diff --git a/datasets/spectrumsense.py b/datasets/spectrumsense.py
new file mode 100644
index 000000000..725b334d6
--- /dev/null
+++ b/datasets/spectrumsense.py
@@ -0,0 +1,384 @@
+###################################################################################################
+#
+# Copyright (C) 2025 Analog Devices, Inc. All Rights Reserved.
+# This software is proprietary to Analog Devices, Inc. and its licensors.
+#
+###################################################################################################
+"""
+Classes and functions used to create spectrumsense dataset.
+"""
+import errno
+import glob
+import os
+import shutil
+import subprocess
+import tarfile
+import tempfile
+import urllib
+import urllib.error
+import urllib.request
+
+import numpy as np
+from torch.utils.data import Dataset
+from torchvision import transforms
+
+from PIL import Image
+
+import ai8x
+
+
+class SpectrumSense(Dataset):
+    """
+    Dataloader for the Matlab Spectrum Sensing 5G/LTE Data Set/
+
+    The dataset is provided by Mathworks and accompanies their
+    'Spectrum Sensing with Deep Learning to Identify 5G and LTE Signals'
+    which can be found at the following URL.
+    https://www.mathworks.com/help/comm/ug/spectrum-sensing-with-deep-learning-to-identify-5g-and-lte-signals.html
+
+    Possible class selections:
+    0: None
+    1: LTE
+    2: NR
+    """
+
+    class_dict = {'None': 0, 'LTE': 1, 'NR': 2}
+
+    # Values in the label images mapped to names
+    mask_map = {'None': 0, 'LTE': 255, 'NR': 127}
+
+    # pylint: disable-next=line-too-long
+    url_spectrumsense = 'https://www.mathworks.com/supportfiles/spc/SpectrumSensing/SpectrumSenseTrainingDataNetwork.tar.gz'  # noqa: E501
+
+    def __init__(self, root_dir, d_type, classes=None, transform=None,
+                 im_size=(256, 256), download=True):
+        self.transform = transform
+        self.classes = classes
+        self.root = root_dir
+
+        # Download the data set if necessary
+        if download:
+            self.__download()
+
+        if d_type not in ('test', 'train'):
+            raise ValueError('Data type name msut be test or train.')
+
+        src_folder = os.path.join(self.root, d_type)
+        lbl_folder = os.path.join(self.root, d_type + '_labels')
+        self.src_list = []
+        self.lbl_list = []
+
+        self.__create_mask_dict(im_size)
+
+        for src_file in sorted(glob.glob('*.png', root_dir=src_folder, recursive=False)):
+            lbl_file = os.path.splitext(src_file)[0] + '_lbl.png'
+
+            src_img = Image.open(os.path.join(src_folder, src_file))
+            lbl_img = Image.open(os.path.join(lbl_folder, lbl_file))
+
+            # Dataset images are 256x256. Pad out when the image size is 352
+            if im_size == [352, 352]:
+                (src_img, lbl_img) = self.pad_image_and_label(src_img, lbl_img, im_size)
+
+            src_data = SpectrumSense.normalize(np.asarray(src_img).astype(np.float32))
+
+            # Convert the grayscale label image to 3 channel (RGB)
+            lbl_gray = np.asarray(lbl_img)
+            lbl_rgb = np.empty((lbl_gray.shape[0], lbl_gray.shape[1], 3))
+            for i in range(3):
+                lbl_rgb[:, :, i] = lbl_gray
+            lbl_data = np.zeros((lbl_rgb.shape[0], lbl_rgb.shape[1]), dtype=np.uint8)
+
+            for label_idx, (_, mask) in enumerate(self.label_mask_dict.items()):
+                res = lbl_rgb == mask
+                res = (label_idx + 1) * res.all(axis=2)
+                lbl_data += res.astype(np.uint8)
+
+            self.src_list.append(src_data)
+            self.lbl_list.append(lbl_data)
+
+        if self.classes:
+            self.__filter_classes()
+
+    def __create_mask_dict(self, img_dims):
+        self.label_mask_dict = {}
+        for lbl in self.class_dict:
+            val = self.mask_map[lbl]
+            label_mask = np.zeros((img_dims[0], img_dims[1], 3), dtype=np.uint8)
+            label_mask[:, :, 0] = np.uint8(val)
+            label_mask[:, :, 1] = np.uint8(val)
+            label_mask[:, :, 2] = np.uint8(val)
+            self.label_mask_dict[lbl] = label_mask
+
+    def __filter_classes(self):
+        for e in self.lbl_list:
+            initial_new_class_label = len(self.class_dict) + 5
+            new_class_label = initial_new_class_label
+            for l_class in self.classes:
+                if l_class not in self.class_dict:
+                    print(f'Class is not in the data: {l_class}')
+                    return
+
+                e[(e == self.class_dict[l_class])] = new_class_label
+                new_class_label += 1
+
+            e[(e < initial_new_class_label)] = new_class_label
+            e -= initial_new_class_label
+
+    @classmethod
+    def pad_image_and_label(cls, img, lbl, im_size):
+        """
+        Pads the original images to the specified size and
+        pastes the original content at the origin
+        """
+
+        img_pad = Image.new(img.mode, im_size)
+        img_pad.paste(img, (0, 0))
+
+        lbl_pad = Image.new(lbl.mode, im_size)
+        lbl_pad.paste(lbl, (0, 0))
+        return (img_pad, lbl_pad)
+
+    @staticmethod
+    def normalize(data):
+        """Normalizes data to the range [0, 1)"""
+        return data / 255.
