diff --git a/README.md b/README.md
index 455b34f..3d05e90 100644
--- a/README.md
+++ b/README.md
@@ -38,7 +38,7 @@ Intended for educational use 🎓, our aim is to highlight the vulnerability of
    - 🛑 Is it a `pip is not recognized...` error? [WATCH THIS!](https://youtu.be/zWYvRS7DtOg)
 3. Fire up `PowerShell` or `Command Prompt` on Windows 🔍.
 4. To install `PyTorch`, select the appropriate command based on your GPU.
-    - Nvidia `pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118`
+    - Nvidia `pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118`
     - AMD or CPU `pip install torch torchvision torchaudio`
 5. 📦 Run the command below to install the required Open Source packages:
 ```
diff --git a/export.py b/export.py
index e167b20..8cd2bf1 100644
--- a/export.py
+++ b/export.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
 
@@ -77,6 +77,25 @@ from utils.torch_utils import select_device, smart_inference_mode
 MACOS = platform.system() == 'Darwin'  # macOS environment
 
 
+class iOSModel(torch.nn.Module):
+
+    def __init__(self, model, im):
+        super().__init__()
+        b, c, h, w = im.shape  # batch, channel, height, width
+        self.model = model
+        self.nc = model.nc  # number of classes
+        if w == h:
+            self.normalize = 1. / w
+        else:
+            self.normalize = torch.tensor([1. / w, 1. / h, 1. / w, 1. / h])  # broadcast (slower, smaller)
+            # np = model(im)[0].shape[1]  # number of points
+            # self.normalize = torch.tensor([1. / w, 1. / h, 1. / w, 1. / h]).expand(np, 4)  # explicit (faster, larger)
+
+    def forward(self, x):
+        xywh, conf, cls = self.model(x)[0].squeeze().split((4, 1, self.nc), 1)
+        return cls * conf, xywh * self.normalize  # confidence (3780, 80), coordinates (3780, 4)
+
+
 def export_formats():
     # YOLOv5 export formats
     x = [
@@ -91,7 +110,7 @@ def export_formats():
         ['TensorFlow Lite', 'tflite', '.tflite', True, False],
         ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False],
         ['TensorFlow.js', 'tfjs', '_web_model', False, False],
-        ['PaddlePaddle', 'paddle', '_paddle_model', True, True],]
+        ['PaddlePaddle', 'paddle', '_paddle_model', True, True], ]
     return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
 
 
@@ -136,7 +155,7 @@ def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX
     import onnx
 
     LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
-    f = file.with_suffix('.onnx')
+    f = str(file.with_suffix('.onnx'))
 
     output_names = ['output0', 'output1'] if isinstance(model, SegmentationModel) else ['output0']
     if dynamic:
@@ -186,23 +205,68 @@ def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX
 
 
 @try_export
-def export_openvino(file, metadata, half, prefix=colorstr('OpenVINO:')):
+def export_openvino(file, metadata, half, int8, data, prefix=colorstr('OpenVINO:')):
     # YOLOv5 OpenVINO export
-    check_requirements('openvino-dev')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
-    import openvino.inference_engine as ie
+    check_requirements('openvino-dev>=2023.0')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
+    import openvino.runtime as ov  # noqa
+    from openvino.tools import mo  # noqa
 
-    LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
-    f = str(file).replace('.pt', f'_openvino_model{os.sep}')
+    LOGGER.info(f'\n{prefix} starting export with openvino {ov.__version__}...')
+    f = str(file).replace(file.suffix, f'_openvino_model{os.sep}')
+    f_onnx = file.with_suffix('.onnx')
+    f_ov = str(Path(f) / file.with_suffix('.xml').name)
+    if int8:
+        check_requirements('nncf>=2.4.0')  # requires at least version 2.4.0 to use the post-training quantization
+        import nncf
+        import numpy as np
+        from openvino.runtime import Core
 
-    args = [
-        'mo',
-        '--input_model',
-        str(file.with_suffix('.onnx')),
-        '--output_dir',
-        f,
-        '--data_type',
-        ('FP16' if half else 'FP32'),]
-    subprocess.run(args, check=True, env=os.environ)  # export
+        from utils.dataloaders import create_dataloader
+        core = Core()
+        onnx_model = core.read_model(f_onnx)  # export
+
+        def prepare_input_tensor(image: np.ndarray):
+            input_tensor = image.astype(np.float32)  # uint8 to fp16/32
+            input_tensor /= 255.0  # 0 - 255 to 0.0 - 1.0
+
+            if input_tensor.ndim == 3:
+                input_tensor = np.expand_dims(input_tensor, 0)
+            return input_tensor
+
+        def gen_dataloader(yaml_path, task='train', imgsz=640, workers=4):
+            data_yaml = check_yaml(yaml_path)
+            data = check_dataset(data_yaml)
+            dataloader = create_dataloader(data[task],
+                                           imgsz=imgsz,
+                                           batch_size=1,
+                                           stride=32,
+                                           pad=0.5,
+                                           single_cls=False,
+                                           rect=False,
+                                           workers=workers)[0]
+            return dataloader
+
+        # noqa: F811
+
+        def transform_fn(data_item):
+            """
+            Quantization transform function. Extracts and preprocess input data from dataloader item for quantization.
+            Parameters:
+               data_item: Tuple with data item produced by DataLoader during iteration
+            Returns:
+                input_tensor: Input data for quantization
+            """
+            img = data_item[0].numpy()
+            input_tensor = prepare_input_tensor(img)
+            return input_tensor
+
+        ds = gen_dataloader(data)
+        quantization_dataset = nncf.Dataset(ds, transform_fn)
+        ov_model = nncf.quantize(onnx_model, quantization_dataset, preset=nncf.QuantizationPreset.MIXED)
+    else:
+        ov_model = mo.convert_model(f_onnx, model_name=file.stem, framework='onnx', compress_to_fp16=half)  # export
+
+    ov.serialize(ov_model, f_ov)  # save
     yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata)  # add metadata.yaml
     return f, None
 
@@ -223,7 +287,7 @@ def export_paddle(model, im, file, metadata, prefix=colorstr('PaddlePaddle:')):
 
 
 @try_export
-def export_coreml(model, im, file, int8, half, prefix=colorstr('CoreML:')):
+def export_coreml(model, im, file, int8, half, nms, prefix=colorstr('CoreML:')):
     # YOLOv5 CoreML export
     check_requirements('coremltools')
     import coremltools as ct
@@ -231,6 +295,8 @@ def export_coreml(model, im, file, int8, half, prefix=colorstr('CoreML:')):
     LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
     f = file.with_suffix('.mlmodel')
 
+    if nms:
+        model = iOSModel(model, im)
     ts = torch.jit.trace(model, im, strict=False)  # TorchScript model
     ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])])
     bits, mode = (8, 'kmeans_lut') if int8 else (16, 'linear') if half else (32, None)
@@ -435,7 +501,7 @@ def export_edgetpu(file, prefix=colorstr('Edge TPU:')):
         '10',
         '--out_dir',
         str(file.parent),
-        f_tfl,], check=True)
+        f_tfl, ], check=True)
     return f, None
 
 
@@ -456,7 +522,7 @@ def export_tfjs(file, int8, prefix=colorstr('TensorFlow.js:')):
         '--quantize_uint8' if int8 else '',
         '--output_node_names=Identity,Identity_1,Identity_2,Identity_3',
         str(f_pb),
-        str(f),]
+        str(f), ]
     subprocess.run([arg for arg in args if arg], check=True)
 
     json = Path(f_json).read_text()
@@ -506,6 +572,129 @@ def add_tflite_metadata(file, metadata, num_outputs):
         tmp_file.unlink()
 
 
+def pipeline_coreml(model, im, file, names, y, prefix=colorstr('CoreML Pipeline:')):
+    # YOLOv5 CoreML pipeline
+    import coremltools as ct
+    from PIL import Image
+
+    print(f'{prefix} starting pipeline with coremltools {ct.__version__}...')
+    batch_size, ch, h, w = list(im.shape)  # BCHW
+    t = time.time()
+
+    # YOLOv5 Output shapes
+    spec = model.get_spec()
+    out0, out1 = iter(spec.description.output)
+    if platform.system() == 'Darwin':
+        img = Image.new('RGB', (w, h))  # img(192 width, 320 height)
+        # img = torch.zeros((*opt.img_size, 3)).numpy()  # img size(320,192,3) iDetection
+        out = model.predict({'image': img})
+        out0_shape, out1_shape = out[out0.name].shape, out[out1.name].shape
+    else:  # linux and windows can not run model.predict(), get sizes from pytorch output y
+        s = tuple(y[0].shape)
+        out0_shape, out1_shape = (s[1], s[2] - 5), (s[1], 4)  # (3780, 80), (3780, 4)
+
+    # Checks
+    nx, ny = spec.description.input[0].type.imageType.width, spec.description.input[0].type.imageType.height
+    na, nc = out0_shape
+    # na, nc = out0.type.multiArrayType.shape  # number anchors, classes
+    assert len(names) == nc, f'{len(names)} names found for nc={nc}'  # check
+
+    # Define output shapes (missing)
+    out0.type.multiArrayType.shape[:] = out0_shape  # (3780, 80)
+    out1.type.multiArrayType.shape[:] = out1_shape  # (3780, 4)
+    # spec.neuralNetwork.preprocessing[0].featureName = '0'
+
+    # Flexible input shapes
+    # from coremltools.models.neural_network import flexible_shape_utils
+    # s = [] # shapes
+    # s.append(flexible_shape_utils.NeuralNetworkImageSize(320, 192))
+    # s.append(flexible_shape_utils.NeuralNetworkImageSize(640, 384))  # (height, width)
+    # flexible_shape_utils.add_enumerated_image_sizes(spec, feature_name='image', sizes=s)
+    # r = flexible_shape_utils.NeuralNetworkImageSizeRange()  # shape ranges
+    # r.add_height_range((192, 640))
+    # r.add_width_range((192, 640))
+    # flexible_shape_utils.update_image_size_range(spec, feature_name='image', size_range=r)
