diff --git a/.github/workflows/cla.yml b/.github/workflows/cla.yml
index 59a607a..89183e8 100644
--- a/.github/workflows/cla.yml
+++ b/.github/workflows/cla.yml
@@ -13,6 +13,7 @@ on:
 
 jobs:
   CLA:
+    if: github.repository == 'ultralytics/ultralytics'
     runs-on: ubuntu-latest
     steps:
       - name: "CLA Assistant"
diff --git a/ultralytics/hub/session.py b/ultralytics/hub/session.py
index 817530c..b441beb 100644
--- a/ultralytics/hub/session.py
+++ b/ultralytics/hub/session.py
@@ -7,7 +7,7 @@ import requests
 
 from ultralytics import __version__
 from ultralytics.hub.utils import HUB_API_ROOT, check_dataset_disk_space, smart_request
-from ultralytics.yolo.utils import LOGGER, is_colab, threaded
+from ultralytics.yolo.utils import is_colab, threaded
 
 AGENT_NAME = f'python-{__version__}-colab' if is_colab() else f'python-{__version__}-local'
 
diff --git a/ultralytics/nn/autobackend.py b/ultralytics/nn/autobackend.py
index 661a6fc..6f68da2 100644
--- a/ultralytics/nn/autobackend.py
+++ b/ultralytics/nn/autobackend.py
@@ -32,21 +32,21 @@ class AutoBackend(nn.Module):
             fp16 (bool): If True, use half precision. Default: False
             fuse (bool): Whether to fuse the model or not. Default: True
 
-        Supported formats and their usage:
-            Platform              | Weights Format
-            -----------------------|------------------
-            PyTorch               | *.pt
-            TorchScript           | *.torchscript
-            ONNX Runtime          | *.onnx
-            ONNX OpenCV DNN       | *.onnx --dnn
-            OpenVINO              | *.xml
-            CoreML                | *.mlmodel
-            TensorRT              | *.engine
-            TensorFlow SavedModel | *_saved_model
-            TensorFlow GraphDef   | *.pb
-            TensorFlow Lite       | *.tflite
-            TensorFlow Edge TPU   | *_edgetpu.tflite
-            PaddlePaddle          | *_paddle_model
+        Supported formats and their naming conventions:
+            | Format                | Suffix           |
+            |-----------------------|------------------|
+            | PyTorch               | *.pt             |
+            | TorchScript           | *.torchscript    |
+            | ONNX Runtime          | *.onnx           |
+            | ONNX OpenCV DNN       | *.onnx --dnn     |
+            | OpenVINO              | *.xml            |
+            | CoreML                | *.mlmodel        |
+            | TensorRT              | *.engine         |
+            | TensorFlow SavedModel | *_saved_model    |
+            | TensorFlow GraphDef   | *.pb             |
+            | TensorFlow Lite       | *.tflite         |
+            | TensorFlow Edge TPU   | *_edgetpu.tflite |
+            | PaddlePaddle          | *_paddle_model   |
         """
         super().__init__()
         w = str(weights[0] if isinstance(weights, list) else weights)
@@ -357,7 +357,7 @@ class AutoBackend(nn.Module):
         This function takes a path to a model file and returns the model type
 
         Args:
-          p: path to the model file. Defaults to path/to/model.pt
+            p: path to the model file. Defaults to path/to/model.pt
         """
         # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx
         # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle]
@@ -374,12 +374,11 @@ class AutoBackend(nn.Module):
     @staticmethod
     def _load_metadata(f=Path('path/to/meta.yaml')):
         """
-        > Loads the metadata from a yaml file
+        Loads the metadata from a yaml file
 
         Args:
-          f: The path to the metadata file.
+            f: The path to the metadata file.
         """
-        from ultralytics.yolo.utils.files import yaml_load
 