+
+    def __len__(self):
+        return len(self.src_list)
+
+    def __getitem__(self, idx):
+        if self.transform is not None:
+            img = self.transform(self.src_list[idx])
+        return img, self.lbl_list[idx].astype(np.int64)
+
+    def __download(self):
+        train_dir = os.path.join(self.root, 'train')
+        test_dir = os.path.join(self.root, 'test')
+        train_lbl_dir = os.path.join(self.root, 'train_labels')
+        test_lbl_dir = os.path.join(self.root, 'test_labels')
+
+        self.__makedir_exist_ok(train_dir)
+        self.__makedir_exist_ok(test_dir)
+        self.__makedir_exist_ok(train_lbl_dir)
+        self.__makedir_exist_ok(test_lbl_dir)
+
+        if self.__check_data_exists():
+            return  # skip do
+
+        # Need to make sure the user has the hdftor8 utility to convert Matlab
+        # HDF4 format to a PNG.  The pyHDF library didn't seem to expose the
+        # DFR8 API
+        if shutil.which('hdftor8') is None:
+            print('This data set requires the HDF4 Tools to be installed\n'
+                  'Please use apt-get install hdf4-tools, or the respective '
+                  'command for your platform')
+            raise RuntimeError('HDF4 Tools Not installed.')
+
+        # Do everything in temporary directory that can be cleaned up later
+        tmpdir = tempfile.mkdtemp()
+        extract_dir = os.path.join(tmpdir, 'rawdata')
+        filename = self.url_spectrumsense.rpartition('/')[2]
+        self.__download_url(self.url_spectrumsense, tmpdir, filename)
+        self.__extract_archive(os.path.join(tmpdir, filename), extract_dir)
+
+        # Copy the Mathworks licenses over
+        print('This dataset is subject the license terms as described by Mathworks.\n'
+              'Please review the license files extracted from the archive.')
+        lic_files = list(glob.glob('*license*', root_dir=extract_dir, recursive=False))
+        for lic in lic_files:
+            shutil.copy(os.path.join(extract_dir, lic), self.root)
+
+        # Process the data files. TrainingData is used for training
+        processed_files = self.__process_data(
+            os.path.join(extract_dir, 'TrainingData'), train_dir, train_lbl_dir)
+        # The LTE_NR sub-folder is used for test
+        processed_files += self.__process_data(
+            os.path.join(extract_dir, 'TrainingData', 'LTE_NR'), test_dir, test_lbl_dir)
+
+        # Write all the files processed to manifest.txt. This doesn't really add
+        # any value, but we'll use it to confirm this entire process was
+        # successful next time training is requested.
