downsampling_ratio = downsampling_ratio
+        self.signal_duration_in_sec = signal_duration_in_sec
+        self.overlap_ratio = overlap_ratio
+
+        self.eval_mode = eval_mode
+        self.label_as_signal = label_as_signal
+
+        self.random_or_speed_split = random_or_speed_split
+        self.accel_in_second_dim = accel_in_second_dim
+
+        self.num_of_features = 3
+
+        if self.download:
+            self.__download()
+
+        processed_folder = \
+            os.path.join(root, self.__class__.__name__, 'processed')
+
+        self.__makedir_exist_ok(processed_folder)
+
+        self.specs_identifier = f'eval_mode_{self.eval_mode}_' + \
+                                f'label_as_signal_{self.label_as_signal}_' + \
+                                f'ds_{self.downsampling_ratio}_' + \
+                                f'dur_{self.signal_duration_in_sec}_' + \
+                                f'ovlp_ratio_{self.overlap_ratio}_' + \
+                                f'random_split_{self.random_or_speed_split}_'
+
+        train_dataset_pkl_file_path = \
+            os.path.join(processed_folder, f'train_{self.specs_identifier}.pkl')
+
+        test_dataset_pkl_file_path =  \
+            os.path.join(processed_folder, f'test_{self.specs_identifier}.pkl')
+
+        if self.d_type == 'train':
+            self.dataset_pkl_file_path = train_dataset_pkl_file_path
+
+        elif self.d_type == 'test':
+            self.dataset_pkl_file_path = test_dataset_pkl_file_path
+
+        self.signal_list = []
+        self.lbl_list = []
+
+        self.__create_pkl_files()
+        self.is_truncated = False
+
+    def __download(self):
+        """
+        Downloads Sample Motor Data Limerick dataset from:
+        https://github.com/analogdevicesinc/CbM-Datasets
+        """
+        destination_folder = self.root
+        dataset_repository = 'https://github.com/analogdevicesinc/CbM-Datasets'
+
+        self.__makedir_exist_ok(destination_folder)
+
+        try:
+            if not os.path.exists(os.path.join(destination_folder, 'SampleMotorDataLimerick')):
+                print('\nDownloading SampleMotorDataLimerick dataset from\n'
+                      f'{dataset_repository}\n')
+                git.Repo.clone_from(dataset_repository, destination_folder)
+
+            else:
+                print('\nSampleMotorDataLimerick dataset already downloaded...')
+
+        except GitCommandError:
+            pass
+
+    def __create_pkl_files(self):
+        if os.path.exists(self.dataset_pkl_file_path):
+
+            print('\nPickle files are already generated ...\n')
+
+            (self.signal_list, self.lbl_list) = \
+                pickle.load(open(self.dataset_pkl_file_path, 'rb'))
+            return
+
+        self.__gen_datasets()
+
+    def normalize_signal(self, features):
+        """
+        Normalize signal with Local Min Max Normalization
+        """
+        # Normalize data:
+        for instance in range(features.shape[0]):
+            instance_max = np.max(features[instance, :, :], axis=1)
+            instance_min = np.min(features[instance, :, :], axis=1)
+
+            for feature in range(features.shape[1]):
+                for signal in range(features.shape[2]):
+                    features[instance, feature, signal] = (
+                        (features[instance, feature, signal] - instance_min[feature]) /
+                        (instance_max[feature] - instance_min[feature])
+                    )
+
+        return features
+
+    def __gen_datasets(self):
+        print('\nGenerating dataset pickle files from the raw data '
+              f'files (specs identifier: {self.specs_identifier}) ...\n')
+
+        actual_root_dir = os.path.join(self.root, self.__class__.__name__,
+                                       "SpectraQuest_Rig_Data_Voyager_3/")
+
+        data_dir = os.path.join(actual_root_dir, 'Data_ADXL356C')
+
+        if not os.listdir(data_dir):
+            print('\nDataset directory is empty.\n')
+
+        rpm_prefixes = ('0600', '1200', '1800', '2400', '3000')
+
+        faulty_data_list = []
+        healthy_data_list = []
+
+        df_normals = SampleMotorDataLimerick.create_common_empty_df()
+        df_anormals = SampleMotorDataLimerick.create_common_empty_df()
+
+        for file in os.listdir(data_dir):
+            full_path = os.path.join(data_dir, file)
+
+            if any(file.startswith(rpm_prefix + SampleMotorDataLimerick.healthy_file_identifier)
+                   for rpm_prefix in rpm_prefixes):
+                healthy_row = SampleMotorDataLimerick.parse_ADXL356C_and_return_common_df_row(
+                    file_full_path=full_path
+                )
+
+                healthy_data_list.append(healthy_row)
+
+            else:
+                faulty_row = SampleMotorDataLimerick.parse_ADXL356C_and_return_common_df_row(
+                    file_full_path=full_path
+                    )
+
+                faulty_data_list.append(faulty_row)
+
+        # Can keep and process those further
+        df_normals = pd.DataFrame(data=np.array(healthy_data_list, dtype=object),
+                                  columns=SampleMotorDataLimerick.common_dataframe_columns)
+        df_anormals = pd.DataFrame(data=np.array(faulty_data_list, dtype=object),
+                                   columns=SampleMotorDataLimerick.common_dataframe_columns)
+
+        # LOAD NORMAL FEATURES
+        test_train_idx_max = 4
+        test_train_idx = 0  # 0, 1, 2 : train, 3: test
+
+        train_features = []
+        test_normal_features = []
+
+        for _, row in df_normals.iterrows():
+            raw_data = row['raw_data_accel_in_g']
+            cnn_signals = self.process_file_and_return_signal_windows(raw_data)
+            if self.random_or_speed_split:
+                num_training = int(SampleMotorDataLimerick.train_ratio * cnn_signals.shape[0])
+
+                for i in range(cnn_signals.shape[0]):
+                    if i < num_training:
+                        train_features.append(cnn_signals[i])
+                    else:
+                        test_normal_features.append(cnn_signals[i])
+            else:
+                if test_train_idx < test_train_idx_max - 1:
+                    for i in range(cnn_signals.shape[0]):
+                        train_features.append(cnn_signals[i])
+                else:
+                    for i in range(cnn_signals.shape[0]):
+                        test_normal_features.append(cnn_signals[i])
+
+                test_train_idx = (test_train_idx + 1) % test_train_idx_max
+
+        train_features = np.asarray(train_features)
+        test_normal_features = np.asarray(test_normal_features)
+
+        anomaly_features = []
+
+        for _, row in df_anormals.iterrows():
+            raw_data = row['raw_data_accel_in_g']
+            cnn_signals = self.process_file_and_return_signal_windows(raw_data)
+            for i in range(cnn_signals.shape[0]):
+                anomaly_features.append(cnn_signals[i])
+
+        anomaly_features = np.asarray(anomaly_features)
+
+        train_features = self.normalize_signal(train_features)
+        test_normal_features = self.normalize_signal(test_normal_features)
+        anomaly_features = self.normalize_signal(anomaly_features)
+
+        # For eliminating filter effects
+        train_features[:, :, :SampleMotorDataLimerick.num_start_zeros] = 0.5
+        train_features[:, :, -SampleMotorDataLimerick.num_end_zeros:] = 0.5
+
+        test_normal_features[:, :, :SampleMotorDataLimerick.num_start_zeros] = 0.5
+        test_normal_features[:, :, -SampleMotorDataLimerick.num_end_zeros:] = 0.5
+
+        anomaly_features[:, :, :SampleMotorDataLimerick.num_start_zeros] = 0.5
+        anomaly_features[:, :, -SampleMotorDataLimerick.num_end_zeros:] = 0.5
+
+        # ARRANGE TEST-TRAIN SPLIT AND LABELS
+        if self.d_type == 'train':
+            self.lbl_list = [train_features[i, :, :] for i in range(train_features.shape[0])]
+            self.signal_list = [torch.Tensor(label) for label in self.lbl_list]
+            self.lbl_list = list(self.signal_list)
+
+            if not self.label_as_signal:
+                self.lbl_list = np.zeros([len(self.signal_list), 1])
+
+        elif self.d_type == 'test':
+
+            # Testing in training phase includes only normal test samples
+            if not self.eval_mode:
+                test_data = test_normal_features
+            else:
+                test_data = np.concatenate((test_normal_features, anomaly_features), axis=0)
+
+            self.lbl_list = [test_data[i, :, :] for i in range(test_data.shape[0])]
+            self.signal_list = [torch.Tensor(label) for label in self.lbl_list]
+            self.lbl_list = list(self.signal_list)
+
+            if not self.label_as_signal:
+                self.lbl_list = np.concatenate(
+                                    (np.zeros([len(test_normal_features), 1]),
+                                     np.ones([len(anomaly_features), 1])), axis=0)
+        # Save pickle file
+        pickle.dump((self.signal_list, self.lbl_list), open(self.dataset_pkl_file_path, 'wb'))
+
+    def __len__(self):
+        if self.is_truncated:
+            return 1
+        return len(self.signal_list)
+
+    def __getitem__(self, index):
+        if index >= len(self):
+            raise IndexError
+
+        if self.is_truncated:
+            index = 0
+
+        signal = self.signal_list[index]
+        lbl = self.lbl_list[index]
+
+        if self.transform is not None:
+            signal = self.transform(signal)
+
+            if self.label_as_signal:
+                lbl = self.transform(lbl)
+
+        if not self.label_as_signal:
+            lbl = lbl.astype(np.long)
+        else:
+            lbl = lbl.numpy().astype(np.float32)
+
+        if self.accel_in_second_dim:
+            signal = torch.transpose(signal, 0, 1)
+            lbl = lbl.transpose()
+
+        return signal, lbl
+
+
+def samplemotordatalimerick_get_datasets(data, load_train=True, load_test=True,
+                                         download=True,
+                                         downsampling_ratio=10,
+                                         signal_duration_in_sec=0.25,