+
+    # Print
+    print(spec.description)
+
+    # Model from spec
+    model = ct.models.MLModel(spec)
+
+    # 3. Create NMS protobuf
+    nms_spec = ct.proto.Model_pb2.Model()
+    nms_spec.specificationVersion = 5
+    for i in range(2):
+        decoder_output = model._spec.description.output[i].SerializeToString()
+        nms_spec.description.input.add()
+        nms_spec.description.input[i].ParseFromString(decoder_output)
+        nms_spec.description.output.add()
+        nms_spec.description.output[i].ParseFromString(decoder_output)
+
+    nms_spec.description.output[0].name = 'confidence'
+    nms_spec.description.output[1].name = 'coordinates'
+
+    output_sizes = [nc, 4]
+    for i in range(2):
+        ma_type = nms_spec.description.output[i].type.multiArrayType
+        ma_type.shapeRange.sizeRanges.add()
+        ma_type.shapeRange.sizeRanges[0].lowerBound = 0
+        ma_type.shapeRange.sizeRanges[0].upperBound = -1
+        ma_type.shapeRange.sizeRanges.add()
+        ma_type.shapeRange.sizeRanges[1].lowerBound = output_sizes[i]
+        ma_type.shapeRange.sizeRanges[1].upperBound = output_sizes[i]
+        del ma_type.shape[:]
+
+    nms = nms_spec.nonMaximumSuppression
+    nms.confidenceInputFeatureName = out0.name  # 1x507x80
+    nms.coordinatesInputFeatureName = out1.name  # 1x507x4
+    nms.confidenceOutputFeatureName = 'confidence'
+    nms.coordinatesOutputFeatureName = 'coordinates'
+    nms.iouThresholdInputFeatureName = 'iouThreshold'
+    nms.confidenceThresholdInputFeatureName = 'confidenceThreshold'
+    nms.iouThreshold = 0.45
+    nms.confidenceThreshold = 0.25
+    nms.pickTop.perClass = True
+    nms.stringClassLabels.vector.extend(names.values())
+    nms_model = ct.models.MLModel(nms_spec)
+
+    # 4. Pipeline models together
+    pipeline = ct.models.pipeline.Pipeline(input_features=[('image', ct.models.datatypes.Array(3, ny, nx)),
+                                                           ('iouThreshold', ct.models.datatypes.Double()),
+                                                           ('confidenceThreshold', ct.models.datatypes.Double())],
+                                           output_features=['confidence', 'coordinates'])
+    pipeline.add_model(model)
+    pipeline.add_model(nms_model)
+
+    # Correct datatypes
+    pipeline.spec.description.input[0].ParseFromString(model._spec.description.input[0].SerializeToString())
+    pipeline.spec.description.output[0].ParseFromString(nms_model._spec.description.output[0].SerializeToString())
+    pipeline.spec.description.output[1].ParseFromString(nms_model._spec.description.output[1].SerializeToString())
+
+    # Update metadata
+    pipeline.spec.specificationVersion = 5
+    pipeline.spec.description.metadata.versionString = 'https://github.com/ultralytics/yolov5'
+    pipeline.spec.description.metadata.shortDescription = 'https://github.com/ultralytics/yolov5'
+    pipeline.spec.description.metadata.author = 'glenn.jocher@ultralytics.com'
+    pipeline.spec.description.metadata.license = 'https://github.com/ultralytics/yolov5/blob/master/LICENSE'
+    pipeline.spec.description.metadata.userDefined.update({
+        'classes': ','.join(names.values()),
+        'iou_threshold': str(nms.iouThreshold),
+        'confidence_threshold': str(nms.confidenceThreshold)})
+
+    # Save the model
+    f = file.with_suffix('.mlmodel')  # filename
+    model = ct.models.MLModel(pipeline.spec)
+    model.input_description['image'] = 'Input image'
+    model.input_description['iouThreshold'] = f'(optional) IOU Threshold override (default: {nms.iouThreshold})'
+    model.input_description['confidenceThreshold'] = \
+        f'(optional) Confidence Threshold override (default: {nms.confidenceThreshold})'
+    model.output_description['confidence'] = 'Boxes × Class confidence (see user-defined metadata "classes")'
+    model.output_description['coordinates'] = 'Boxes × [x, y, width, height] (relative to image size)'
+    model.save(f)  # pipelined
+    print(f'{prefix} pipeline success ({time.time() - t:.2f}s), saved as {f} ({file_size(f):.1f} MB)')
+
+
 @smart_inference_mode()
 def run(
         data=ROOT / 'data/coco128.yaml',  # 'dataset.yaml path'
@@ -582,9 +771,11 @@ def run(
     if onnx or xml:  # OpenVINO requires ONNX
         f[2], _ = export_onnx(model, im, file, opset, dynamic, simplify)
     if xml:  # OpenVINO
-        f[3], _ = export_openvino(file, metadata, half)
+        f[3], _ = export_openvino(file, metadata, half, int8, data)
     if coreml:  # CoreML
-        f[4], _ = export_coreml(model, im, file, int8, half)
+        f[4], ct_model = export_coreml(model, im, file, int8, half, nms)
+        if nms:
+            pipeline_coreml(ct_model, im, file, model.names, y)
     if any((saved_model, pb, tflite, edgetpu, tfjs)):  # TensorFlow formats
         assert not tflite or not tfjs, 'TFLite and TF.js models must be exported separately, please pass only one type.'
         assert not isinstance(model, ClassificationModel), 'ClassificationModel export to TF formats not yet supported.'
@@ -640,7 +831,7 @@ def parse_opt(known=False):
     parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True')
     parser.add_argument('--keras', action='store_true', help='TF: use Keras')
     parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile')
-    parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization')
+    parser.add_argument('--int8', action='store_true', help='CoreML/TF/OpenVINO INT8 quantization')
     parser.add_argument('--dynamic', action='store_true', help='ONNX/TF/TensorRT: dynamic axes')
     parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
     parser.add_argument('--opset', type=int, default=17, help='ONNX: opset version')
@@ -669,4 +860,4 @@ def main(opt):
 
 if __name__ == '__main__':
     opt = parse_opt()
-    main(opt)
+    main(opt)
\ No newline at end of file
diff --git a/models/common.py b/models/common.py
index aa8ae67..75cc4e9 100644
--- a/models/common.py
+++ b/models/common.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Common modules
 """
@@ -24,12 +24,24 @@ import torch.nn as nn
 from PIL import Image
 from torch.cuda import amp
 
+# Import 'ultralytics' package or install if if missing
+try:
+    import ultralytics
+
+    assert hasattr(ultralytics, '__version__')  # verify package is not directory
+except (ImportError, AssertionError):
+    import os
+
+    os.system('pip install -U ultralytics')
+    import ultralytics
+
+from ultralytics.utils.plotting import Annotator, colors, save_one_box
+
 from utils import TryExcept
 from utils.dataloaders import exif_transpose, letterbox
 from utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr,
                            increment_path, is_jupyter, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy,
                            xyxy2xywh, yaml_load)
-from utils.plots import Annotator, colors, save_one_box
 from utils.torch_utils import copy_attr, smart_inference_mode
 
 
@@ -333,7 +345,7 @@ class DetectMultiBackend(nn.Module):
         super().__init__()
         w = str(weights[0] if isinstance(weights, list) else weights)
         pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w)
-        fp16 &= pt or jit or onnx or engine  # FP16
+        fp16 &= pt or jit or onnx or engine or triton  # FP16
         nhwc = coreml or saved_model or pb or tflite or edgetpu  # BHWC formats (vs torch BCWH)
         stride = 32  # default stride
         cuda = torch.cuda.is_available() and device.type != 'cpu'  # use CUDA
@@ -353,8 +365,9 @@ class DetectMultiBackend(nn.Module):
             model.half() if fp16 else model.float()
             if extra_files['config.txt']:  # load metadata dict
                 d = json.loads(extra_files['config.txt'],
-                               object_hook=lambda d: {int(k) if k.isdigit() else k: v
-                                                      for k, v in d.items()})
+                               object_hook=lambda d: {
+                                   int(k) if k.isdigit() else k: v
+                                   for k, v in d.items()})
                 stride, names = int(d['stride']), d['names']
         elif dnn:  # ONNX OpenCV DNN
             LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...')
@@ -372,18 +385,18 @@ class DetectMultiBackend(nn.Module):
                 stride, names = int(meta['stride']), eval(meta['names'])
         elif xml:  # OpenVINO
             LOGGER.info(f'Loading {w} for OpenVINO inference...')
-            check_requirements('openvino')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
+            check_requirements('openvino>=2023.0')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
             from openvino.runtime import Core, Layout, get_batch
-            ie = Core()
+            core = Core()
             if not Path(w).is_file():  # if not *.xml
                 w = next(Path(w).glob('*.xml'))  # get *.xml file from *_openvino_model dir
-            network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin'))
-            if network.get_parameters()[0].get_layout().empty:
-                network.get_parameters()[0].set_layout(Layout('NCHW'))
-            batch_dim = get_batch(network)
+            ov_model = core.read_model(model=w, weights=Path(w).with_suffix('.bin'))
+            if ov_model.get_parameters()[0].get_layout().empty:
+                ov_model.get_parameters()[0].set_layout(Layout('NCHW'))
+            batch_dim = get_batch(ov_model)
             if batch_dim.is_static:
                 batch_size = batch_dim.get_length()
-            executable_network = ie.compile_model(network, device_name='CPU')  # device_name="MYRIAD" for Intel NCS2
+            ov_compiled_model = core.compile_model(ov_model, device_name='AUTO')  # AUTO selects best available device