         # Load metadata from meta.yaml if it exists
         if f.exists():
diff --git a/ultralytics/nn/modules.py b/ultralytics/nn/modules.py
index 9cba6ff..7847593 100644
--- a/ultralytics/nn/modules.py
+++ b/ultralytics/nn/modules.py
@@ -5,28 +5,11 @@ Common modules
 
 import math
 import warnings
-from copy import copy
-from pathlib import Path
 
-import cv2
-import numpy as np
-import pandas as pd
-import requests
 import torch
 import torch.nn as nn
-from PIL import Image, ImageOps
-from torch.cuda import amp
-
-from ultralytics.nn.autobackend import AutoBackend
-from ultralytics.yolo.data.augment import LetterBox
-from ultralytics.yolo.utils import LOGGER, colorstr
-from ultralytics.yolo.utils.files import increment_path
-from ultralytics.yolo.utils.ops import Profile, make_divisible, non_max_suppression, scale_boxes, xyxy2xywh
-from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box
-from ultralytics.yolo.utils.tal import dist2bbox, make_anchors
-from ultralytics.yolo.utils.torch_utils import copy_attr, smart_inference_mode
 
-# from utils.plots import feature_visualization TODO
+from ultralytics.yolo.utils.tal import dist2bbox, make_anchors
 
 
 def autopad(k, p=None, d=1):  # kernel, padding, dilation
@@ -365,216 +348,6 @@ class Concat(nn.Module):
         return torch.cat(x, self.d)
 