+                                         overlap_ratio=0.75,
+                                         eval_mode=False,
+                                         label_as_signal=True,
+                                         random_or_speed_split=True,
+                                         accel_in_second_dim=True):
+    """"
+    Returns Sample Motor Data Limerick Dataset
+    """
+    (data_dir, args) = data
+
+    if load_train:
+        train_transform = transforms.Compose([
+            ai8x.normalize(args=args)
+        ])
+
+        train_dataset = SampleMotorDataLimerick(root=data_dir, d_type='train',
+                                                download=download,
+                                                transform=train_transform,
+                                                downsampling_ratio=downsampling_ratio,
+                                                signal_duration_in_sec=signal_duration_in_sec,
+                                                overlap_ratio=overlap_ratio,
+                                                eval_mode=eval_mode,
+                                                label_as_signal=label_as_signal,
+                                                random_or_speed_split=random_or_speed_split,
+                                                accel_in_second_dim=accel_in_second_dim)
+
+        print(f'Train dataset length: {len(train_dataset)}\n')
+    else:
+        train_dataset = None
+
+    if load_test:
+        test_transform = transforms.Compose([
+            ai8x.normalize(args=args)
+        ])
+
+        test_dataset = SampleMotorDataLimerick(root=data_dir, d_type='test',
+                                               download=download,
+                                               transform=test_transform,
+                                               downsampling_ratio=downsampling_ratio,
+                                               signal_duration_in_sec=signal_duration_in_sec,
+                                               overlap_ratio=overlap_ratio,
+                                               eval_mode=eval_mode,
+                                               label_as_signal=label_as_signal,
+                                               random_or_speed_split=random_or_speed_split,
+                                               accel_in_second_dim=accel_in_second_dim)
+
+        print(f'Test dataset length: {len(test_dataset)}\n')
+    else:
+        test_dataset = None
+
+    return train_dataset, test_dataset
+
+
+def samplemotordatalimerick_get_datasets_for_train(data,
+                                                   load_train=True,
+                                                   load_test=True):
+    """"
+    Returns Sample Motor Data Limerick Dataset For Training Mode
+    """
+
+    eval_mode = False   # Test set includes validation normals
+    label_as_signal = True
+
+    signal_duration_in_sec = 0.25
+    overlap_ratio = 0.75
+
+    wanted_sampling_rate_Hz = 2000
+    downsampling_ratio = round(SampleMotorDataLimerick.sensor_sr_Hz /
+                               wanted_sampling_rate_Hz)
+
+    # ds_ratio = 10,  sr: 20K / 10 = 2000, 0.25 sec window, fft input will have: 500 samples,
+    # fftout's first 256 samples will be used
+    # cnn input will have 256 samples
+
+    accel_in_second_dim = True
+
+    random_or_speed_split = True
+
+    return samplemotordatalimerick_get_datasets(data, load_train, load_test,
+                                                downsampling_ratio=downsampling_ratio,
+                                                signal_duration_in_sec=signal_duration_in_sec,
+                                                overlap_ratio=overlap_ratio,
+                                                eval_mode=eval_mode,
+                                                label_as_signal=label_as_signal,
+                                                random_or_speed_split=random_or_speed_split,
+                                                accel_in_second_dim=accel_in_second_dim)
+
+
+def samplemotordatalimerick_get_datasets_for_eval_with_anomaly_label(data,
+                                                                     load_train=True,
+                                                                     load_test=True):
+    """"
+    Returns Sample Motor Data Limerick Dataset For Evaluation Mode
+    Label is anomaly status
+    """
+
+    eval_mode = True   # Test set includes validation normals
+    label_as_signal = False
+
+    signal_duration_in_sec = 0.25
+    overlap_ratio = 0.75
+
+    wanted_sampling_rate_Hz = 2000
+    downsampling_ratio = round(SampleMotorDataLimerick.sensor_sr_Hz /
+                               wanted_sampling_rate_Hz)
+
+    # ds_ratio = 10,  sr: 20K / 10 = 2000, 0.25 sec window, fft input will have: 500 samples,
+    # fftout's first 256 samples will be used
+    # cnn input will have 2556 samples
+
+    accel_in_second_dim = True
+
+    random_or_speed_split = True
+
+    return samplemotordatalimerick_get_datasets(data, load_train, load_test,
+                                                downsampling_ratio=downsampling_ratio,
+                                                signal_duration_in_sec=signal_duration_in_sec,
+                                                overlap_ratio=overlap_ratio,
+                                                eval_mode=eval_mode,
+                                                label_as_signal=label_as_signal,
+                                                random_or_speed_split=random_or_speed_split,
+                                                accel_in_second_dim=accel_in_second_dim)
+
+
+def samplemotordatalimerick_get_datasets_for_eval_with_signal(data,
+                                                              load_train=True,
+                                                              load_test=True):
+    """"
+    Returns Sample Motor Data Limerick Dataset For Evaluation Mode
+    Label is signal
+    """
+
+    eval_mode = True   # Test set includes anormal samples as well as validation normals
+    label_as_signal = True
+
+    signal_duration_in_sec = 0.25
+    overlap_ratio = 0.75
+
+    wanted_sampling_rate_Hz = 2000
+    downsampling_ratio = round(SampleMotorDataLimerick.sensor_sr_Hz /
+                               wanted_sampling_rate_Hz)
+
+    # ds_ratio = 10,  sr: 20K / 10 = 2000, 0.25 sec window, fft input will have: 500 samples,
+    # fftout's first 256 samples will be used
+    # cnn input will have 2556 samples
+
+    accel_in_second_dim = True
+
+    random_or_speed_split = True
+
+    return samplemotordatalimerick_get_datasets(data, load_train, load_test,
+                                                downsampling_ratio=downsampling_ratio,
+                                                signal_duration_in_sec=signal_duration_in_sec,
+                                                overlap_ratio=overlap_ratio,
+                                                eval_mode=eval_mode,
+                                                label_as_signal=label_as_signal,
+                                                random_or_speed_split=random_or_speed_split,
+                                                accel_in_second_dim=accel_in_second_dim)
+
+
+datasets = [
+    {
+        'name': 'SampleMotorDataLimerick_ForTrain',
+        'input': (256, 3),
+        'output': ('signal'),
+        'regression': True,
+        'loader': samplemotordatalimerick_get_datasets_for_train,
+    },
+    {
+        'name': 'SampleMotorDataLimerick_ForEvalWithAnomalyLabel',
+        'input': (256, 3),
+        'output': ('normal', 'anomaly'),
+        'loader': samplemotordatalimerick_get_datasets_for_eval_with_anomaly_label,
+    },
+    {
+        'name': 'SampleMotorDataLimerick_ForEvalWithSignal',
+        'input': (256, 3),
+        'output': ('signal'),
+        'loader': samplemotordatalimerick_get_datasets_for_eval_with_signal,
+    }
+]
diff --git a/models/ai85net-autoencoder.py b/models/ai85net-autoencoder.py
new file mode 100755
index 000000000..caa316d8c
--- /dev/null
+++ b/models/ai85net-autoencoder.py
@@ -0,0 +1,180 @@
+###################################################################################################
+#
+# Copyright (C) 2024 Analog Devices, Inc. All Rights Reserved.
+# This software is proprietary to Analog Devices, Inc. and its licensors.
+#
+###################################################################################################
+"""
+Auto Encoder Network
+"""
+
+from torch import nn
+
+import ai8x
+
+
+class CNN_BASE(nn.Module):
+    """
+    Auto Encoder Network
+    """
+    def __init__(self,
+                 num_channels=3,  # pylint: disable=unused-argument
+                 bias=True,  # pylint: disable=unused-argument
+                 weight_init="kaiming",  # pylint: disable=unused-argument
+                 num_classes=0,  # pylint: disable=unused-argument
+                 **kwargs):  # pylint: disable=unused-argument
+        super().__init__()
+
+    def initWeights(self, weight_init="kaiming"):
+        """
+        Auto Encoder Weight Initialization
+        """
+        weight_init = weight_init.lower()
+        assert weight_init in ('kaiming', 'xavier', 'glorot')
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                if weight_init == "kaiming":
+                    nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+
+                elif weight_init in ('glorot', 'xavier'):
+                    nn.init.xavier_uniform_(m.weight)
+
+            elif isinstance(m, nn.ConvTranspose2d):
+                if weight_init == "kaiming":
+                    nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+
+                elif weight_init in ('glorot', 'xavier'):
+                    nn.init.xavier_uniform_(m.weight)
+
+            elif isinstance(m, nn.Linear):
+                if weight_init == "kaiming":
+                    nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+
+                elif weight_init in ('glorot', 'xavier'):
+                    nn.init.xavier_uniform_(m.weight)
+
+
+class AI85AutoEncoder(CNN_BASE):
+    """
+    Neural Network that has depthwise convolutions to reduce input dimensions.