             stride, names = self._load_metadata(Path(w).with_suffix('.yaml'))  # load metadata
         elif engine:  # TensorRT
             LOGGER.info(f'Loading {w} for TensorRT inference...')
@@ -523,7 +536,7 @@ class DetectMultiBackend(nn.Module):
             y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
         elif self.xml:  # OpenVINO
             im = im.cpu().numpy()  # FP32
-            y = list(self.executable_network([im]).values())
+            y = list(self.ov_compiled_model(im).values())
         elif self.engine:  # TensorRT
             if self.dynamic and im.shape != self.bindings['images'].shape:
                 i = self.model.get_binding_index('images')
@@ -540,7 +553,7 @@ class DetectMultiBackend(nn.Module):
         elif self.coreml:  # CoreML
             im = im.cpu().numpy()
             im = Image.fromarray((im[0] * 255).astype('uint8'))
-            # im = im.resize((192, 320), Image.ANTIALIAS)
+            # im = im.resize((192, 320), Image.BILINEAR)
             y = self.model.predict({'image': im})  # coordinates are xywh normalized
             if 'confidence' in y:
                 box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]])  # xyxy pixels
diff --git a/models/experimental.py b/models/experimental.py
index 02d35b9..11f75e2 100644
--- a/models/experimental.py
+++ b/models/experimental.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Experimental modules
 """
@@ -87,11 +87,11 @@ def attempt_load(weights, device=None, inplace=True, fuse=True):
 
         model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval())  # model in eval mode
 
-    # Module compatibility updates
+    # Module updates
     for m in model.modules():
         t = type(m)
         if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model):
-            m.inplace = inplace  # torch 1.7.0 compatibility
+            m.inplace = inplace
             if t is Detect and not isinstance(m.anchor_grid, list):
                 delattr(m, 'anchor_grid')
                 setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)
diff --git a/models/hub/anchors.yaml b/models/hub/anchors.yaml
index e4d7beb..df2f668 100644
--- a/models/hub/anchors.yaml
+++ b/models/hub/anchors.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 # Default anchors for COCO data
 
 
diff --git a/models/hub/yolov3-spp.yaml b/models/hub/yolov3-spp.yaml
index c669821..4a71ed4 100644
--- a/models/hub/yolov3-spp.yaml
+++ b/models/hub/yolov3-spp.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/hub/yolov3-tiny.yaml b/models/hub/yolov3-tiny.yaml
index b28b443..50b47e2 100644
--- a/models/hub/yolov3-tiny.yaml
+++ b/models/hub/yolov3-tiny.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/hub/yolov3.yaml b/models/hub/yolov3.yaml
index d1ef912..c5e2109 100644
--- a/models/hub/yolov3.yaml
+++ b/models/hub/yolov3.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/hub/yolov5-bifpn.yaml b/models/hub/yolov5-bifpn.yaml
index 504815f..9dbdd4e 100644
--- a/models/hub/yolov5-bifpn.yaml
+++ b/models/hub/yolov5-bifpn.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/hub/yolov5-fpn.yaml b/models/hub/yolov5-fpn.yaml
index a23e9c6..2292eb1 100644
--- a/models/hub/yolov5-fpn.yaml
+++ b/models/hub/yolov5-fpn.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/hub/yolov5-p2.yaml b/models/hub/yolov5-p2.yaml
index 554117d..2c0ae44 100644
--- a/models/hub/yolov5-p2.yaml
+++ b/models/hub/yolov5-p2.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/hub/yolov5-p34.yaml b/models/hub/yolov5-p34.yaml
index dbf0f85..60ae3b4 100644
--- a/models/hub/yolov5-p34.yaml
+++ b/models/hub/yolov5-p34.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/hub/yolov5-p6.yaml b/models/hub/yolov5-p6.yaml
index a17202f..a9e1b5f 100644
--- a/models/hub/yolov5-p6.yaml
+++ b/models/hub/yolov5-p6.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/hub/yolov5-p7.yaml b/models/hub/yolov5-p7.yaml
index edd7d13..a502412 100644
--- a/models/hub/yolov5-p7.yaml
+++ b/models/hub/yolov5-p7.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/hub/yolov5-panet.yaml b/models/hub/yolov5-panet.yaml
index ccfbf90..5595e25 100644
--- a/models/hub/yolov5-panet.yaml
+++ b/models/hub/yolov5-panet.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/hub/yolov5l6.yaml b/models/hub/yolov5l6.yaml
index 632c2cb..651dbb0 100644
--- a/models/hub/yolov5l6.yaml
+++ b/models/hub/yolov5l6.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/hub/yolov5m6.yaml b/models/hub/yolov5m6.yaml
index ecc53fd..059b12b 100644
--- a/models/hub/yolov5m6.yaml
+++ b/models/hub/yolov5m6.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/hub/yolov5n6.yaml b/models/hub/yolov5n6.yaml
index 0c0c71d..5052e7c 100644
--- a/models/hub/yolov5n6.yaml
+++ b/models/hub/yolov5n6.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/hub/yolov5s-LeakyReLU.yaml b/models/hub/yolov5s-LeakyReLU.yaml
index 3a179bf..0368a78 100644
--- a/models/hub/yolov5s-LeakyReLU.yaml
+++ b/models/hub/yolov5s-LeakyReLU.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/hub/yolov5s-ghost.yaml b/models/hub/yolov5s-ghost.yaml
index ff9519c..ce5238f 100644
--- a/models/hub/yolov5s-ghost.yaml
+++ b/models/hub/yolov5s-ghost.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/hub/yolov5s-transformer.yaml b/models/hub/yolov5s-transformer.yaml
index 100d7c4..f526716 100644
--- a/models/hub/yolov5s-transformer.yaml
+++ b/models/hub/yolov5s-transformer.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/hub/yolov5s6.yaml b/models/hub/yolov5s6.yaml
index a28fb55..2f39b03 100644
--- a/models/hub/yolov5s6.yaml
+++ b/models/hub/yolov5s6.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/hub/yolov5x6.yaml b/models/hub/yolov5x6.yaml
index ba795c4..e1edbcb 100644
--- a/models/hub/yolov5x6.yaml
+++ b/models/hub/yolov5x6.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/segment/yolov5l-seg.yaml b/models/segment/yolov5l-seg.yaml
index 4782de1..71f80cc 100644
--- a/models/segment/yolov5l-seg.yaml
+++ b/models/segment/yolov5l-seg.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/segment/yolov5m-seg.yaml b/models/segment/yolov5m-seg.yaml
index 07ec25b..2b8e1db 100644
--- a/models/segment/yolov5m-seg.yaml
+++ b/models/segment/yolov5m-seg.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/segment/yolov5n-seg.yaml b/models/segment/yolov5n-seg.yaml
index c28225a..1f67f8e 100644
--- a/models/segment/yolov5n-seg.yaml
+++ b/models/segment/yolov5n-seg.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/segment/yolov5s-seg.yaml b/models/segment/yolov5s-seg.yaml
index a827814..2ff2524 100644
--- a/models/segment/yolov5s-seg.yaml
+++ b/models/segment/yolov5s-seg.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/segment/yolov5x-seg.yaml b/models/segment/yolov5x-seg.yaml
index 5d0c452..589f65c 100644
--- a/models/segment/yolov5x-seg.yaml
+++ b/models/segment/yolov5x-seg.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/tf.py b/models/tf.py
index 8290cf2..62ba3eb 100644
--- a/models/tf.py
+++ b/models/tf.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 TensorFlow, Keras and TFLite versions of YOLOv5
 Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127
@@ -310,7 +310,7 @@ class TFDetect(keras.layers.Layer):
                 y = tf.concat([xy, wh, tf.sigmoid(y[..., 4:5 + self.nc]), y[..., 5 + self.nc:]], -1)
                 z.append(tf.reshape(y, [-1, self.na * ny * nx, self.no]))
 
-        return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1),)
+        return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1), )
 
     @staticmethod
     def _make_grid(nx=20, ny=20):
@@ -486,7 +486,7 @@ class TFModel:
                                                             iou_thres,
                                                             conf_thres,
                                                             clip_boxes=False)
-            return (nms,)
+            return (nms, )
         return x  # output [1,6300,85] = [xywh, conf, class0, class1, ...]
         # x = x[0]  # [x(1,6300,85), ...] to x(6300,85)
         # xywh = x[..., :4]  # x(6300,4) boxes
diff --git a/models/yolo.py b/models/yolo.py
index ed21c06..4f4d567 100644
--- a/models/yolo.py
+++ b/models/yolo.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 YOLO-specific modules
 
@@ -21,8 +21,8 @@ if str(ROOT) not in sys.path:
 if platform.system() != 'Windows':
     ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
-from models.common import *
-from models.experimental import *
+from models.common import *  # noqa
+from models.experimental import *  # noqa
 from utils.autoanchor import check_anchor_order
 from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args
 from utils.plots import feature_visualization
@@ -76,7 +76,7 @@ class Detect(nn.Module):
                     y = torch.cat((xy, wh, conf), 4)
                 z.append(y.view(bs, self.na * nx * ny, self.no))
 
-        return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x)
+        return x if self.training else (torch.cat(z, 1), ) if self.export else (torch.cat(z, 1), x)
 
     def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, '1.10.0')):
         d = self.anchors[i].device
@@ -126,7 +126,7 @@ class BaseModel(nn.Module):
 
     def _profile_one_layer(self, m, x, dt):
         c = m == self.model[-1]  # is final layer, copy input as inplace fix
-        o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPs
+        o = thop.profile(m, inputs=(x.copy() if c else x, ), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPs
         t = time_sync()
         for _ in range(10):
             m(x.copy() if c else x)
diff --git a/models/yolov5l.yaml b/models/yolov5l.yaml
index ce8a5de..31362f8 100644
--- a/models/yolov5l.yaml
+++ b/models/yolov5l.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/yolov5m.yaml b/models/yolov5m.yaml
index ad13ab3..a76900c 100644
--- a/models/yolov5m.yaml