 
-class AutoShape(nn.Module):
-    # YOLOv8 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
-    conf = 0.25  # NMS confidence threshold
-    iou = 0.45  # NMS IoU threshold
-    agnostic = False  # NMS class-agnostic
-    multi_label = False  # NMS multiple labels per box
-    classes = None  # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
-    max_det = 1000  # maximum number of detections per image
-    amp = False  # Automatic Mixed Precision (AMP) inference
-
-    def __init__(self, model, verbose=True):
-        super().__init__()
-        if verbose:
-            LOGGER.info('Adding AutoShape... ')
-        copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=())  # copy attributes
-        self.dmb = isinstance(model, AutoBackend)  # DetectMultiBackend() instance
-        self.pt = not self.dmb or model.pt  # PyTorch model
-        self.model = model.eval()
-        if self.pt:
-            m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()
-            m.inplace = False  # Detect.inplace=False for safe multithread inference
-            m.export = True  # do not output loss values
-
-    def _apply(self, fn):
-        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
-        self = super()._apply(fn)
-        if self.pt:
-            m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()
-            m.stride = fn(m.stride)
-            m.grid = list(map(fn, m.grid))
-            if isinstance(m.anchor_grid, list):
-                m.anchor_grid = list(map(fn, m.anchor_grid))
-        return self
-
-    @smart_inference_mode()
-    def forward(self, ims, size=640, augment=False, profile=False):
-        # Inference from various sources. For size(height=640, width=1280), RGB images example inputs are:
-        #   file:        ims = 'data/images/zidane.jpg'  # str or PosixPath
-        #   URI:             = 'https://ultralytics.com/images/zidane.jpg'
-        #   OpenCV:          = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(640,1280,3)
-        #   PIL:             = Image.open('image.jpg') or ImageGrab.grab()  # HWC x(640,1280,3)
-        #   numpy:           = np.zeros((640,1280,3))  # HWC
-        #   torch:           = torch.zeros(16,3,320,640)  # BCHW (scaled to size=640, 0-1 values)
-        #   multiple:        = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...]  # list of images
-
-        dt = (Profile(), Profile(), Profile())
-        with dt[0]:
-            if isinstance(size, int):  # expand
-                size = (size, size)
-            p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device)  # param
-            autocast = self.amp and (p.device.type != 'cpu')  # Automatic Mixed Precision (AMP) inference
-            if isinstance(ims, torch.Tensor):  # torch
-                with amp.autocast(autocast):
-                    return self.model(ims.to(p.device).type_as(p), augment=augment)  # inference
-
-            # Pre-process
-            n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims])  # number, list of images
-            shape0, shape1, files = [], [], []  # image and inference shapes, filenames
-            for i, im in enumerate(ims):
-                f = f'image{i}'  # filename
-                if isinstance(im, (str, Path)):  # filename or uri
-                    im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im
-                    im = np.asarray(ImageOps.exif_transpose(im))
-                elif isinstance(im, Image.Image):  # PIL Image
-                    im, f = np.asarray(ImageOps.exif_transpose(im)), getattr(im, 'filename', f) or f
-                files.append(Path(f).with_suffix('.jpg').name)
-                if im.shape[0] < 5:  # image in CHW
-                    im = im.transpose((1, 2, 0))  # reverse dataloader .transpose(2, 0, 1)
-                im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR)  # enforce 3ch input
-                s = im.shape[:2]  # HWC
-                shape0.append(s)  # image shape
-                g = max(size) / max(s)  # gain
-                shape1.append([y * g for y in s])
-                ims[i] = im if im.data.contiguous else np.ascontiguousarray(im)  # update
-            shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] if self.pt else size  # inf shape
-            x = [LetterBox(shape1, auto=False)(image=im)["img"] for im in ims]  # pad
-            x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2)))  # stack and BHWC to BCHW
-            x = torch.from_numpy(x).to(p.device).type_as(p) / 255  # uint8 to fp16/32
-
-        with amp.autocast(autocast):
-            # Inference
-            with dt[1]:
-                y = self.model(x, augment=augment)  # forward
-
-            # Post-process
-            with dt[2]:
-                y = non_max_suppression(y if self.dmb else y[0],
-                                        self.conf,
-                                        self.iou,
-                                        self.classes,
-                                        self.agnostic,
-                                        self.multi_label,
-                                        max_det=self.max_det)  # NMS
-                for i in range(n):
-                    scale_boxes(shape1, y[i][:, :4], shape0[i])
-
-            return Detections(ims, y, files, dt, self.names, x.shape)
-
-
-class Detections:
-    # YOLOv8 detections class for inference results
-    def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None):
-        super().__init__()
-        d = pred[0].device  # device
-        gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims]  # normalizations