+    Filters work across individual axis data first.
+    Output of 1D Conv layer is then flattened before being fed to fully connected layers
+    Fully connected layers down sample the data to a bottleneck. This completes the encoder.
+    The decoder is then the same in reverse
+
+    Input Shape: [BATCH_SZ, FFT_LEN, N_AXES] -> [BATCH_SZ, 256, 3] = [N, N_CHANNELS, SIGNAL_LEN]
+    """
+
+    def __init__(self,
+                 num_channels=256,
+                 dimensions=None,  # pylint: disable=unused-argument
+                 num_classes=1,  # pylint: disable=unused-argument
+                 n_axes=3,
+                 bias=True,
+                 weight_init="kaiming",
+                 batchNorm=True,
+                 bottleNeckDim=4,
+                 **kwargs):
+
+        super().__init__()
+
+        print("Batchnorm setting in model = ", batchNorm)
+
+        weight_init = weight_init.lower()
+        assert weight_init in ('kaiming', 'xavier', 'glorot')
+
+        # Num channels is equal to the length of FFTs here
+        self.num_channels = num_channels
+        self.n_axes = n_axes
+
+        S = 1
+        P = 0
+
+        # ----- DECODER ----- #
+        # Kernel in 1st layer looks at 1 axis at a time. Output width = input width
+        n_in = num_channels
+        n_out = 128
+        if batchNorm:
+            self.en_conv1 = ai8x.FusedConv1dBNReLU(n_in, n_out, 1, stride=S, padding=P, dilation=1,
+                                                   bias=bias, batchnorm='Affine', **kwargs)
+        else:
+            self.en_conv1 = ai8x.FusedConv1dReLU(n_in, n_out, 1, stride=S, padding=P, dilation=1,
+                                                 bias=bias, **kwargs)
+        self.layer1_n_in = n_in
+        self.layer1_n_out = n_out
+
+        # Kernel in 2nd layer looks at 3 axes at once. Output Width = 1. Depth=n_out
+        n_in = n_out
+        n_out = 64
+        if batchNorm:
+            self.en_conv2 = ai8x.FusedConv1dBNReLU(n_in, n_out, 3, stride=S, padding=P, dilation=1,
+                                                   bias=bias, batchnorm='Affine', **kwargs)
+        else:
+            self.en_conv2 = ai8x.FusedConv1dReLU(n_in, n_out, 3, stride=S, padding=P, dilation=1,
+                                                 bias=bias, **kwargs)
+        self.layer2_n_in = n_in
+        self.layer2_n_out = n_out
+
+        n_in = n_out
+        n_out = 32
+        self.en_lin1 = ai8x.FusedLinearReLU(n_in, n_out, bias=bias, **kwargs)
+        # ----- END OF DECODER ----- #
+
+        # ---- BOTTLENECK ---- #
+        n_in = n_out
+        self.bottleNeckDim = bottleNeckDim
+        n_out = self.bottleNeckDim
+        self.en_lin2 = ai8x.Linear(n_in, n_out, bias=0, **kwargs)
+        # ---- END OF BOTTLENECK ---- #
+
+        # ----- ENCODER ----- #
+        n_in = n_out
+        n_out = 32
+        self.de_lin1 = ai8x.FusedLinearReLU(n_in, n_out, bias=bias, **kwargs)
+
+        n_in = n_out
+        n_out = 96
+        self.de_lin2 = ai8x.FusedLinearReLU(n_in, n_out, bias=bias, **kwargs)
+
+        n_in = n_out
+        n_out = num_channels*n_axes
+        self.out_lin = ai8x.Linear(n_in, n_out, bias=0, **kwargs)
+        # ----- END OF ENCODER ----- #
+
+        self.initWeights(weight_init)
+
+    def forward(self, x, return_bottleneck=False):
+        """Forward prop"""
+        x = self.en_conv1(x)
+        x = self.en_conv2(x)
+        x = x.view(x.shape[0], x.shape[1])
+        x = self.en_lin1(x)
+        x = self.en_lin2(x)
+
+        if return_bottleneck:
+            return x
+
+        x = self.de_lin1(x)
+        x = self.de_lin2(x)
+        x = self.out_lin(x)
+        x = x.view(x.shape[0], self.num_channels, self.n_axes)
+
+        return x
+
+
+def ai85autoencoder(pretrained=False, **kwargs):
+    """
+    Constructs an Autoencoder model
+    """
+    assert not pretrained
+    return AI85AutoEncoder(**kwargs)
+
+
+models = [
+    {
+        'name': 'ai85autoencoder',
+        'min_input': 1,
+        'dim': 1,
+    }
+]
diff --git a/notebooks/AutoEncoder_Evaluation.ipynb b/notebooks/AutoEncoder_Evaluation.ipynb
new file mode 100755
index 000000000..cda423032
--- /dev/null
+++ b/notebooks/AutoEncoder_Evaluation.ipynb
@@ -0,0 +1,575 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "###################################################################################################\n",
+    "#\n",
+    "# Copyright (C) 2023 Analog Devices, Inc. All Rights Reserved.\n",
+    "# This software is proprietary and confidential to Analog Devices, Inc. and its licensors.\n",
+    "#\n",
+    "###################################################################################################\n",
+    "\"\"\"\n",
+    "For more details about the dataset, data loader, model and training,\n",
+    "please see the following documentation:\n",
+    "https://github.com/MaximIntegratedAI/MaximAI_Documentation/blob/master/Guides/MAX78000%20Motor%20Monitoring%20Case%20Study%20with%20SampleMotorDataLimerick%20Dataset.pdf\n",
+    "\"\"\"\n",
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "import math\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "\n",
+    "import importlib\n",
+    "\n",
+    "import matplotlib.patches as patches\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "sys.path.append(os.path.dirname(os.getcwd()))\n",
+    "sys.path.append(os.path.join(os.path.dirname(os.getcwd()), 'models'))\n",
+    "\n",
+    "from datasets import samplemotordatalimerick\n",
+    "\n",
+    "ai85net_autoencoder = __import__(\"ai85net-autoencoder\")\n",
+    "\n",
+    "import parse_qat_yaml\n",
+    "import ai8x\n",
+    "from torch import nn\n",
+    "\n",
+    "from torch.utils import data\n",
+    "\n",
+    "from distiller import apputils\n",
+    "\n",
+    "import seaborn as sns\n",
+    "\n",
+    "from statistics import mean\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from utils import autoencoder_eval_utils as utilsV5\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "directory = os.getcwd()\n",
+    "training_dir = os.path.abspath(os.path.join(directory, os.pardir))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Configuring device: MAX78000, simulate=False.\n"
+     ]
+    }
+   ],
+   "source": [