+++ b/models/yolov5m.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/yolov5n.yaml b/models/yolov5n.yaml
index 8a28a40..aba96cf 100644
--- a/models/yolov5n.yaml
+++ b/models/yolov5n.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/yolov5s.yaml b/models/yolov5s.yaml
index f35beab..5d05364 100644
--- a/models/yolov5s.yaml
+++ b/models/yolov5s.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/models/yolov5x.yaml b/models/yolov5x.yaml
index f617a02..4bdd939 100644
--- a/models/yolov5x.yaml
+++ b/models/yolov5x.yaml
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # Parameters
 nc: 80  # number of classes
diff --git a/utils/__init__.py b/utils/__init__.py
index 33db6b0..d33b924 100644
--- a/utils/__init__.py
+++ b/utils/__init__.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 utils/initialization
 """
@@ -54,13 +54,17 @@ def notebook_init(verbose=True):
     import os
     import shutil
 
-    from utils.general import check_font, check_requirements, is_colab
+    from ultralytics.utils.checks import check_requirements
+
+    from utils.general import check_font, is_colab
     from utils.torch_utils import select_device  # imports
 
     check_font()
 
     import psutil
 
+    if check_requirements('wandb', install=False):
+        os.system('pip uninstall -y wandb')  # eliminate unexpected account creation prompt with infinite hang
     if is_colab():
         shutil.rmtree('/content/sample_data', ignore_errors=True)  # remove colab /sample_data directory
 
diff --git a/utils/activations.py b/utils/activations.py
index 084ce8c..e4d4bbd 100644
--- a/utils/activations.py
+++ b/utils/activations.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Activation functions
 """
diff --git a/utils/augmentations.py b/utils/augmentations.py
index 7ab75f1..1e60930 100644
--- a/utils/augmentations.py
+++ b/utils/augmentations.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Image augmentation functions
 """
@@ -330,7 +330,7 @@ def classify_albumentations(
                 if vflip > 0:
                     T += [A.VerticalFlip(p=vflip)]
                 if jitter > 0:
-                    color_jitter = (float(jitter),) * 3  # repeat value for brightness, contrast, satuaration, 0 hue
+                    color_jitter = (float(jitter), ) * 3  # repeat value for brightness, contrast, satuaration, 0 hue
                     T += [A.ColorJitter(*color_jitter, 0)]
         else:  # Use fixed crop for eval set (reproducibility)
             T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]
diff --git a/utils/autoanchor.py b/utils/autoanchor.py
index bb5cf6e..4c11ab3 100644
--- a/utils/autoanchor.py
+++ b/utils/autoanchor.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 AutoAnchor utils
 """
diff --git a/utils/autobatch.py b/utils/autobatch.py
index bdeb91c..aa763b8 100644
--- a/utils/autobatch.py
+++ b/utils/autobatch.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Auto-batch utils
 """
diff --git a/utils/callbacks.py b/utils/callbacks.py
index 166d893..c90fa82 100644
--- a/utils/callbacks.py
+++ b/utils/callbacks.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Callback utils
 """
@@ -32,7 +32,7 @@ class Callbacks:
             'on_model_save': [],
             'on_train_end': [],
             'on_params_update': [],
-            'teardown': [],}
+            'teardown': [], }
         self.stop_training = False  # set True to interrupt training
 
     def register_action(self, hook, name='', callback=None):
diff --git a/utils/dataloaders.py b/utils/dataloaders.py
index 28d5b79..1fbd036 100644
--- a/utils/dataloaders.py
+++ b/utils/dataloaders.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Dataloaders and dataset utils
 """
@@ -36,7 +36,7 @@ from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, c
 from utils.torch_utils import torch_distributed_zero_first
 
 # Parameters
-HELP_URL = 'See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
+HELP_URL = 'See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data'
 IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm'  # include image suffixes
 VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv'  # include video suffixes
 LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
@@ -355,7 +355,7 @@ class LoadStreams:
             # Start thread to read frames from video stream
             st = f'{i + 1}/{n}: {s}... '
             if urlparse(s).hostname in ('www.youtube.com', 'youtube.com', 'youtu.be'):  # if source is YouTube video
-                # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/Zgi9g1ksQHc'
+                # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/LNwODJXcvt4'
                 check_requirements(('pafy', 'youtube_dl==2020.12.2'))
                 import pafy
                 s = pafy.new(s).getbest(preftype='mp4').url  # YouTube URL
diff --git a/utils/docker/Dockerfile b/utils/docker/Dockerfile
index 811ad4a..f472716 100644
--- a/utils/docker/Dockerfile
+++ b/utils/docker/Dockerfile
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 # Builds ultralytics/yolov5:latest image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
 # Image is CUDA-optimized for YOLOv5 single/multi-GPU training and inference
 
@@ -12,7 +12,7 @@ ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Aria
 ENV DEBIAN_FRONTEND noninteractive
 RUN apt update
 RUN TZ=Etc/UTC apt install -y tzdata
-RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg
+RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg
 # RUN alias python=python3
 
 # Security updates
@@ -24,14 +24,13 @@ RUN rm -rf /usr/src/app && mkdir -p /usr/src/app
 WORKDIR /usr/src/app
 
 # Copy contents
-# COPY . /usr/src/app  (issues as not a .git directory)
-RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app
+COPY . /usr/src/app
 
 # Install pip packages
 COPY requirements.txt .
 RUN python3 -m pip install --upgrade pip wheel
 RUN pip install --no-cache -r requirements.txt albumentations comet gsutil notebook \
-    coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2022.3'
+    coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0'
     # tensorflow tensorflowjs \
 
 # Set environment variables
diff --git a/utils/docker/Dockerfile-arm64 b/utils/docker/Dockerfile-arm64
index 7023c6a..0de85bf 100644
--- a/utils/docker/Dockerfile-arm64
+++ b/utils/docker/Dockerfile-arm64
@@ -1,9 +1,9 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 # Builds ultralytics/yolov5:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
 # Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi
 
 # Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
-FROM arm64v8/ubuntu:rolling
+FROM arm64v8/ubuntu:22.10
 
 # Downloads to user config dir
 ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
@@ -12,7 +12,7 @@ ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Aria
 ENV DEBIAN_FRONTEND noninteractive
 RUN apt update
 RUN TZ=Etc/UTC apt install -y tzdata
-RUN apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1-mesa-glx libglib2.0-0 libpython3-dev
+RUN apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1 libglib2.0-0 libpython3-dev
 # RUN alias python=python3
 
 # Install pip packages
@@ -27,8 +27,7 @@ RUN mkdir -p /usr/src/app
 WORKDIR /usr/src/app
 
 # Copy contents
-# COPY . /usr/src/app  (issues as not a .git directory)
-RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app
+COPY . /usr/src/app
 ENV DEBIAN_FRONTEND teletype
 
 
diff --git a/utils/docker/Dockerfile-cpu b/utils/docker/Dockerfile-cpu
index 06bad9a..c7ba1e3 100644
--- a/utils/docker/Dockerfile-cpu
+++ b/utils/docker/Dockerfile-cpu
@@ -1,25 +1,27 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 # Builds ultralytics/yolov5:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
 # Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments
 
 # Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
-FROM ubuntu:rolling
+FROM ubuntu:mantic-20231011
 
 # Downloads to user config dir
 ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
 
 # Install linux packages
-ENV DEBIAN_FRONTEND noninteractive
-RUN apt update
-RUN TZ=Etc/UTC apt install -y tzdata
-RUN apt install --no-install-recommends -y python3-pip git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg
+# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
+RUN apt update \
+    && apt install --no-install-recommends -y python3-pip git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
 # RUN alias python=python3
 
+# Remove python3.11/EXTERNALLY-MANAGED or use 'pip install --break-system-packages' avoid 'externally-managed-environment' Ubuntu nightly error
+RUN rm -rf /usr/lib/python3.11/EXTERNALLY-MANAGED
+
 # Install pip packages
 COPY requirements.txt .
 RUN python3 -m pip install --upgrade pip wheel
 RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \
-    coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2022.3' \
+    coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0' \
     # tensorflow tensorflowjs \
     --extra-index-url https://download.pytorch.org/whl/cpu
 
@@ -28,9 +30,7 @@ RUN mkdir -p /usr/src/app
 WORKDIR /usr/src/app
 
 # Copy contents
-# COPY . /usr/src/app  (issues as not a .git directory)
-RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app
-ENV DEBIAN_FRONTEND teletype
+COPY . /usr/src/app
 
 
 # Usage Examples -------------------------------------------------------------------------------------------------------
diff --git a/utils/downloads.py b/utils/downloads.py
index 88f5237..9298259 100644
--- a/utils/downloads.py
+++ b/utils/downloads.py
@@ -1,10 +1,9 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Download utils
 """
 
 import logging
-import os
 import subprocess
 import urllib
 from pathlib import Path
@@ -53,7 +52,7 @@ def curl_download(url, filename, *, silent: bool = False) -> bool:
         '--retry',
         '9',
         '-C',
-        '-',])
+        '-', ])
     return proc.returncode == 0
 
 