-        self.ims = ims  # list of images as numpy arrays
-        self.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)
-        self.names = names  # class names
-        self.files = files  # image filenames
-        self.times = times  # profiling times
-        self.xyxy = pred  # xyxy pixels
-        self.xywh = [xyxy2xywh(x) for x in pred]  # xywh pixels
-        self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)]  # xyxy normalized
-        self.xywhn = [x / g for x, g in zip(self.xywh, gn)]  # xywh normalized
-        self.n = len(self.pred)  # number of images (batch size)
-        self.t = tuple(x.t / self.n * 1E3 for x in times)  # timestamps (ms)
-        self.s = tuple(shape)  # inference BCHW shape
-
-    def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')):
-        s, crops = '', []
-        for i, (im, pred) in enumerate(zip(self.ims, self.pred)):
-            s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} '  # string
-            if pred.shape[0]:
-                for c in pred[:, -1].unique():
-                    n = (pred[:, -1] == c).sum()  # detections per class
-                    s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "  # add to string
-                s = s.rstrip(', ')
-                if show or save or render or crop:
-                    annotator = Annotator(im, example=str(self.names))
-                    for *box, conf, cls in reversed(pred):  # xyxy, confidence, class
-                        label = f'{self.names[int(cls)]} {conf:.2f}'
-                        if crop:
-                            file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None
-                            crops.append({
-                                'box': box,
-                                'conf': conf,
-                                'cls': cls,
-                                'label': label,
-                                'im': save_one_box(box, im, file=file, save=save)})
-                        else:  # all others
-                            annotator.box_label(box, label if labels else '', color=colors(cls))
-                    im = annotator.im
-            else:
-                s += '(no detections)'
-
-            im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im  # from np
-            if show:
-                im.show(self.files[i])  # show
-            if save:
-                f = self.files[i]
-                im.save(save_dir / f)  # save
-                if i == self.n - 1:
-                    LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}")
-            if render:
-                self.ims[i] = np.asarray(im)
-        if pprint:
-            s = s.lstrip('\n')
-            return f'{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t
-        if crop:
-            if save:
-                LOGGER.info(f'Saved results to {save_dir}\n')
-            return crops
-
-    def show(self, labels=True):
-        self._run(show=True, labels=labels)  # show results
-
-    def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False):
-        save_dir = increment_path(save_dir, exist_ok, mkdir=True)  # increment save_dir
-        self._run(save=True, labels=labels, save_dir=save_dir)  # save results
-
-    def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False):
-        save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None
-        return self._run(crop=True, save=save, save_dir=save_dir)  # crop results
-
-    def render(self, labels=True):
-        self._run(render=True, labels=labels)  # render results
-        return self.ims
-
-    def pandas(self):
-        # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
-        new = copy(self)  # return copy
-        ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name'  # xyxy columns
-        cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name'  # xywh columns
-        for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
-            a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)]  # update
-            setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
-        return new
-
-    def tolist(self):
-        # return a list of Detections objects, i.e. 'for result in results.tolist():'
-        r = range(self.n)  # iterable
-        x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
-        # for d in x:
-        #    for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
-        #        setattr(d, k, getattr(d, k)[0])  # pop out of list
-        return x
-
-    def print(self):
-        LOGGER.info(self.__str__())
-
-    def __len__(self):  # override len(results)
-        return self.n
-
-    def __str__(self):  # override print(results)
-        return self._run(pprint=True)  # print results
-
-    def __repr__(self):
-        return f'YOLOv8 {self.__class__} instance\n' + self.__str__()
-
-
 class Proto(nn.Module):
     # YOLOv8 mask Proto module for segmentation models
     def __init__(self, c1, c_=256, c2=32):  # ch_in, number of protos, number of masks
diff --git a/ultralytics/nn/results.py b/ultralytics/nn/results.py
new file mode 100644
index 0000000..30c6110
--- /dev/null
+++ b/ultralytics/nn/results.py
@@ -0,0 +1,237 @@
+# Ultralytics YOLO 🚀, GPL-3.0 license
+"""
+Common modules
+"""
+
+from copy import copy
+from pathlib import Path
+
+import cv2
+import numpy as np
+import pandas as pd
+import requests
+import torch
+import torch.nn as nn
+from PIL import Image, ImageOps
+from torch.cuda import amp
+
+from ultralytics.nn.autobackend import AutoBackend
+from ultralytics.yolo.data.augment import LetterBox
+from ultralytics.yolo.utils import LOGGER, colorstr