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "data_path = os.path.join(training_dir, 'data')\n",
+    "simulate = False\n",
+    "\n",
+    "class Args:\n",
+    "    def __init__(self, act_mode_8bit):\n",
+    "        self.act_mode_8bit = act_mode_8bit\n",
+    "        self.truncate_testset = False\n",
+    "\n",
+    "args = Args(act_mode_8bit=simulate)\n",
+    "\n",
+    "ai8x.set_device(device=85, simulate=simulate, round_avg=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "SampleMotorDataLimerick dataset already downloaded...\n",
+      "\n",
+      "Pickle files are already generated ...\n",
+      "\n",
+      "Train dataset length: 230\n",
+      "\n",
+      "\n",
+      "SampleMotorDataLimerick dataset already downloaded...\n",
+      "\n",
+      "Pickle files are already generated ...\n",
+      "\n",
+      "Test dataset length: 3540\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Generate Dataset For Evaluation\n",
+    "train_set, test_set = samplemotordatalimerick.samplemotordatalimerick_get_datasets_for_eval_with_anomaly_label((data_path, args), load_train=True, load_test=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 32\n",
+    "train_dataloader = data.DataLoader(train_set, batch_size=batch_size, shuffle=True)\n",
+    "test_dataloader = data.DataLoader(test_set, batch_size=batch_size, shuffle=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Load Trained AE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Batchnorm setting in model =  True\n",
+      "\n",
+      "Number of Model Weights: 136640\n",
+      "Number of Model Bias: 544\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = ai85net_autoencoder.ai85autoencoder()\n",
+    "_ = utilsV5.calc_model_size(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Change this checkpoint file path with your own trained one\n",
+    "checkpoint_path = os.path.abspath(os.path.join(training_dir, os.pardir, 'ai8x-synthesis', 'trained', 'ai85-autoencoder-samplemotordatalimerick-qat.pth.tar'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'start_epoch': 200, 'weight_bits': 8, 'shift_quantile': 0.995}\n",
+      "Configuring device: MAX78000, simulate=False.\n"
+     ]
+    }
+   ],
+   "source": [
+    "qat_yaml_file_used_in_training = os.path.join(training_dir, 'policies', 'qat_policy_autoencoder.yaml')\n",
+    "\n",
+    "qat_policy = parse_qat_yaml.parse(qat_yaml_file_used_in_training)\n",
+    "\n",
+    "ai8x.set_device(85, simulate, False)\n",
+    "\n",
+    "# Fuse the BN parameters into conv layers before Quantization Aware Training (QAT)\n",
+    "ai8x.fuse_bn_layers(model)\n",
+    "\n",
+    "# Switch model from unquantized to quantized for QAT\n",
+    "ai8x.initiate_qat(model, qat_policy)\n",
+    "\n",
+    "model = apputils.load_lean_checkpoint(model, checkpoint_path, model_device=device)\n",
+    "ai8x.update_model(model)\n",
+    "\n",
+    "model = model.to(device)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Visualizing whether trained model has good separation the latent space"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "230\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_base_tuple = utilsV5.extract_reconstructions_losses(model, train_dataloader, device)\n",
+    "train_base_reconstructions, train_base_losses, train_base_inputs, train_base_labels = \\\n",
+    "    train_base_tuple\n",
+    "    \n",
+    "    \n",
+    "print(len(train_base_losses))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3540\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_base_tuple = utilsV5.extract_reconstructions_losses(model, test_dataloader, device)\n",
+    "test_base_reconstructions, test_base_losses, test_base_inputs, test_base_labels = \\\n",
+    "    test_base_tuple\n",
+    "    \n",
+    "print(len(test_base_losses))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.08606820548770566"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean(test_base_losses)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "60\n"
+     ]
+    }
+   ],
+   "source": [
+    "normal_test_sample_idxs = [test_item_idx for test_item_idx, test_item in enumerate(test_set) if test_item[1] == 0]\n",
+    "normal_test_samples = torch.utils.data.Subset(test_set, normal_test_sample_idxs)\n",
+    "normal_test_samples_loader = torch.utils.data.DataLoader(normal_test_samples, batch_size=batch_size)\n",
+    "print(len(normal_test_sample_idxs))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.027154843012491863, 0.01931905746459961, 0.022384484608968098, 0.027895212173461914, 0.02215234438578288, 0.02309735616048177, 0.01860976219177246, 0.0230864683787028, 0.022903998692830402, 0.02098989486694336, 0.017391522725423176, 0.02986884117126465, 0.025111277898152668, 0.026648998260498047, 0.0289913813273112, 0.028342485427856445, 0.02594900131225586, 0.023530244827270508, 0.02023140589396159, 0.023876508076985676, 0.025062402089436848, 0.03147419293721517, 0.026927073796590168, 0.02293237050374349, 0.02016894022623698, 0.03138017654418945, 0.029456456502278645, 0.02879174550374349, 0.03609673182169596, 0.042246739069620766, 0.011744022369384766, 0.014452854792277018, 0.01569652557373047, 0.0213166077931722, 0.024822314580281574, 0.02770860989888509, 0.0341958204905192, 0.02867404619852702, 0.026907602945963543, 0.025069236755371094, 0.018611669540405273, 0.021029233932495117, 0.010935386021931967, 0.012288649876912435, 0.01129762331644694, 0.011835495630900065, 0.011105537414550781, 0.014148712158203125, 0.018083969751993816, 0.017012675603230793, 0.017467419306437176, 0.021580616633097332, 0.02849753697713216, 0.025730212529500324, 0.01306001345316569, 0.01197377840677897, 0.0089109738667806, 0.011348247528076172, 0.01638944943745931, 0.022724707921346027]\n"
+     ]
+    }
+   ],
+   "source": [
+    "normal_base_output = utilsV5.extract_reconstructions_losses(model, normal_test_samples_loader, device)\n",
+    "    \n",
+    "test_base_normal_reconstructions, test_base_normal_losses, \\\n",
+    "test_base_normal_inputs, test_base_normal_labels = normal_base_output\n",
+    "\n",
+    "print(test_base_normal_losses)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3480\n"
+     ]
+    }
+   ],
+   "source": [
+    "anormal_test_sample_idxs = [test_item_idx for test_item_idx, test_item in enumerate(test_set) if test_item[1] == 1]\n",