diff --git a/utils/flask_rest_api/example_request.py b/utils/flask_rest_api/example_request.py
index 952e5dc..256ad13 100644
--- a/utils/flask_rest_api/example_request.py
+++ b/utils/flask_rest_api/example_request.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Perform test request
 """
diff --git a/utils/flask_rest_api/restapi.py b/utils/flask_rest_api/restapi.py
index 9258b1a..ae4756b 100644
--- a/utils/flask_rest_api/restapi.py
+++ b/utils/flask_rest_api/restapi.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Run a Flask REST API exposing one or more YOLOv5s models
 """
diff --git a/utils/general.py b/utils/general.py
index adb9242..135141e 100644
--- a/utils/general.py
+++ b/utils/general.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 General utils
 """
@@ -36,6 +36,17 @@ import torch
 import torchvision
 import yaml
 
+# Import 'ultralytics' package or install if if missing
+try:
+    import ultralytics
+
+    assert hasattr(ultralytics, '__version__')  # verify package is not directory
+except (ImportError, AssertionError):
+    os.system('pip install -U ultralytics')
+    import ultralytics
+
+from ultralytics.utils.checks import check_requirements
+
 from utils import TryExcept, emojis
 from utils.downloads import curl_download, gsutil_getsize
 from utils.metrics import box_iou, fitness
@@ -58,6 +69,7 @@ pd.options.display.max_columns = 10
 cv2.setNumThreads(0)  # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
 os.environ['NUMEXPR_MAX_THREADS'] = str(NUM_THREADS)  # NumExpr max threads
 os.environ['OMP_NUM_THREADS'] = '1' if platform.system() == 'darwin' else str(NUM_THREADS)  # OpenMP (PyTorch and SciPy)
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  # suppress verbose TF compiler warnings in Colab
 
 
 def is_ascii(s=''):
@@ -137,12 +149,12 @@ def set_logging(name=LOGGING_NAME, verbose=True):
             name: {
                 'class': 'logging.StreamHandler',
                 'formatter': name,
-                'level': level,}},
+                'level': level, }},
         'loggers': {
             name: {
                 'level': level,
                 'handlers': [name],
-                'propagate': False,}}})
+                'propagate': False, }}})
 
 
 set_logging(LOGGING_NAME)  # run before defining LOGGER
@@ -369,7 +381,7 @@ def check_git_info(path='.'):
         return {'remote': None, 'branch': None, 'commit': None}
 
 
-def check_python(minimum='3.7.0'):
+def check_python(minimum='3.8.0'):
     # Check current python version vs. required python version
     check_version(platform.python_version(), minimum, name='Python ', hard=True)
 
@@ -386,41 +398,6 @@ def check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=Fals
     return result
 
 
-@TryExcept()
-def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), install=True, cmds=''):
-    # Check installed dependencies meet YOLOv5 requirements (pass *.txt file or list of packages or single package str)
-    prefix = colorstr('red', 'bold', 'requirements:')
-    check_python()  # check python version
-    if isinstance(requirements, Path):  # requirements.txt file
-        file = requirements.resolve()
-        assert file.exists(), f'{prefix} {file} not found, check failed.'
-        with file.open() as f:
-            requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(f) if x.name not in exclude]
-    elif isinstance(requirements, str):
-        requirements = [requirements]
-
-    s = ''
-    n = 0
-    for r in requirements:
-        try:
-            pkg.require(r)
-        except (pkg.VersionConflict, pkg.DistributionNotFound):  # exception if requirements not met
-            s += f'"{r}" '
-            n += 1
-
-    if s and install and AUTOINSTALL:  # check environment variable
-        LOGGER.info(f"{prefix} YOLOv5 requirement{'s' * (n > 1)} {s}not found, attempting AutoUpdate...")
-        try:
-            # assert check_online(), "AutoUpdate skipped (offline)"
-            LOGGER.info(check_output(f'pip install {s} {cmds}', shell=True).decode())
-            source = file if 'file' in locals() else requirements
-            s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \
-                f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n"
-            LOGGER.info(s)
-        except Exception as e:
-            LOGGER.warning(f'{prefix} ❌ {e}')
-
-
 def check_img_size(imgsz, s=32, floor=0):
     # Verify image size is a multiple of stride s in each dimension
     if isinstance(imgsz, int):  # integer i.e. img_size=640
@@ -449,7 +426,7 @@ def check_imshow(warn=False):
         return False
 
 
-def check_suffix(file='yolov5s.pt', suffix=('.pt',), msg=''):
+def check_suffix(file='yolov5s.pt', suffix=('.pt', ), msg=''):
     # Check file(s) for acceptable suffix
     if file and suffix:
         if isinstance(suffix, str):
@@ -1135,6 +1112,7 @@ def imshow(path, im):
     imshow_(path.encode('unicode_escape').decode(), im)
 
 
-cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow  # redefine
+if Path(inspect.stack()[0].filename).parent.parent.as_posix() in inspect.stack()[-1].filename:
+    cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow  # redefine
 
 # Variables ------------------------------------------------------------------------------------------------------------
diff --git a/utils/google_app_engine/additional_requirements.txt b/utils/google_app_engine/additional_requirements.txt
index d5b7675..c1a2af2 100644
--- a/utils/google_app_engine/additional_requirements.txt
+++ b/utils/google_app_engine/additional_requirements.txt
@@ -1,5 +1,5 @@
 # add these requirements in your app on top of the existing ones
-pip==21.1
-Flask==1.0.2
+pip==23.3
+Flask==2.3.2
 gunicorn==19.10.0
-werkzeug>=2.2.3 # not directly required, pinned by Snyk to avoid a vulnerability
+werkzeug>=3.0.1 # not directly required, pinned by Snyk to avoid a vulnerability
diff --git a/utils/loggers/__init__.py b/utils/loggers/__init__.py
index 9de1f22..ba7d279 100644
--- a/utils/loggers/__init__.py
+++ b/utils/loggers/__init__.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Logging utils
 """
@@ -9,7 +9,6 @@ from pathlib import Path
 
 import pkg_resources as pkg
 import torch
-from torch.utils.tensorboard import SummaryWriter
 
 from utils.general import LOGGER, colorstr, cv2
 from utils.loggers.clearml.clearml_utils import ClearmlLogger
@@ -20,6 +19,11 @@ from utils.torch_utils import de_parallel
 LOGGERS = ('csv', 'tb', 'wandb', 'clearml', 'comet')  # *.csv, TensorBoard, Weights & Biases, ClearML
 RANK = int(os.getenv('RANK', -1))
 
+try:
+    from torch.utils.tensorboard import SummaryWriter
+except ImportError:
+    SummaryWriter = lambda *args: None  # None = SummaryWriter(str)
+
 try:
     import wandb
 
@@ -42,15 +46,15 @@ except (ImportError, AssertionError):
     clearml = None
 
 try:
-    if RANK not in [0, -1]:
-        comet_ml = None
-    else:
+    if RANK in {0, -1}:
         import comet_ml
 
         assert hasattr(comet_ml, '__version__')  # verify package import not local dir
         from utils.loggers.comet import CometLogger
 
-except (ModuleNotFoundError, ImportError, AssertionError):
+    else:
+        comet_ml = None
+except (ImportError, AssertionError):
     comet_ml = None
 
 
@@ -84,10 +88,6 @@ class Loggers():
         self.csv = True  # always log to csv
 
         # Messages
-        if not clearml:
-            prefix = colorstr('ClearML: ')
-            s = f"{prefix}run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML"
-            self.logger.info(s)
         if not comet_ml:
             prefix = colorstr('Comet: ')
             s = f"{prefix}run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet"
@@ -114,7 +114,7 @@ class Loggers():
                 self.clearml = None
                 prefix = colorstr('ClearML: ')
                 LOGGER.warning(f'{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging.'
-                               f' See https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml#readme')
+                               f' See https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration#readme')
 
         else:
             self.clearml = None
diff --git a/utils/loggers/clearml/clearml_utils.py b/utils/loggers/clearml/clearml_utils.py
index 2764abe..4e999bf 100644
--- a/utils/loggers/clearml/clearml_utils.py
+++ b/utils/loggers/clearml/clearml_utils.py
@@ -5,8 +5,7 @@ from pathlib import Path
 
 import numpy as np
 import yaml
-
-from utils.plots import Annotator, colors
+from ultralytics.utils.plotting import Annotator, colors
 
 try:
     import clearml
diff --git a/utils/loggers/comet/README.md b/utils/loggers/comet/README.md
index 47e6a45..3ad52b0 100644
--- a/utils/loggers/comet/README.md
+++ b/utils/loggers/comet/README.md
@@ -59,7 +59,7 @@ Check out an example of a [completed run here](https://www.comet.com/examples/co
 
 Or better yet, try it out yourself in this Colab Notebook
 
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/comet-ml/comet-examples/blob/master/integrations/model-training/yolov5/notebooks/Comet_and_YOLOv5.ipynb)
 
 # Log automatically
 
@@ -164,7 +164,7 @@ env COMET_LOG_PER_CLASS_METRICS=true python train.py \
 
 If you would like to store your data using [Comet Artifacts](https://www.comet.com/docs/v2/guides/data-management/using-artifacts/#learn-more?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github), you can do so using the `upload_dataset` flag.
 
-The dataset be organized in the way described in the [YOLOv5 documentation](https://docs.ultralytics.com/tutorials/train-custom-datasets/#3-organize-directories). The dataset config `yaml` file must follow the same format as that of the `coco128.yaml` file.
+The dataset be organized in the way described in the [YOLOv5 documentation](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/). The dataset config `yaml` file must follow the same format as that of the `coco128.yaml` file.
 