+        with open(os.path.join(self.root, 'manifest.txt'), 'w', encoding='utf-8') as mf:
+            mf.write('\n'.join(sorted(processed_files)))
+
+        # Clean up downloaded archive and files
+        shutil.rmtree(tmpdir)
+
+    def __process_data(self, from_path, to_path, to_lbl_path):
+        """
+        Processes all the PNG files from the from_path and converts their
+        respective HDF files to grayscale PNGs
+        """
+        # Find all the PNGs. Don't recurse as a sub folder may be used for
+        # different train/test data sets
+        img_files = list(glob.glob('*.png', root_dir=from_path, recursive=False))
+        proc_files = []
+
+        for img in img_files:
+            # Grab the original source image
+            shutil.copy(os.path.join(from_path, img), to_path)
+
+            # Create a temp file. Convert the HDF to that temp file. This will
+            # just be a byte stream of data 65536 (256x256)
+            rast_file = tempfile.mktemp()
+            hdf_file = os.path.join(from_path, os.path.splitext(img)[0] + '.hdf')
+            subprocess.run('hdftor8 ' + hdf_file + ' -r ' + rast_file, shell=True, check=False)
+
+            # Now open the file as binary, and tell PIL to treat it like a
+            # 256x256 8-bit single channel ('L')
+            result_file = os.path.splitext(img)[0] + '_lbl.png'
+            with open(rast_file, 'rb') as f:
+                im = Image.frombytes('L', (256, 256), f.read())
+                im.save(os.path.join(to_lbl_path, result_file))
+            os.remove(rast_file)  # Clean up
+
+            proc_files.append(img)
+            proc_files.append(result_file)
+        return proc_files
+
+    def __extract_archive(self, from_path, to_path=None, remove_finished=False):
+        if to_path is None:
+            to_path = os.path.dirname(from_path)
+
+        if from_path.endswith('.tar.gz'):
+            with tarfile.open(from_path, 'r:gz') as tar:
+                tar.extractall(path=to_path)
+        else:
+            raise ValueError(f"Extraction of {from_path} not supported")
+
+        if remove_finished:
+            os.remove(from_path)
+
+    def __download_url(self, url, dl_dir, filename=None):
+        if not filename:
+            filename = os.path.basename(url)
+        fpath = os.path.join(dl_dir, filename)
+        self.__makedir_exist_ok(dl_dir)
+
+        # downloads file
+        try:
+            print('Downloading ' + url + ' to ' + fpath)
+            urllib.request.urlretrieve(url, fpath)
+        except (urllib.error.URLError, IOError) as e:
+            if url[:5] == 'https':
+                url = url.replace('https:', 'http:')
+                print('Failed download. Trying https -> http instead.'
+                      ' Downloading ' + url + ' to ' + fpath)
+                urllib.request.urlretrieve(url, fpath)
+            else:
+                raise e
+
+    def __check_data_exists(self):
+        # Check for manifest.txt to determine if was already done
+        return os.path.exists(os.path.join(self.root, 'manifest.txt'))
+
+    def __makedir_exist_ok(self, dirpath):
+        try:
+            os.makedirs(dirpath)
+        except OSError as e:
+            if e.errno == errno.EEXIST:
+                pass
+            else:
+                raise
+
+
+def spectrumsense_get_datasets_s256(data, load_train=True, load_test=True):
+    """
+    Load the spectrumsense dataset in 48x88x88 format which are composed of 3x352x352 images folded
+    with a fold_ratio of 4.
+    """
+    (data_dir, args) = data
+
+    classes = ['LTE', 'NR']
+
+    transform = transforms.Compose([transforms.ToTensor(),
+                                    ai8x.normalize(args=args),
+                                    ai8x.fold(fold_ratio=4)])
+
+    if load_train:
+        train_dataset = SpectrumSense(root_dir=os.path.join(data_dir, 'SpectrumSense'),
+                                      d_type='train',
+                                      im_size=[256, 256], classes=classes,
+                                      transform=transform)
+    else:
+        train_dataset = None
+
+    if load_test:
+        test_dataset = SpectrumSense(root_dir=os.path.join(data_dir, 'SpectrumSense'),
+                                     d_type='test',
+                                     im_size=[256, 256], classes=classes,
+                                     transform=transform)
+        if args.truncate_testset:
+            test_dataset.src_list = test_dataset.src_list[:1]
+    else:
+        test_dataset = None
+
+    return train_dataset, test_dataset
+
+
+def spectrumsense_get_datasets_s352(data, load_train=True, load_test=True):
+    """
+    Load the spectrumsense dataset in 48x88x88 format which are composed of 3x352x352 images folded
+    with a fold_ratio of 4.
+    """
+    (data_dir, args) = data
+
+    classes = ['LTE', 'NR']
+
+    transform = transforms.Compose([transforms.ToTensor(),
+                                    ai8x.normalize(args=args),
+                                    ai8x.fold(fold_ratio=4)])
+
+    if load_train:
+        train_dataset = SpectrumSense(root_dir=os.path.join(data_dir, 'SpectrumSense'),
+                                      d_type='train',
+                                      im_size=[352, 352], classes=classes,
+                                      transform=transform)
+    else:
+        train_dataset = None
+
+    if load_test:
+        test_dataset = SpectrumSense(root_dir=os.path.join(data_dir, 'SpectrumSense'),
+                                     d_type='test',
+                                     im_size=[352, 352], classes=classes,
+                                     transform=transform)
+        if args.truncate_testset:
+            test_dataset.src_list = test_dataset.src_list[:1]
+    else:
+        test_dataset = None
+
+    return train_dataset, test_dataset
+
+
+def spectrumsense_get_datasets_s256_c3(data, load_train=True, load_test=True):
+    """
+    Load the spectrumsense dataset for 3 classes in 48x64x64 images.
+    """
+    return spectrumsense_get_datasets_s256(data, load_train, load_test)
+
+
+def spectrumsense_get_datasets_s352_c3(data, load_train=True, load_test=True):
+    """
+    Load the spectrumsense dataset for 3 classes in 48x88x88 images.
+    """
+    return spectrumsense_get_datasets_s352(data, load_train, load_test)
+
+
+datasets = [
+    {
+        'name': 'SpectrumSense_s256_c2',  # 2 classes
+        'input': (48, 64, 64),
+        'output': (0, 1, 2),
+        'weight': (1, 1, 1),
+        'loader': spectrumsense_get_datasets_s256_c3,
+    },
+    {
+        'name': 'SpectrumSense_s352_c2',  # 2 classes
+        'input': (48, 88, 88),
+        'output': (0, 1, 2),
+        'weight': (1, 1, 1),
+        'loader': spectrumsense_get_datasets_s352_c3,
+        'fold_ratio': 4,
+    }
+]
diff --git a/policies/qat_policy_spectrumsense.yaml b/policies/qat_policy_spectrumsense.yaml
new file mode 100644
index 000000000..84b577d17
--- /dev/null
+++ b/policies/qat_policy_spectrumsense.yaml
@@ -0,0 +1,3 @@
+---
+start_epoch: 40
+weight_bits: 8
diff --git a/policies/schedule-spectrumsense.yaml b/policies/schedule-spectrumsense.yaml
new file mode 100644
index 000000000..1121b1ee7
--- /dev/null
+++ b/policies/schedule-spectrumsense.yaml
@@ -0,0 +1,13 @@
+---
+lr_schedulers:
+  training_lr:
+    class: MultiStepLR
+    milestones: [20, 80]
+    gamma: 0.5
+
+policies:
+  - lr_scheduler:
+      instance_name: training_lr
+    starting_epoch: 0
+    ending_epoch: 100
+    frequency: 1
diff --git a/scripts/evaluate_spectrumsense_unet.sh b/scripts/evaluate_spectrumsense_unet.sh
new file mode 100755
index 000000000..fe4e42467
--- /dev/null
+++ b/scripts/evaluate_spectrumsense_unet.sh
@@ -0,0 +1,2 @@
+#!/bin/sh
+python train.py --deterministic --model ai85unetlarge --out-fold-ratio 4 --dataset SpectrumSense_s352_c2 --device MAX78000 --qat-policy policies/qat_policy_spectrumsense.yaml --use-bias --evaluate -8 --exp-load-weights-from ../ai8x-synthesis/trained/ai85-spectrumsense-unet-large-q.pth.tar "$@"
diff --git a/scripts/train_spectrumsense_unet.sh b/scripts/train_spectrumsense_unet.sh
new file mode 100755
index 000000000..4d0582024
--- /dev/null
+++ b/scripts/train_spectrumsense_unet.sh
@@ -0,0 +1,3 @@
+#!/bin/sh
+python train.py --deterministic --epochs 100 --optimizer Adam --lr 0.001 --wd 0 --model ai85unetlarge --out-fold-ratio 4 --use-bias --dataset SpectrumSense_s352_c2 --device MAX78000 --batch-size 50 --qat-policy policies/qat_policy_spectrumsense.yaml --compress policies/schedule-spectrumsense.yaml --validation-split 0  "$@"
+
diff --git a/train_all_models.sh b/train_all_models.sh
index 18fef7f0b..ed6e145fa 100755
--- a/train_all_models.sh
+++ b/train_all_models.sh
@@ -62,3 +62,6 @@ scripts/train_kws20_nas.sh "$@"
 echo "-----------------------------"
 echo "Training Auto Encoder model"
 scripts/train_autoencoder.sh "$@"
+echo "-----------------------------"
+echo "Training SpectrumSense UNet model"
+scripts/train_spectrumsense_unet.sh "$@"