+    "print(len(anormal_test_sample_idxs))\n",
+    "\n",
+    "anormal_test_samples = torch.utils.data.Subset(test_set, anormal_test_sample_idxs)\n",
+    "anormal_test_samples_loader = torch.utils.data.DataLoader(anormal_test_samples, batch_size=batch_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "anormal_base_output = utilsV5.extract_reconstructions_losses(model, anormal_test_samples_loader, device)\n",
+    "    \n",
+    "test_base_anormal_reconstructions, test_base_anormal_losses, \\\n",
+    "test_base_anormal_inputs, test_base_anormal_labels = anormal_base_output"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **2. Determine Reconst. Err. Threshold:** Using 100% percentile on base model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "60% percentile threshold: 0.0167\n",
+      "65% percentile threshold: 0.0177\n",
+      "70% percentile threshold: 0.0190\n",
+      "75% percentile threshold: 0.0201\n",
+      "80% percentile threshold: 0.0209\n",
+      "85% percentile threshold: 0.0223\n",
+      "90% percentile threshold: 0.0235\n",
+      "95% percentile threshold: 0.0250\n",
+      "99% percentile threshold: 0.0271\n",
+      "100% percentile threshold: 0.0357\n"
+     ]
+    }
+   ],
+   "source": [
+    "percentiles = [60, 65, 70, 75, 80, 85, 90, 95, 99, 100]\n",
+    "thresholds = np.percentile(train_base_losses, percentiles)\n",
+    "\n",
+    "for idx, threshold in enumerate(thresholds):\n",
+    "    print(f'{percentiles[idx]}% percentile threshold: {threshold:.4f}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.01594951049141262"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from statistics import mean\n",
+    "mean(train_base_losses)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.022111524475945367"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean(test_base_normal_losses)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "60"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(test_base_normal_losses)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1:  0.9934, BalancedAccuracy:  0.6167, FPRate:  0.7667, Precision:  0.9870, TPRate (Recall):  1.0000, Accuracy:  0.9870, TRAIN-SET Accuracy:  0.6000\n",
+      "F1:  0.9939, BalancedAccuracy:  0.6417, FPRate:  0.7167, Precision:  0.9878, TPRate (Recall):  1.0000, Accuracy:  0.9879, TRAIN-SET Accuracy:  0.6478\n",
+      "F1:  0.9943, BalancedAccuracy:  0.6667, FPRate:  0.6667, Precision:  0.9886, TPRate (Recall):  1.0000, Accuracy:  0.9887, TRAIN-SET Accuracy:  0.7000\n",
+      "F1:  0.9944, BalancedAccuracy:  0.6750, FPRate:  0.6500, Precision:  0.9889, TPRate (Recall):  1.0000, Accuracy:  0.9890, TRAIN-SET Accuracy:  0.7478\n",
+      "F1:  0.9947, BalancedAccuracy:  0.6917, FPRate:  0.6167, Precision:  0.9895, TPRate (Recall):  1.0000, Accuracy:  0.9895, TRAIN-SET Accuracy:  0.8000\n",
+      "F1:  0.9951, BalancedAccuracy:  0.7330, FPRate:  0.5333, Precision:  0.9909, TPRate (Recall):  0.9994, Accuracy:  0.9904, TRAIN-SET Accuracy:  0.8478\n",
+      "F1:  0.9960, BalancedAccuracy:  0.7912, FPRate:  0.4167, Precision:  0.9929, TPRate (Recall):  0.9991, Accuracy:  0.9921, TRAIN-SET Accuracy:  0.9000\n",
+      "F1:  0.9961, BalancedAccuracy:  0.8078, FPRate:  0.3833, Precision:  0.9934, TPRate (Recall):  0.9989, Accuracy:  0.9924, TRAIN-SET Accuracy:  0.9478\n",
+      "F1:  0.9953, BalancedAccuracy:  0.8724, FPRate:  0.2500, Precision:  0.9957, TPRate (Recall):  0.9948, Accuracy:  0.9907, TRAIN-SET Accuracy:  0.9870\n",
+      "F1:  0.9834, BalancedAccuracy:  0.9672, FPRate:  0.0333, Precision:  0.9994, TPRate (Recall):  0.9678, Accuracy:  0.9678, TRAIN-SET Accuracy:  1.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculating performance metrics with respect to changing thresholds\n",
+    "F1s, BalancedAccuracies, FPRs, Recalls = utilsV5.sweep_performance_metrics(thresholds, train_base_tuple, test_base_tuple)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Reconstruction Loss (RL)')"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 2))\n",
+    "\n",
+    "plt.xlim([0, 0.3])\n",
+    "\n",
+    "sns.histplot(test_base_anormal_losses, element=\"step\", label='RL for Anomalies in Test Set', kde=True)\n",
+    "sns.histplot(test_base_normal_losses, element=\"step\", label='RL for Unseen Normals in Test Set', kde=True)\n",
+    "sns.histplot(train_base_losses, element=\"step\", label='RL for Normals in Training Set', kde=True)\n",
+    "\n",
+    "plt.legend(prop={'size': 9}, loc='best')\n",
+    "plt.xlabel('Reconstruction Loss (RL)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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HjhxRv379tGTJEvn6+tpk29aoXr26WrZsqdDQUJ04ccL4vGXLlqmWz2jWmPTExsZq6NChKWZWKlu2rEqXLq2IiAidO3dOsbGxkhL7zHPPPacFCxaoWrVqVm0jKChIo0aNMs6h0qVLq0yZMoqLi9PZs2eNJLWgoCC99tpr2rBhQ4azEmXVmDFjjJlqvL29VbVqVXl6eury5ctGAkBUVJQmTZqke/fuaeDAgenWd/HiRb322msWs895eHioSpUqKly4sPFyg6TxzIYNG3Tp0iUtXLjQqvuByWTSu+++q23btkmS8ubNq6pVqypv3ry6efOmMWZKYov7qbnbt29rwIAB+ueff4zPXF1dVblyZRUrVkzR0dE6d+6c8Tf8888/1adPHy1ZskQlS5bMcP8k6dtvv9Xnn38uSfL09FS1atWUP39+3bx50+K+sXz5chUpUkRvv/12ijpycgxgrewcU2fG+vXrNWrUKIvrfIECBVS5cmW5u7vr0qVLCg4OlpSYWPb222/r5s2b+t///peiLvNr0IEDB4xraPXq1VO8sCMr1yVbX/uOHz+u999/3zj/ypUrp1KlSikyMtKibyU5ffq0sezq6qqyZcuqWLFiypMnjyIiInTx4kXjpQMhISEaMmSIPvvsM3Xu3Dmzu5ohRxgHbty40Tjf3N3dVbVqVRUuXFjBwcH6999/jfFAUhJV0nmclh07dmjYsGHG/USS8uXLpypVqsjLy0tXrlzRrVu3dOTIEb3yyitWJ2Fnhi3GPeZyos/89ddfGjx4sOLj4+Xu7q7q1aurQIECunHjhjEuMplMGjdunHx8fNS2bVtNnDhRS5YskZR4jKtWrSovLy9dvHjRuNfdvXtXr7/+ujZt2mT1GMee46cZM2bom2++sfgsaYZuDw8PI4lUkm7duqU333xTU6ZM0bPPPptqfbbuC5kRExOjv/76y1i3ZpbmYsWKGTOUh4WFpToLoHmSd7FixSy+O3PmjBYuXChJ6tChg1q1apXV5lvw9vZWnTp1jBc87Nq1iwRAAAAAAAAA2A0JgAAAAAAAAHB65gkZUvqJYV988YXFg8rdunXTG2+8oYoVK1qU+/vvvzVhwgRduHBBUVFRGjlypNauXZvmbH0//vijRfJfyZIlNWLECD311FPKmzevRdlLly5p8+bN+vnnn1OtKyEhQSNHjrR4cLd79+4aMWKExfavXLmiSZMmac+ePZKkQ4cOaeLEifrkk0/S3H9zgwcPVmxsrPr06aPBgwdb1B0YGKgPP/xQu3btkiQdPnxYGzZsUJcuXVLUExwcrK+++spYb926td5//31VqVIlRdnQ0FD98ccfWrt2bYq3/CfNXObn56fRo0dLSkyk+f77763an+Tef/99mUwmde/eXcOGDbN4UD88PDzN5EtbM5lMev/99y2SFerXr68RI0aoSZMmFg8zx8fH69ixY1q/fr32799vUU/S33XOnDn68ssvJSU+GJ7V45MWZ+t/9pYdxysuLk7Tpk0z1h9++GF99NFHevjhh1OUjYyM1N69e7Vx40a5u6f8L39bnZ9Z9dNPP1kk/9WtW1cTJ05UrVq1jM+CgoL0+eefa9WqVZo0aZJNZ7+aOnWq8VBzxYoV9fHHH+vRRx9NkUQQExOjAwcOaMuWLcZD30m6du2qrl27yt/fX+3atTM+nzZtWqZniZOkKVOmKDw8XA0aNNCYMWMs/q5xcXG6ceNGputM7sMPP1R0dLQaNGigjz/+WDVr1jS+u337tmbMmKFVq1ZJkq5evarRo0enSGDPTq+++qpeffVV7du3T/369TM+t/X1TJK+/PJLi+S/+vXra9y4capdu7bxWWhoqL755hv98MMPMplMCgwM1NChQ7V69WqrZo+ZMGGC7ty5oyZNmmj06NEWdUdHR+vrr7/WvHnzJCUmSMyaNUuTJk2y4V4m2r17t4KDg+Xl5aURI0bo+eefN9qfkJCgXbt26eOPPzZm25oxY4YeeeSRNGdHCg8P16BBg4yEg4IFC2r48OHq1q2bxQx/9+7d05IlS/TFF18oNjZWp06d0scff6zp06dn2Obt27crPDxchQoV0rvvvquuXbvK09PT+N48KdZW99MkcXFxGjp0qJH85+XlpUGDBun5559X4cKFjXKxsbFau3atMSOrv7+/3nnnHS1cuDDDhKRz587pwIEDypMnj0aMGKE+ffpYjAtPnTqlt99+27iHfP/99+rTp4/KlCljUU9OjQEyI7vG1Jlx9uxZjRkzxrjOFyhQQKNGjVKXLl2MfpSQkKAdO3Zo/PjxCgwMlMlk0ieffKKaNWuqWbNmFvWZH8+2bdsaff9///ufunfvft/ttfW176OPPlJMTIzatGmj9957T5UrVza+i46OTvGSlHz58unJJ5/UE088oSZNmqSIURISEvTnn3/qs88+09mzZ2UymTR27Fg1adLE5kni9h4H3rlzR6NHj5abm5sGDhyoV1991SLh6MqVK3r33Xd17NgxSYnJzS+88IIaNmyYan23bt3SqFGjjOS/vHnz6t1331XPnj0tXhbw999/a9y4cbp8+bLF+MxWbDHuMZcTfWbEiBFKSEjQwIEDNWDAAIu/w6FDhzR06FDdvn1bJpNJM2bMUGRkpJYsWaKCBQsa53vSCyRMJpN++eUXTZgwQfHx8QoPD9esWbM0ZcoUq9pir/HTzz//bJH816ZNGw0ZMkQPPfSQRblTp05p4sSJOnLkiOLj4zV27FjVqlUr1WRhW/eFzDhx4oTFDL2pxTDJ1alTx/h/nTVr1lhcI6XEcywpqdDV1dXi2CQkJGjs2LGKi4tTgQIFjP9LsJV69eoZCYB///23hg0bZtP6AQAAAAAAAGs55uuSAQAAAAAAACtdvnzZ4uHjEiVKpPlg5tGjR/Xdd98Z6+PGjdO0adNSPKgsSU2bNtUvv/xiJMkEBwdr7ty5abbhs88+M9arV6+uVatW6dlnn03xkKQkVapUSW+++aZ27NiR6sPPq1at0sGDB431V199VVOnTk1RtkKFCvrmm2/01FNPGZ+tWbNGf//9d6rtTO7OnTt65513NGHChBR1+/j4aM6cORZJQkkPOyb3+++/Gw+7li1bVl9++WWqyUVS4qwDHTt21LfffqvnnnvOqnZmVXh4uN544w1NnTo1xSw9+fPnV9GiRbN1+0lWr16t7du3G+udOnXSkiVL1LRp0xQPYLq5uemRRx7RuHHjtHz58hxpX3LO1v/sLTuO19GjRxUUFCQpcdaJ7777Ls0HZ729vdWuXTvNmDFDw4cPT/G9Pc/P4OBgzZ4921ivU6eOFi5caJH8JyXO4DFlyhS9/PLLio6Otnhg+H7cuHHDSOxxcXHRvHnz1KxZs1QTdjw9PdWiRQtNnDhRU6dOtcn20xIeHq6mTZtq4cKFKf6u7u7uWUoqTC44OFgNGjTQTz/9ZPHwupSYWJ10vJP8+eef+vXXX+97u47m4sWL+vbbb431Ro0aaeHChRYJelJi33/vvfc0ZsyYNH+bnjt37qhNmzZasGBBirqTkvF69uxpfLZx40ZFRUVlZZfSFRwcLBcXF82YMUP/+9//LJIXXV1d1bZtWy1evFhFihSRlJgoMX78+DTr++yzz4zENB8fH/n5+enFF1+0SP6TEhNdXnvtNX399dfG+bV+/XodP348wzaHh4crb968WrhwoXr16mWR/CclXjuT2Pp++sMPP+jQoUOSEpNcFi9erEGDBlkk/0mJMx727NlTixcvNsZ1Bw4csOqcCQkJkbu7u77//nu98sorKcaFDz30kL799ltjv+Pi4lLMqOeIsmtMnVkff/yx8UKHvHnz6ocfflDPnj0t+pGrq6vat2+vJUuWGGM/k8mkDz/80JjNy1mFh4erS5cumjt3rkXyn5R47UmegLV+/XpNnDhRrVu3TjVGcXV1VatWrbRs2TIjuSYyMlJLly61edvtPQ6MjIxUdHS0pk+fruHDh6eYbaxChQqaP3++RbyQXhu++OILhYWFSUo8jnPnztWLL76YYqbgpk2baunSpSpbtqwxM6WtZMe4Jyf6TEhIiD7++GONHDkyxd+hYcOGFsnk//77r0aNGqU8efLop59+Uo8ePSxmj3ZxcdFzzz2n1157zfhs8+bNxoywGbHH+CkgIMDibzBgwADNmzcvRfKflHjPWLhwoZo0aSIpMdE3tZkp7T0GNp/htFixYhnOji1JHTt2NJa/+uorizru3r2rDz74wEhQbN26tcUswz///LORrJv8JSy2YN4XTp8+7fT3DgAAAAAAADgvEgABAAAAAADglBISErRt2zb169fPIlnkvffeS3UWLEn69ttvZTKZJCU+NP7CCy+ku40CBQpowoQJxvrq1asVHh6eotz8+fONt+vnyZNHs2fPtuohN09PzxQPukvSwoULjeWqVatq5MiRadbh6uqqiRMnWjys/tNPP2W4bUl65JFHNGDAgHTbZ/6A49GjR1N92M18tqqHH3441X1KjS1n+UpN1apVNXTo0GzdRkYSEhIsZnOoWrWqpkyZkmYfNZcvX77sbFqanK3/2Vt2HC/zc6patWoqVKiQVW1J7Zyy5/np5+dnPHDt6uqqyZMnp0gcMvfOO++oXLly973dJNevXzeWixUrpkqVKln1u+y+Nnl4eGjq1KlW/y2ywt3dXVOmTEmReGAu+fFesmRJtrXHXpYsWaKEhARJideUadOmpXtM+vXrp6ZNmxrry5Yts2q22Hz58mnatGnpXtvNkxEiIyONB/NtrXPnznriiSfS/L58+fIW16mkGeqSCwwMlJ+fn7E+bdq0DM/Pxx57TN26dTPWFy9ebFWbBw0alCLRIjlb309jYmIsrr/vv/9+hjMU1apVy+K+Ze3+9e/fP81ZFqXERKMnn3zSWE9KSnRk2TWmzoyTJ0/q8OHDxvobb7yR7t+wQoUKev/99411f39/i9lBnVGRIkU0btw4q2fttXZs6e3tbXGdMJ/J11YcYRzYqVMndejQIc3vCxUqpF69ehnraZ2bYWFh2rhxo7Heq1evFLNLmitWrJg++OCDLLQ4fdkx7smJPtO0adN0XzzRrFkzi+Ti2NhYDRw4MEXCvTnza1JkZKROnz5tVVvsMX768ccfjcS2Bg0apBtLSInnhvn9b8+ePfrvv/8syth7DHzhwgVjuXz58lb9pm7dunrmmWckJSaF9urVS927d1ffvn3VunVrYxZ1Dw8Pi5eeBAYGaubMmZISY53nn3/eJvtgznwf7t27p6tXr9p8GwAAAAAAAIA1SAAEAAAAAACAQxs1apT69+9v8a9Pnz5q3LixBg8erJs3b0pKfFjtvffeU+fOnVOtJyQkRDt37jTWX331Vau236hRI2M2pnv37uno0aMW38fFxWnTpk3GepcuXax+wC41ly9f1rlz54z1vn37Zvhwe6FChdSjRw9j/ffffzceIkxPRg9rS1Ljxo2N5aioqFQfdjN/QPLs2bMOk6TVq1evbE/kyciJEyeM2ZOkxIfT03ug1N6csf/ZU3YdL/NZuy5fvnxfM+LZ8/zctm2bsdyoUaMME3w8PT3Vp08fm23f/DgGBQUZ9wt7a9WqlUqXLp2t22jZsmWK2aCSS3689+3bp5CQkGxtV04zT0Bo3769VQmm5uODoKAgq5KxOnbsmGLWuOQqVaokHx8fY/38+fMZ1psV/fr1y7BM165dLWZZSm32oo0bNxrJj9WrV1fLli2t2r55AuDevXszLO/m5maRYJMWW99P9+zZo9u3b0uSChcurO7du1v1O/P9O3r0qFWzSmX2fvfvv/9a1RZ7ya4xdWaZ32O8vLz04osvZvibzp07W5yH2ZHYlpM6depkMQuWLdWvX99YvnTpku7evWvT+h1hHGhNnzFvw5UrV1JNCv/7778tZnW1Zt/atGlj87GAvcc9We0z1oz96tWrl6nflCxZ0mIGePOEtPTk9PgpISFBa9euNdZfffVVqxJ6y5Urp4YNG0pKnNE0+f3W3n3B39/fWE4+E2l6Jk2aZJxzJpNJp06d0v79+xURESEp8dhPnz7dIqaYPHmy7t69Kzc3N02YMMFilsOdO3fq9ddfV7NmzVSnTh21bt1a7733nkX8Zg3zviQlztoIAAAAAAAA2AMJgAAAAAAAAHBoBw4c0B9//GHx7+jRoxazhnTs2FGrV69W//7906zn4MGDxixAhQsX1kMPPWR1G8wfMDt58qTFd//8849Fck56s0hYI/nD0G3atLHqd+3atTOWY2NjdeLEiQx/88gjj2RYJvnDbmFhYSnKmD+QeeHCBb3zzjsO8VBcerPt5JT9+/cbyx4eHmrfvr0dW5MxZ+x/9pRdx6tu3brGw7+hoaF68803s5wsZK/zMyYmxmKGs8cee8yq3z3++OM2a0OVKlWMGQdNJpNef/11HTt2zGb1Z5V5MkF2yerxPn78eDa0xj6uXbumW7duGevWnp8tWrSwSCwzn2EsLdZczyTLh+Cz43pWtGhR1a1bN8Nynp6eFrNTpfZ3N58VsEWLFla3wXzMdOvWrQyTDqpUqaKiRYtmWK+t76fm9TVp0kQeHh5W/a506dJG8mRcXFyGs0qVKVPGquQH8/udrROtbC27xtSZdeTIEWO5UaNGKlCgQIa/cXNzs7jumdfhjLJ6PzGZTDp06JAWLFigjz76SMOGDdOAAQMsXrhiPou2yWSyuJ7agr3HgR4eHlZdL83bYDKZUp250nxsUaRIkQxfeCBJLi4uatKkiZWttU52jnuys8+YJw6mpXjx4sZy2bJlLdbTYp7sGxoaalVbcnr8dO7cOYu2NW/e3Orfpnc9tfcYODg42Fi2diZzScqfP79++uknTZgwQQ0bNlSBAgXk6empsmXLqnfv3tqwYYOefvppo/yePXu0efNmSYkvQKhVq5akxMTKsWPH6o033tCuXbsUHBys2NhY3bhxQ2vXrlW3bt20Zs0aq9uVfB+CgoKs/i0AAAAAAABgS+m/ihgAAAAAAABwAkeOHMnwob6zZ88ayzExMekmCyZn/oZ484fZpJQz+FjzIGl6zGe3KVKkiNVvzK9Ro4bF+pUrVzJMgDN/KDItSQ8OJjGf3SJJgwYN1LBhQ2OWpE2bNmnz5s16+OGH1axZM9WvX1/169dXkSJFMtyeLZUvXz5Ht5ca89kmqlWrZjEbgyNyxv5nT9l1vHx9fdW5c2etW7dOUuIsWh07dlSNGjXUvHlzPfLII6pXr55V27PX+Xn9+nWLWXKqV69u1e8qVaokDw8PxcbG3ncbPD099fLLL2vu3LmSpNOnT6t3796qUKGCWrZsaRxHa2aFs6WcuDYl72NpSX68L1++bPXD747uypUrFuvWJGRIkru7u6pWrapTp05Jkv77778Mf2NNIoJkeU3LjuuZtedZUtmkmf/Mr2VJzMdNu3btyvKsdHfu3En3WmXt+Wfr+6n5/p08eTJT40LzWVuTjwuTs+ZeJ0l58+Y1lq2ZVdCesmtMnVnm57i117zkZf39/ZWQkGAxY5Qzyez9xGQyadWqVfrqq6907dq1TP3W1knL9h4HFi5c2KrEX/NzU0r9/DQ/lhnNHmeuSpUqVpe1RnaMe3Kiz1hzDzX/O2Tlumpt38np8dOZM2eMZTc3Nw0bNszq35qPT5JfT+09BjY/TzJ7v3Zzc1OfPn0ynOUxKipKEyZMkCSVKlVKQ4YMMb777rvv9Msvv0hKvE4OGjRIZcqU0bFjxzR37lxFRkZqzJgxqly5sh5++OEM25R8Hxz9Pg0AAAAAAIAHFwmAAAAAAAAAcGg7duxQ2bJljfW7d+/q2rVr2rZtmxYtWqSQkBBdu3ZNAwYM0Pfff59m0lFISIixHBkZqT/++CNL7Uk+K4t54qGnp6fy58+fpXqTmD8oac2MOEny588vT09PI+HGmgcuPT09M90+k8mU6uezZ8/Wm2++acwsYDKZdOzYMWPdxcVFNWrU0FNPPaUePXpYnSh1P+73b2EL5v0uM39Pe3HW/mcv2Xm8xo8fr7CwMO3atcv47