 ```shell
 python train.py \
diff --git a/utils/loggers/comet/__init__.py b/utils/loggers/comet/__init__.py
index d459984..c14a5f8 100644
--- a/utils/loggers/comet/__init__.py
+++ b/utils/loggers/comet/__init__.py
@@ -18,7 +18,7 @@ try:
     # Project Configuration
     config = comet_ml.config.get_config()
     COMET_PROJECT_NAME = config.get_string(os.getenv('COMET_PROJECT_NAME'), 'comet.project_name', default='yolov5')
-except (ModuleNotFoundError, ImportError):
+except ImportError:
     comet_ml = None
     COMET_PROJECT_NAME = None
 
@@ -42,7 +42,7 @@ COMET_MODEL_NAME = os.getenv('COMET_MODEL_NAME', 'yolov5')
 COMET_UPLOAD_DATASET = os.getenv('COMET_UPLOAD_DATASET', 'false').lower() == 'true'
 
 # Evaluation Settings
-COMET_LOG_CONFUSION_MATRIX = os.getenv('COMET_LOG_CONFUSION_MATRIX', 'true').lower() == 'true'
+COMET_LOG_CONFUSION_MATRIX = (os.getenv('COMET_LOG_CONFUSION_MATRIX', 'true').lower() == 'true')
 COMET_LOG_PREDICTIONS = os.getenv('COMET_LOG_PREDICTIONS', 'true').lower() == 'true'
 COMET_MAX_IMAGE_UPLOADS = int(os.getenv('COMET_MAX_IMAGE_UPLOADS', 100))
 