+from ultralytics.yolo.utils.files import increment_path
+from ultralytics.yolo.utils.ops import Profile, make_divisible, non_max_suppression, scale_boxes, xyxy2xywh
+from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box
+from ultralytics.yolo.utils.torch_utils import copy_attr, smart_inference_mode
+
+
+class AutoShape(nn.Module):
+    # YOLOv8 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
+    conf = 0.25  # NMS confidence threshold
+    iou = 0.45  # NMS IoU threshold
+    agnostic = False  # NMS class-agnostic
+    multi_label = False  # NMS multiple labels per box
+    classes = None  # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
+    max_det = 1000  # maximum number of detections per image
+    amp = False  # Automatic Mixed Precision (AMP) inference
+
+    def __init__(self, model, verbose=True):
+        super().__init__()
+        if verbose:
+            LOGGER.info('Adding AutoShape... ')
+        copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=())  # copy attributes
+        self.dmb = isinstance(model, AutoBackend)  # DetectMultiBackend() instance
+        self.pt = not self.dmb or model.pt  # PyTorch model
+        self.model = model.eval()
+        if self.pt:
+            m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()
+            m.inplace = False  # Detect.inplace=False for safe multithread inference
+            m.export = True  # do not output loss values
+
+    def _apply(self, fn):
+        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
+        self = super()._apply(fn)
+        if self.pt:
+            m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()
+            m.stride = fn(m.stride)
+            m.grid = list(map(fn, m.grid))
+            if isinstance(m.anchor_grid, list):
+                m.anchor_grid = list(map(fn, m.anchor_grid))
+        return self
+
+    @smart_inference_mode()
+    def forward(self, ims, size=640, augment=False, profile=False):
+        # Inference from various sources. For size(height=640, width=1280), RGB images example inputs are:
+        #   file:        ims = 'data/images/zidane.jpg'  # str or PosixPath
+        #   URI:             = 'https://ultralytics.com/images/zidane.jpg'
+        #   OpenCV:          = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(640,1280,3)
+        #   PIL:             = Image.open('image.jpg') or ImageGrab.grab()  # HWC x(640,1280,3)
+        #   numpy:           = np.zeros((640,1280,3))  # HWC
+        #   torch:           = torch.zeros(16,3,320,640)  # BCHW (scaled to size=640, 0-1 values)
+        #   multiple:        = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...]  # list of images
+
+        dt = (Profile(), Profile(), Profile())
+        with dt[0]:
+            if isinstance(size, int):  # expand
+                size = (size, size)
+            p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device)  # param
+            autocast = self.amp and (p.device.type != 'cpu')  # Automatic Mixed Precision (AMP) inference
+            if isinstance(ims, torch.Tensor):  # torch
+                with amp.autocast(autocast):
+                    return self.model(ims.to(p.device).type_as(p), augment=augment)  # inference
+
+            # Pre-process
+            n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims])  # number, list of images
+            shape0, shape1, files = [], [], []  # image and inference shapes, filenames
+            for i, im in enumerate(ims):
+                f = f'image{i}'  # filename
+                if isinstance(im, (str, Path)):  # filename or uri
+                    im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im
+                    im = np.asarray(ImageOps.exif_transpose(im))
+                elif isinstance(im, Image.Image):  # PIL Image
+                    im, f = np.asarray(ImageOps.exif_transpose(im)), getattr(im, 'filename', f) or f
+                files.append(Path(f).with_suffix('.jpg').name)
+                if im.shape[0] < 5:  # image in CHW
+                    im = im.transpose((1, 2, 0))  # reverse dataloader .transpose(2, 0, 1)
+                im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR)  # enforce 3ch input
+                s = im.shape[:2]  # HWC
+                shape0.append(s)  # image shape
+                g = max(size) / max(s)  # gain
+                shape1.append([y * g for y in s])
+                ims[i] = im if im.data.contiguous else np.ascontiguousarray(im)  # update
+            shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] if self.pt else size  # inf shape
+            x = [LetterBox(shape1, auto=False)(image=im)["img"] for im in ims]  # pad
+            x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2)))  # stack and BHWC to BCHW
+            x = torch.from_numpy(x).to(p.device).type_as(p) / 255  # uint8 to fp16/32
+
+        with amp.autocast(autocast):
+            # Inference
+            with dt[1]:
+                y = self.model(x, augment=augment)  # forward
+
+            # Post-process
+            with dt[2]:
+                y = non_max_suppression(y if self.dmb else y[0],
+                                        self.conf,
+                                        self.iou,
+                                        self.classes,
+                                        self.agnostic,
+                                        self.multi_label,
+                                        max_det=self.max_det)  # NMS
+                for i in range(n):
+                    scale_boxes(shape1, y[i][:, :4], shape0[i])
+
+            return Detections(ims, y, files, dt, self.names, x.shape)
+
+
+class Detections:
+    # YOLOv8 detections class for inference results
+    def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None):
+        super().__init__()
+        d = pred[0].device  # device
+        gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims]  # normalizations
+        self.ims = ims  # list of images as numpy arrays
+        self.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)
+        self.names = names  # class names
+        self.files = files  # image filenames
+        self.times = times  # profiling times
+        self.xyxy = pred  # xyxy pixels
+        self.xywh = [xyxy2xywh(x) for x in pred]  # xywh pixels
+        self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)]  # xyxy normalized
+        self.xywhn = [x / g for x, g in zip(self.xywh, gn)]  # xywh normalized
+        self.n = len(self.pred)  # number of images (batch size)
+        self.t = tuple(x.t / self.n * 1E3 for x in times)  # timestamps (ms)
+        self.s = tuple(shape)  # inference BCHW shape
+
+    def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')):
+        s, crops = '', []
+        for i, (im, pred) in enumerate(zip(self.ims, self.pred)):
+            s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} '  # string
+            if pred.shape[0]:
+                for c in pred[:, -1].unique():
+                    n = (pred[:, -1] == c).sum()  # detections per class
+                    s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "  # add to string
+                s = s.rstrip(', ')
+                if show or save or render or crop:
+                    annotator = Annotator(im, example=str(self.names))
+                    for *box, conf, cls in reversed(pred):  # xyxy, confidence, class
+                        label = f'{self.names[int(cls)]} {conf:.2f}'
+                        if crop:
+                            file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None
+                            crops.append({
+                                'box': box,
+                                'conf': conf,
+                                'cls': cls,
+                                'label': label,
+                                'im': save_one_box(box, im, file=file, save=save)})
+                        else:  # all others
+                            annotator.box_label(box, label if labels else '', color=colors(cls))
+                    im = annotator.im
+            else:
+                s += '(no detections)'
+
+            im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im  # from np
+            if show:
+                im.show(self.files[i])  # show
+            if save:
+                f = self.files[i]
+                im.save(save_dir / f)  # save
+                if i == self.n - 1:
+                    LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}")
+            if render:
+                self.ims[i] = np.asarray(im)
+        if pprint:
+            s = s.lstrip('\n')
+            return f'{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t
+        if crop:
+            if save:
+                LOGGER.info(f'Saved results to {save_dir}\n')
+            return crops
+
+    def show(self, labels=True):
+        self._run(show=True, labels=labels)  # show results
+
+    def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False):
+        save_dir = increment_path(save_dir, exist_ok, mkdir=True)  # increment save_dir
+        self._run(save=True, labels=labels, save_dir=save_dir)  # save results
+
+    def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False):
+        save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None
+        return self._run(crop=True, save=save, save_dir=save_dir)  # crop results
+
+    def render(self, labels=True):
+        self._run(render=True, labels=labels)  # render results
+        return self.ims
+
+    def pandas(self):
+        # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
+        new = copy(self)  # return copy
+        ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name'  # xyxy columns
+        cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name'  # xywh columns
+        for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
+            a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)]  # update
+            setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
+        return new
+
+    def tolist(self):
+        # return a list of Detections objects, i.e. 'for result in results.tolist():'
+        r = range(self.n)  # iterable
+        x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
+        # for d in x:
+        #    for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
+        #        setattr(d, k, getattr(d, k)[0])  # pop out of list
+        return x
+
+    def print(self):
+        LOGGER.info(self.__str__())
+
+    def __len__(self):  # override len(results)
+        return self.n
+
+    def __str__(self):  # override print(results)
+        return self._run(pprint=True)  # print results
+
+    def __repr__(self):
+        return f'YOLOv8 {self.__class__} instance\n' + self.__str__()
+
+
+print('works')
diff --git a/ultralytics/nn/tasks.py b/ultralytics/nn/tasks.py
index b676294..c3ec574 100644
--- a/ultralytics/nn/tasks.py
+++ b/ultralytics/nn/tasks.py
@@ -57,7 +57,7 @@ class BaseModel(nn.Module):
             x = m(x)  # run
             y.append(x if m.i in self.save else None)  # save output
             if visualize:
-                pass
+                LOGGER.info('visualize feature not yet supported')
                 # TODO: feature_visualization(x, m.type, m.i, save_dir=visualize)
         return x
 