OzZszp79qwWLFggSapYsaLat2+vXr16qUKFCmluzx7nZ/KkbGsTDN3d3ZU/f37duXPnvtsgSUOGDFFgYKBWrlxpfHblyhVduXJFS5YskZQ4o1fbtm3Vs2dPY+aQ7JQvX75s30bhwoWtKpf8eDvaLJv3I3kfzMw5at5frZk1yFGuZ9b+3SXLfbx7965MJpMx86hkef+6dOmSLl26lKU2ZTSbnbX3alvfT83ru3btWqYTW5KkNhuYuaz0DUeXXWPqzMrqPdi8rMlkUlhYWKbOHUeSmbFuQkKCRo8enalZr8yZJ/Xbgr2vm1k9N1Nrg3lfTJoh1BqZKWstW457cqrPZPZvYe2Mreas7Ts5PX4yv57Gx8fb9HrqKGPg7IrfvvrqK129elWS9OGHHxrj67CwMH399deSEs+xpUuXGkmjjz76qKpWrapBgwYpPj5en3zyiXEs7LEPAAAAAAAAQGY55+sMAQAAAAAAkGsVKFBANWrU0ODBg+Xn56eSJUtKSnwD/Ntvv51m0oit3tKe/OEv81lgbPGQt/mDkpl9uNF8++btygnFixfXzz//rE8//VQNGzZMMZOKyWTSmTNnNGvWLLVv315z587N9gfpHGE2F/O/pzMkAThr/7OX7Dxe3t7e+uabbzR37ly1atUq1fovX76s+fPn65lnntGkSZPSfNDaHudn8hn8MnN8bHmuuLm5afLkyVqyZImeeuqpVGchuXbtmhYvXqxnn31WI0eOvO+klIzkxLUpq8fb1gke9pR8X3LDMcnqPiYkJKQ4Z201bkpISEj3e2vPB1vfT3Nq/x5E2TWmzqys3oOT9x9nHrOYJ+1mZNGiRRaJXBUrVtTIkSO1ZMkS7dq1S0eOHNHp06eNlw2Yz/QI52HLcU9u7DM5PVbIzuupPcfA5rMvZsc19t9//zVeiNKuXTs98cQTxndbt241jmvPnj1TzBjZtm1bY1bogwcPyt/fP8PtJd+H5DODAgAAAAAAADmFGQABAAAAAADgtMqUKaMZM2bopZdeUkJCgm7evKnPP/9ckyZNSlG2QIECxnLVqlW1ceNGm7TBfOaGiIgIJSQk3Fdyh3k7IyIirP6dyWRSZGRkqvXkFDc3N3Xt2lVdu3ZVWFiYDh48qCNHjujAgQM6fvy44uPjJSU+QPfFF18oMjJSI0eOzPF22oo1D/2b94+MZglyBM7c/+whJ45X27Zt1bZtW0VGRurIkSPGOXX48GHjYeP4+HgtWrRIwcHBmjFjRqr15PT5mXxWoswcn+w4Vxo1aqRGjRopJiZGx44d05EjR3Tw4EEdOHDA4m+xYcMG42FoNzc3m7cjp2T1eNtq5lRHSIpKPrNSRESE1bMtmR8TZ7qeZfXv7uXllSIpqkCBAsZLFd5991299tprtmlkFtn6fmr+d33hhRc0bty4+64zt8iuMXVW2pHUR+/nHpMds7A5moSEBH3zzTfGeps2bTR79ux0k2mdYdzqKMz7UGZmgsvOWXfvd9yTW/tMTo+fzPtO3rx5dfTo0SzVkx57jIGLFStmLJvPcmgLJpNJY8eOVWxsrLy9vfXRRx9ZfJ80w7kkNWvWLNU6WrRooTNnzhjly5Ytm+42k++D+f4BAAAAAAAAOcn+r8AGAAAAAAAA7kPDhg3VrVs3Y93Pz0/nzp1LUc78ze9BQUE2236JEiWMZZPJpMuXL99XfeYPk924ccPq2QT8/f0tEi7s/VBawYIF1bZtW40cOVLLli3TX3/9pTFjxqhQoUJGmR9++EHXr1+3Yyv/n/lsD8lnQUpLaGhohmXM+92lS5cy37Ac9qD0v5ySk8fL29tbLVq00ODBg/XTTz/p77//1pQpU4xZUCVp48aNOnLkSIZ15cT5Wbx4cYt1a2bXkKTg4OBMPXydWZ6enmrcuLEGDhyob7/9Vvv27dPs2bNVpUoVo8zhw4e1efPmbGtDTsjq8U7+d5Oy7/qY3YoWLWqx/t9//1n926tXrxrLznQ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+      "text/plain": [
+       "<Figure size 3600x1100 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "utilsV5.plot_all_metrics(F1s=F1s, BalancedAccuracies=BalancedAccuracies, FPRs=FPRs, Recalls=Recalls, percentiles=percentiles)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.11 ('venv': venv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.11"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "f94e0c326c22ca109c8d98fac0e773823a480a04f73c52ef704532a18e9d37e9"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/policies/qat_policy_autoencoder.yaml b/policies/qat_policy_autoencoder.yaml
new file mode 100755
index 000000000..ff3c17aac
--- /dev/null
+++ b/policies/qat_policy_autoencoder.yaml
@@ -0,0 +1,4 @@
+---
+start_epoch: 200
+weight_bits: 8
+shift_quantile: 0.995
diff --git a/pyproject.toml b/pyproject.toml
index f4273ad5e..5ba5cae57 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ target-version = ['py36']
 
 [tool.codespell]
 count = ''
-ignore-words-list = 'nervana,cconfiguration'
+ignore-words-list = 'nervana,cconfiguration,anormal'
 quiet-level = 3
 skip = '*.dasm,*.map,./.mypy_cache,./venv,./.git,./distiller,./data,./datasets/face_id/facenet_pytorch/dependencies,./super-linter.log'
 ignore-regex = '^\s+"image/png".*$'
diff --git a/scripts/evaluate_autoencoder.sh b/scripts/evaluate_autoencoder.sh
new file mode 100755
index 000000000..592e9f0af
--- /dev/null
+++ b/scripts/evaluate_autoencoder.sh
@@ -0,0 +1,2 @@
+#!/bin/sh
+python train.py --deterministic --model ai85autoencoder --dataset SampleMotorDataLimerick_ForEvalWithSignal --regression --device MAX78000 --qat-policy policies/qat_policy_autoencoder.yaml --use-bias --evaluate --exp-load-weights-from ../ai8x-synthesis/trained/ai85-autoencoder-samplemotordatalimerick-qat-q.pth.tar -8 --print-freq 1 "$@"
diff --git a/scripts/train_autoencoder.sh b/scripts/train_autoencoder.sh
new file mode 100755
index 000000000..98de12d61
--- /dev/null
+++ b/scripts/train_autoencoder.sh
@@ -0,0 +1,2 @@
+#!/bin/sh
+python train.py --deterministic --regression --print-freq 1 --epochs 400 --optimizer Adam --lr 0.001 --wd 0 --model ai85autoencoder --use-bias --dataset SampleMotorDataLimerick_ForTrain --device MAX78000 --batch-size 32 --validation-split 0 --show-train-accuracy full --qat-policy policies/qat_policy_autoencoder.yaml "$@"
diff --git a/train_all_models.sh b/train_all_models.sh
index cfb4e804f..18fef7f0b 100755
--- a/train_all_models.sh
+++ b/train_all_models.sh
@@ -59,3 +59,6 @@ scripts/train_bayer2rgb_imagenet.sh "$@"
 echo "-----------------------------"
 echo "Training kws20_nas model"
 scripts/train_kws20_nas.sh "$@"
+echo "-----------------------------"
+echo "Training Auto Encoder model"
+scripts/train_autoencoder.sh "$@"
diff --git a/utils/autoencoder_eval_utils.py b/utils/autoencoder_eval_utils.py
new file mode 100755
index 000000000..a2dd9e4c6
--- /dev/null
+++ b/utils/autoencoder_eval_utils.py
@@ -0,0 +1,261 @@
+###################################################################################################
+#
+# Copyright (C) 2023 Analog Devices, Inc. All Rights Reserved.
+# This software is proprietary to Analog Devices, Inc. and its licensors.
+#
+###################################################################################################
+""" Some utility functions for AutoEncoder Models """
+import numpy as np
+import torch
+from torch import nn
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+sns.set_style("white")
+
+
+DECAY_FACTOR = 1
+
+
+def calc_model_size(model):
+    """
+    Returns the model's weight anf bias number.
+    """
+    model.eval()
+    num_weights = 0
+    num_bias = 0
+    for name, param in model.named_parameters():
+        if param.requires_grad:
+            if name.endswith('weight'):
+                num_weights += np.prod(param.size())
+            elif name.endswith('bias'):
+                num_bias += np.prod(param.size())
+
+    print(f'\nNumber of Model Weights: {num_weights}')
+    print(f'Number of Model Bias: {num_bias}\n')
+    return num_weights, num_bias
+
+
+def extract_reconstructions_losses(model, dataloader, device):
+    """
+    Calculates and returns reconstructed signal reconstruction loss, input signals
+    and latent space representations for autoencoder model.