@@ -51,10 +51,10 @@ CONF_THRES = float(os.getenv('CONF_THRES', 0.001))
 IOU_THRES = float(os.getenv('IOU_THRES', 0.6))
 
 # Batch Logging Settings
-COMET_LOG_BATCH_METRICS = os.getenv('COMET_LOG_BATCH_METRICS', 'false').lower() == 'true'
+COMET_LOG_BATCH_METRICS = (os.getenv('COMET_LOG_BATCH_METRICS', 'false').lower() == 'true')
 COMET_BATCH_LOGGING_INTERVAL = os.getenv('COMET_BATCH_LOGGING_INTERVAL', 1)
 COMET_PREDICTION_LOGGING_INTERVAL = os.getenv('COMET_PREDICTION_LOGGING_INTERVAL', 1)
-COMET_LOG_PER_CLASS_METRICS = os.getenv('COMET_LOG_PER_CLASS_METRICS', 'false').lower() == 'true'
+COMET_LOG_PER_CLASS_METRICS = (os.getenv('COMET_LOG_PER_CLASS_METRICS', 'false').lower() == 'true')
 
 RANK = int(os.getenv('RANK', -1))
 
@@ -82,7 +82,7 @@ class CometLogger:
         self.comet_log_batch_interval = COMET_BATCH_LOGGING_INTERVAL
 
         # Dataset Artifact Settings
-        self.upload_dataset = self.opt.upload_dataset if self.opt.upload_dataset else COMET_UPLOAD_DATASET
+        self.upload_dataset = self.opt.upload_dataset or COMET_UPLOAD_DATASET
         self.resume = self.opt.resume
 
         # Default parameters to pass to Experiment objects
@@ -90,9 +90,10 @@ class CometLogger:
             'log_code': False,
             'log_env_gpu': True,
             'log_env_cpu': True,
-            'project_name': COMET_PROJECT_NAME,}
+            'project_name': COMET_PROJECT_NAME, }
         self.default_experiment_kwargs.update(experiment_kwargs)
         self.experiment = self._get_experiment(self.comet_mode, run_id)
+        self.experiment.set_name(self.opt.name)
 
         self.data_dict = self.check_dataset(self.opt.data)
         self.class_names = self.data_dict['names']
@@ -136,7 +137,7 @@ class CometLogger:
 
         self.comet_log_predictions = COMET_LOG_PREDICTIONS
         if self.opt.bbox_interval == -1:
-            self.comet_log_prediction_interval = 1 if self.opt.epochs < 10 else self.opt.epochs // 10
+            self.comet_log_prediction_interval = (1 if self.opt.epochs < 10 else self.opt.epochs // 10)
         else:
             self.comet_log_prediction_interval = self.opt.bbox_interval
 
@@ -152,7 +153,7 @@ class CometLogger:
             'comet_log_per_class_metrics': COMET_LOG_PER_CLASS_METRICS,
             'comet_log_batch_metrics': COMET_LOG_BATCH_METRICS,
             'comet_log_confusion_matrix': COMET_LOG_CONFUSION_MATRIX,
-            'comet_model_name': COMET_MODEL_NAME,})
+            'comet_model_name': COMET_MODEL_NAME, })
 
         # Check if running the Experiment with the Comet Optimizer
         if hasattr(self.opt, 'comet_optimizer_id'):
@@ -169,7 +170,7 @@ class CometLogger:
                     **self.default_experiment_kwargs,
                 )
 
-            return comet_ml.OfflineExperiment(**self.default_experiment_kwargs,)
+            return comet_ml.OfflineExperiment(**self.default_experiment_kwargs, )
 
         else:
             try:
@@ -213,7 +214,7 @@ class CometLogger:
             'fitness_score': fitness_score[-1],
             'epochs_trained': epoch + 1,
             'save_period': opt.save_period,
-            'total_epochs': opt.epochs,}
+            'total_epochs': opt.epochs, }
 
         model_files = glob.glob(f'{path}/*.pt')
         for model_path in model_files:
@@ -231,7 +232,8 @@ class CometLogger:
         with open(data_file) as f:
             data_config = yaml.safe_load(f)
 
-        if data_config['path'].startswith(COMET_PREFIX):
+        path = data_config.get('path')
+        if path and path.startswith(COMET_PREFIX):
             path = data_config['path'].replace(COMET_PREFIX, '')
             data_dict = self.download_dataset_artifact(path)
 
@@ -269,7 +271,7 @@ class CometLogger:
                     'x': xyxy[0],
                     'y': xyxy[1],
                     'x2': xyxy[2],
-                    'y2': xyxy[3]},})
+                    'y2': xyxy[3]}, })
         for *xyxy, conf, cls in filtered_detections.tolist():
             metadata.append({
                 'label': f'{self.class_names[int(cls)]}',
@@ -278,7 +280,7 @@ class CometLogger:
                     'x': xyxy[0],
                     'y': xyxy[1],
                     'x2': xyxy[2],
-                    'y2': xyxy[3]},})
+                    'y2': xyxy[3]}, })
 
         self.metadata_dict[image_name] = metadata
         self.logged_images_count += 1
@@ -312,8 +314,16 @@ class CometLogger:
             image_logical_path, label_logical_path = map(lambda x: os.path.relpath(x, path), [image_file, label_file])
 
             try:
-                artifact.add(image_file, logical_path=image_logical_path, metadata={'split': split})
-                artifact.add(label_file, logical_path=label_logical_path, metadata={'split': split})
+                artifact.add(
+                    image_file,
+                    logical_path=image_logical_path,
+                    metadata={'split': split},
+                )
+                artifact.add(
+                    label_file,
+                    logical_path=label_logical_path,
+                    metadata={'split': split},
+                )
             except ValueError as e:
                 logger.error('COMET ERROR: Error adding file to Artifact. Skipping file.')
                 logger.error(f'COMET ERROR: {e}')
@@ -355,15 +365,14 @@ class CometLogger:
         data_dict['path'] = artifact_save_dir
 
         metadata_names = metadata.get('names')
-        if type(metadata_names) == dict:
+        if isinstance(metadata_names, dict):
             data_dict['names'] = {int(k): v for k, v in metadata.get('names').items()}
-        elif type(metadata_names) == list:
+        elif isinstance(metadata_names, list):
             data_dict['names'] = {int(k): v for k, v in zip(range(len(metadata_names)), metadata_names)}
         else:
             raise "Invalid 'names' field in dataset yaml file. Please use a list or dictionary"
 
-        data_dict = self.update_data_paths(data_dict)
-        return data_dict
+        return self.update_data_paths(data_dict)
 
     def update_data_paths(self, data_dict):
         path = data_dict.get('path', '')
@@ -475,8 +484,9 @@ class CometLogger:
                             'f1': f1[i],
                             'true_positives': tp[i],
                             'false_positives': fp[i],
-                            'support': nt[c]},
-                        prefix=class_name)
+                            'support': nt[c], },
+                        prefix=class_name,
+                    )
 
         if self.comet_log_confusion_matrix:
             epoch = self.experiment.curr_epoch
diff --git a/utils/loggers/wandb/wandb_utils.py b/utils/loggers/wandb/wandb_utils.py
index c8ab381..4ea32b1 100644
--- a/utils/loggers/wandb/wandb_utils.py
+++ b/utils/loggers/wandb/wandb_utils.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 
 # WARNING ⚠️ wandb is deprecated and will be removed in future release.
 # See supported integrations at https://github.com/ultralytics/yolov5#integrations
diff --git a/utils/loss.py b/utils/loss.py
index 9b9c3d9..26cca87 100644
--- a/utils/loss.py
+++ b/utils/loss.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Loss functions
 """
diff --git a/utils/metrics.py b/utils/metrics.py
index 95f364c..5646f40 100644
--- a/utils/metrics.py
+++ b/utils/metrics.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Model validation metrics
 """
diff --git a/utils/plots.py b/utils/plots.py
index 24c618c..db6f94a 100644
--- a/utils/plots.py
+++ b/utils/plots.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Plotting utils
 """
@@ -8,7 +8,6 @@ import math
 import os
 from copy import copy
 from pathlib import Path
-from urllib.error import URLError
 
 import cv2
 import matplotlib
@@ -17,13 +16,13 @@ import numpy as np
 import pandas as pd
 import seaborn as sn
 import torch
-from PIL import Image, ImageDraw, ImageFont
+from PIL import Image, ImageDraw
+from scipy.ndimage.filters import gaussian_filter1d
+from ultralytics.utils.plotting import Annotator
 
 from utils import TryExcept, threaded
-from utils.general import (CONFIG_DIR, FONT, LOGGER, check_font, check_requirements, clip_boxes, increment_path,
-                           is_ascii, xywh2xyxy, xyxy2xywh)
+from utils.general import LOGGER, clip_boxes, increment_path, xywh2xyxy, xyxy2xywh
 from utils.metrics import fitness
-from utils.segment.general import scale_image
 
 # Settings
 RANK = int(os.getenv('RANK', -1))
@@ -52,120 +51,6 @@ class Colors:
 colors = Colors()  # create instance for 'from utils.plots import colors'
 