+    """
+    model.eval()
+    loss_fn = nn.MSELoss(reduce=False)
+    losses = []
+    reconstructions = []
+    inputs = []
+    labels = []
+
+    with torch.no_grad():
+        for tup in dataloader:
+            if len(tup) == 2:
+                signal, label = tup
+            elif len(tup) == 3:
+                signal, label, _ = tup
+
+            signal = signal.to(device)
+            label = label.type(torch.long).to(device)
+
+            inputs.append(signal)
+            labels.append(label)
+
+            model_out = model(signal)
+            if isinstance(model_out, tuple):
+                model_out = model_out[0]
+
+            loss = loss_fn(model_out, signal)
+            loss_numpy = loss.cpu().detach().numpy()
+            decay_vector = np.array([DECAY_FACTOR**i for i in range(loss_numpy.shape[2])])
+            decay_vector = np.tile(decay_vector, (loss_numpy.shape[0], loss_numpy.shape[1], 1))
+
+            decayed_loss = loss_numpy * decay_vector
+            losses.extend(decayed_loss.mean(axis=(1, 2)))
+            reconstructions.append(model_out)
+
+    return reconstructions, losses, inputs, labels
+
+
+def plot_all_metrics(F1s, BalancedAccuracies, FPRs, Recalls, percentiles):
+    """
+    F1, Balanced Accuracy, False Positive Rate metrics are plotted with respect to
+    threshold decided according to percentiles of training loss in percentile list.
+    """
+    fontsize = 22
+    linewidth = 4
+
+    fig, axs = plt.subplots(1, 4, figsize=(36, 11))
+
+    axs[0].plot(percentiles, F1s, '-o', linewidth=linewidth)
+    for i, xy in enumerate(zip(percentiles, F1s)):  # pylint: disable=unused-variable
+        axs[0].annotate(f"{xy[1]: .3f}", xy=xy, fontsize=fontsize - 2)
+
+    axs[0].grid()
+
+    axs[0].set_title('\nF1 Score on Testset\n\n', fontsize=fontsize + 4, color='#0070C0')
+    axs[0].tick_params(axis='both', which='both', labelsize=fontsize)
+    axs[0].legend(("F1 Score",), loc='lower left', fontsize=fontsize - 2)
+
+    axs[1].plot(percentiles, BalancedAccuracies, '-o', linewidth=linewidth)
+    for i, xy in enumerate(zip(percentiles, BalancedAccuracies)):
+        axs[1].annotate(f"{xy[1]: .3f}", xy=xy, fontsize=fontsize - 2)
+
+    axs[1].grid()
+
+    axs[1].set_title('\nBalanced Accuracy ((TPRate + TNRate) / 2) on Testset\n\n',
+                     fontsize=fontsize + 4, color='#0070C0')
+    axs[1].tick_params(axis='both', which='both', labelsize=fontsize)
+    axs[1].legend(("Balanced Acc.",), loc='lower left', fontsize=fontsize - 2)
+
+    axs[2].plot(percentiles, FPRs, '-o', linewidth=linewidth)
+    for i, xy in enumerate(zip(percentiles, FPRs)):
+        axs[2].annotate(f"{xy[1]: .3f}", xy=xy, fontsize=fontsize - 2)
+
+    axs[2].grid()
+    axs[2].set_title('\nFalse Positive Rate on Testset\n\n',
+                     fontsize=fontsize + 4, color='#0070C0')
+    axs[2].tick_params(axis='both', which='both', labelsize=fontsize)
+    axs[2].legend(("FPRate",), loc='lower left', fontsize=fontsize - 2)
+
+    axs[3].plot(percentiles, Recalls, '-o', linewidth=linewidth)
+    for i, xy in enumerate(zip(percentiles, Recalls)):
+        axs[3].annotate(f"{xy[1]: .3f}", xy=xy, fontsize=fontsize - 2)
+
+    axs[3].grid()
+    axs[3].set_title('\nTrue Positive Rate on Testset\n\n', fontsize=fontsize + 4, color='#0070C0')
+    axs[3].tick_params(axis='both', which='both', labelsize=fontsize)
+    axs[3].legend(("Recall",), loc='lower left', fontsize=fontsize - 2)
+
+    fig.supxlabel('\nReconstruction Loss distribution percentile of training samples (%)',
+                  fontsize=fontsize + 4)
+
+    plt.tight_layout()
+    plt.show()
+
+
+def sweep_performance_metrics(thresholds, train_tuple, test_tuple):
+    """
+    F1s, BalancedAccuracies, FPRs, Recalls are calculated
+    and returned based on different thresholds.
+    """
+
+    train_reconstructions, train_losses, \
+        train_inputs, train_labels = train_tuple  # pylint: disable=unused-variable
+    test_reconstructions, test_losses, \
+        test_inputs, test_labels = test_tuple  # pylint: disable=unused-variable
+
+    FPRs = []
+    F1s = []
+    BalancedAccuracies = []
+    Recalls = []
+
+    for threshold in thresholds:
+        FPRate, _, Recall, Precision, Accuracy, F1, BalancedAccuracy = calc_ae_perf_metrics(
+            test_reconstructions,
+            test_inputs,
+            test_labels,
+            threshold=threshold,
+            print_all=False
+            )
+
+        _, _, _, _, AccuracyTrain, _, _ = calc_ae_perf_metrics(
+            train_reconstructions,
+            train_inputs,
+            train_labels,
+            threshold=threshold,
+            print_all=False
+            )
+
+        F1s.append(F1.item())
+        BalancedAccuracies.append(BalancedAccuracy.item())
+        FPRs.append(FPRate.item())
+        Recalls.append(Recall.item())
+
+        print(f"F1: {F1: .4f}, BalancedAccuracy: {BalancedAccuracy: .4f}, "
+              f"FPRate: {FPRate: .4f}, Precision: {Precision: .4f}, TPRate (Recall): "
+              f"{Recall: .4f}, Accuracy: {Accuracy: .4f}, "
+              f"TRAIN-SET Accuracy: {AccuracyTrain: .4f}")
+
+    return F1s, BalancedAccuracies, FPRs, Recalls
+
+
+def calc_ae_perf_metrics(reconstructions, inputs, labels, threshold, print_all=True):
+    """
+    False Positive Rate, TNRate, Recall, Precision, Accuracy, F1, BalancedAccuracy
+    metrics of AutoEncoder are calculated and returned.
+    """
+
+    loss_fn = nn.MSELoss(reduce=False)
+    FP = 0
+    FN = 0
+    TP = 0
+    TN = 0
+
+    Recall = -1
+    Precision = -1
+    Accuracy = -1
+    F1 = -1
+    FPRate = -1
+
+    BalancedAccuracy = -1
+    TNRate = -1   # specificity (SPC), selectivity
+
+    for i, inputs_batch in enumerate(inputs):
+        label_batch = labels[i]
+        reconstructions_batch = reconstructions[i]
+        # inputs_batch = inputs[i]
+
+        loss = loss_fn(reconstructions_batch, inputs_batch)
+
+        # Loss Decay
+        loss_numpy = loss.cpu().detach().numpy()
+        decay_vector = np.array([DECAY_FACTOR**i for i in range(loss_numpy.shape[2])])
+        decay_vector = np.tile(decay_vector, (loss_numpy.shape[0], loss_numpy.shape[1], 1))
+        decayed_loss = loss_numpy * decay_vector
+        decayed_loss = torch.Tensor(decayed_loss).to(label_batch.device)
+
+        loss_batch = decayed_loss.mean(dim=(1, 2))
+        prediction_batch = loss_batch > threshold
+
+        TN += torch.sum(torch.logical_and(torch.logical_not(prediction_batch),
+                                          torch.squeeze(torch.logical_not(label_batch))))
+        TP += torch.sum(torch.logical_and((prediction_batch),
+                                          torch.squeeze(label_batch)))
+        FN += torch.sum(torch.logical_and(torch.logical_not(prediction_batch),
+                                          torch.squeeze(label_batch)))
+        FP += torch.sum(torch.logical_and((prediction_batch),
+                                          torch.squeeze(torch.logical_not(label_batch))))
+
+    if TP + FN != 0:
+        Recall = TP / (TP + FN)
+
+    if TP + FP != 0:
+        Precision = TP / (TP + FP)
+
+    Accuracy = (TP + TN) / (TP + TN + FP + FN)
+
+    if (TN + FP) != 0:
+        FPRate = FP / (TN + FP)
+        TNRate = TN / (TN + FP)
+
+    if Precision + Recall != 0:
+        F1 = 2 * (Precision * Recall) / (Precision + Recall)
+
+    BalancedAccuracy = (Recall + TNRate) / 2
+
+    if print_all:
+        print(f"TP: {TP}")
+        print(f"FP: {FP}")
+        print(f"TN: {TN}")
+        print(f"FN: {FN}")
+        print(f"FPRate: {FPRate}")
+        print(f"TNRate = Specificity: {TNRate}")
+        print(f"TPRate (Recall): {Recall}")
+        print(f"Precision: {Precision}")
+        print(f"Accuracy: {Accuracy}")
+        print(f"F1: {F1}")
+        print(f"BalancedAccuracy: {BalancedAccuracy}")
+
+    return FPRate, TNRate, Recall, Precision, Accuracy, F1, BalancedAccuracy