 
-def check_pil_font(font=FONT, size=10):
-    # Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary
-    font = Path(font)
-    font = font if font.exists() else (CONFIG_DIR / font.name)
-    try:
-        return ImageFont.truetype(str(font) if font.exists() else font.name, size)
-    except Exception:  # download if missing
-        try:
-            check_font(font)
-            return ImageFont.truetype(str(font), size)
-        except TypeError:
-            check_requirements('Pillow>=8.4.0')  # known issue https://github.com/ultralytics/yolov5/issues/5374
-        except URLError:  # not online
-            return ImageFont.load_default()
-
-
-class Annotator:
-    # YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
-    def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
-        assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
-        non_ascii = not is_ascii(example)  # non-latin labels, i.e. asian, arabic, cyrillic
-        self.pil = pil or non_ascii
-        if self.pil:  # use PIL
-            self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
-            self.draw = ImageDraw.Draw(self.im)
-            self.font = check_pil_font(font='Arial.Unicode.ttf' if non_ascii else font,
-                                       size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
-        else:  # use cv2
-            self.im = im
-        self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2)  # line width
-
-    def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
-        # Add one xyxy box to image with label
-        if self.pil or not is_ascii(label):
-            self.draw.rectangle(box, width=self.lw, outline=color)  # box
-            if label:
-                w, h = self.font.getsize(label)  # text width, height (WARNING: deprecated) in 9.2.0
-                # _, _, w, h = self.font.getbbox(label)  # text width, height (New)
-                outside = box[1] - h >= 0  # label fits outside box
-                self.draw.rectangle(
-                    (box[0], box[1] - h if outside else box[1], box[0] + w + 1,
-                     box[1] + 1 if outside else box[1] + h + 1),
-                    fill=color,
-                )
-                # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls')  # for PIL>8.0
-                self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
-        else:  # cv2
-            p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
-            cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
-            if label:
-                tf = max(self.lw - 1, 1)  # font thickness
-                w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0]  # text width, height
-                outside = p1[1] - h >= 3
-                p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
-                cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA)  # filled
-                cv2.putText(self.im,
-                            label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
-                            0,
-                            self.lw / 3,
-                            txt_color,
-                            thickness=tf,
-                            lineType=cv2.LINE_AA)
-
-    def masks(self, masks, colors, im_gpu, alpha=0.5, retina_masks=False):
-        """Plot masks at once.
-        Args:
-            masks (tensor): predicted masks on cuda, shape: [n, h, w]
-            colors (List[List[Int]]): colors for predicted masks, [[r, g, b] * n]
-            im_gpu (tensor): img is in cuda, shape: [3, h, w], range: [0, 1]
-            alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque
-        """
-        if self.pil:
-            # convert to numpy first
-            self.im = np.asarray(self.im).copy()
-        if len(masks) == 0:
-            self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255
-        colors = torch.tensor(colors, device=im_gpu.device, dtype=torch.float32) / 255.0
-        colors = colors[:, None, None]  # shape(n,1,1,3)
-        masks = masks.unsqueeze(3)  # shape(n,h,w,1)
-        masks_color = masks * (colors * alpha)  # shape(n,h,w,3)
-
-        inv_alph_masks = (1 - masks * alpha).cumprod(0)  # shape(n,h,w,1)
-        mcs = (masks_color * inv_alph_masks).sum(0) * 2  # mask color summand shape(n,h,w,3)
-
-        im_gpu = im_gpu.flip(dims=[0])  # flip channel
-        im_gpu = im_gpu.permute(1, 2, 0).contiguous()  # shape(h,w,3)
-        im_gpu = im_gpu * inv_alph_masks[-1] + mcs
-        im_mask = (im_gpu * 255).byte().cpu().numpy()
-        self.im[:] = im_mask if retina_masks else scale_image(im_gpu.shape, im_mask, self.im.shape)
-        if self.pil:
-            # convert im back to PIL and update draw
-            self.fromarray(self.im)
-
-    def rectangle(self, xy, fill=None, outline=None, width=1):
-        # Add rectangle to image (PIL-only)
-        self.draw.rectangle(xy, fill, outline, width)
-
-    def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'):
-        # Add text to image (PIL-only)
-        if anchor == 'bottom':  # start y from font bottom
-            w, h = self.font.getsize(text)  # text width, height
-            xy[1] += 1 - h
-        self.draw.text(xy, text, fill=txt_color, font=self.font)
-
-    def fromarray(self, im):
-        # Update self.im from a numpy array
-        self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
-        self.draw = ImageDraw.Draw(self.im)
-
-    def result(self):
-        # Return annotated image as array
-        return np.asarray(self.im)
-
-
 def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')):
     """
     x:              Features to be visualized
@@ -265,7 +150,7 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None):
         x, y = int(w * (i // ns)), int(h * (i % ns))  # block origin
         annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2)  # borders
         if paths:
-            annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220))  # filenames
+            annotator.text([x + 5, y + 5], text=Path(paths[i]).name[:40], txt_color=(220, 220, 220))  # filenames
         if len(targets) > 0:
             ti = targets[targets[:, 0] == i]  # image targets
             boxes = xywh2xyxy(ti[:, 2:6]).T
@@ -500,7 +385,8 @@ def plot_results(file='path/to/results.csv', dir=''):
             for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]):
                 y = data.values[:, j].astype('float')
                 # y[y == 0] = np.nan  # don't show zero values
-                ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)
+                ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)  # actual results
+                ax[i].plot(x, gaussian_filter1d(y, sigma=3), ':', label='smooth', linewidth=2)  # smoothing line
                 ax[i].set_title(s[j], fontsize=12)
                 # if j in [8, 9, 10]:  # share train and val loss y axes
                 #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
diff --git a/utils/segment/augmentations.py b/utils/segment/augmentations.py
index 169adde..f8154b8 100644
--- a/utils/segment/augmentations.py
+++ b/utils/segment/augmentations.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Image augmentation functions
 """
diff --git a/utils/segment/dataloaders.py b/utils/segment/dataloaders.py
index 097a5d5..3ee826d 100644
--- a/utils/segment/dataloaders.py
+++ b/utils/segment/dataloaders.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Dataloaders
 """
diff --git a/utils/segment/metrics.py b/utils/segment/metrics.py
index c9f137e..787961b 100644
--- a/utils/segment/metrics.py
+++ b/utils/segment/metrics.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 Model validation metrics
 """
@@ -196,7 +196,7 @@ KEYS = [
     'val/cls_loss',
     'x/lr0',
     'x/lr1',
-    'x/lr2',]
+    'x/lr2', ]
 
 BEST_KEYS = [
     'best/epoch',
@@ -207,4 +207,4 @@ BEST_KEYS = [
     'best/precision(M)',
     'best/recall(M)',
     'best/mAP_0.5(M)',
-    'best/mAP_0.5:0.95(M)',]
+    'best/mAP_0.5:0.95(M)', ]
diff --git a/utils/segment/plots.py b/utils/segment/plots.py
index 1b22ec8..f9938cd 100644
--- a/utils/segment/plots.py
+++ b/utils/segment/plots.py
@@ -54,7 +54,7 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg'
         x, y = int(w * (i // ns)), int(h * (i % ns))  # block origin
         annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2)  # borders
         if paths:
-            annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220))  # filenames
+            annotator.text([x + 5, y + 5], text=Path(paths[i]).name[:40], txt_color=(220, 220, 220))  # filenames
         if len(targets) > 0:
             idx = targets[:, 0] == i
             ti = targets[idx]  # image targets
diff --git a/utils/torch_utils.py b/utils/torch_utils.py
index 5b67b3f..13a356f 100644
--- a/utils/torch_utils.py
+++ b/utils/torch_utils.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """
 PyTorch utils
 """
@@ -170,7 +170,7 @@ def profile(input, ops, n=10, device=None):
             m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m
             tf, tb, t = 0, 0, [0, 0, 0]  # dt forward, backward
             try:
-                flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2  # GFLOPs
+                flops = thop.profile(m, inputs=(x, ), verbose=False)[0] / 1E9 * 2  # GFLOPs
             except Exception:
                 flops = 0
 
@@ -284,7 +284,7 @@ def model_info(model, verbose=False, imgsz=640):
         p = next(model.parameters())
         stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32  # max stride
         im = torch.empty((1, p.shape[1], stride, stride), device=p.device)  # input image in BCHW format
-        flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1E9 * 2  # stride GFLOPs
+        flops = thop.profile(deepcopy(model), inputs=(im, ), verbose=False)[0] / 1E9 * 2  # stride GFLOPs
         imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz]  # expand if int/float
         fs = f', {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs'  # 640x640 GFLOPs
     except Exception:
diff --git a/utils/triton.py b/utils/triton.py
index 2592802..b5153da 100644
--- a/utils/triton.py
+++ b/utils/triton.py
@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """ Utils to interact with the Triton Inference Server
 """
 
diff --git a/yolov5m320Half.engine b/yolov5m320Half.engine
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