@@ -106,8 +106,8 @@ class BaseModel(nn.Module):
         Prints model information
 
         Args:
-          verbose (bool): if True, prints out the model information. Defaults to False
-          imgsz (int): the size of the image that the model will be trained on. Defaults to 640
+            verbose (bool): if True, prints out the model information. Defaults to False
+            imgsz (int): the size of the image that the model will be trained on. Defaults to 640
         """
         model_info(self, verbose, imgsz)
 
@@ -117,10 +117,10 @@ class BaseModel(nn.Module):
         parameters or registered buffers
 
         Args:
-          fn: the function to apply to the model
+            fn: the function to apply to the model
 
         Returns:
-          A model that is a Detect() object.
+            A model that is a Detect() object.
         """
         self = super()._apply(fn)
         m = self.model[-1]  # Detect()
@@ -135,7 +135,7 @@ class BaseModel(nn.Module):
         This function loads the weights of the model from a file
 
         Args:
-          weights (str): The weights to load into the model.
+            weights (str): The weights to load into the model.
         """
         # Force all tasks to implement this function
         raise NotImplementedError("This function needs to be implemented by derived classes!")
diff --git a/ultralytics/yolo/engine/model.py b/ultralytics/yolo/engine/model.py
index f55394a..18a5313 100644
--- a/ultralytics/yolo/engine/model.py
+++ b/ultralytics/yolo/engine/model.py
@@ -32,7 +32,7 @@ class YOLO:
 
     def __init__(self, model='yolov8n.yaml', type="v8") -> None:
         """
-        > Initializes the YOLO object.
+        Initializes the YOLO object.
 
         Args:
             model (str, Path): model to load or create
@@ -59,7 +59,7 @@ class YOLO:
 
     def _new(self, cfg: str, verbose=True):
         """
-        > Initializes a new model and infers the task type from the model definitions.
+        Initializes a new model and infers the task type from the model definitions.
 
         Args:
             cfg (str): model configuration file
@@ -75,7 +75,7 @@ class YOLO:
 
     def _load(self, weights: str):
         """
-        > Initializes a new model and infers the task type from the model head.
+        Initializes a new model and infers the task type from the model head.
 
         Args:
             weights (str): model checkpoint to be loaded
@@ -90,7 +90,7 @@ class YOLO:
 
     def reset(self):
         """
-        > Resets the model modules.
+        Resets the model modules.
         """
         for m in self.model.modules():
             if hasattr(m, 'reset_parameters'):
@@ -100,7 +100,7 @@ class YOLO:
 
     def info(self, verbose=False):
         """
-        > Logs model info.
+        Logs model info.
 
         Args:
             verbose (bool): Controls verbosity.
@@ -133,7 +133,7 @@ class YOLO:
     @smart_inference_mode()
     def val(self, data=None, **kwargs):
         """
-        > Validate a model on a given dataset .
+        Validate a model on a given dataset .
 
         Args:
             data (str): The dataset to validate on. Accepts all formats accepted by yolo
@@ -152,7 +152,7 @@ class YOLO:
     @smart_inference_mode()
     def export(self, **kwargs):
         """
-        > Export model.
+        Export model.
 
         Args:
             **kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in docs
@@ -168,7 +168,7 @@ class YOLO:
 
     def train(self, **kwargs):
         """
-        > Trains the model on a given dataset.
+        Trains the model on a given dataset.
 
         Args:
             **kwargs (Any): Any number of arguments representing the training configuration. List of all args can be found in 'config' section.
@@ -197,7 +197,7 @@ class YOLO:
 
     def to(self, device):
         """
-        > Sends the model to the given device.
+        Sends the model to the given device.
 
         Args:
             device (str): device
diff --git a/ultralytics/yolo/engine/predictor.py b/ultralytics/yolo/engine/predictor.py
index 58495c1..1ad0021 100644
--- a/ultralytics/yolo/engine/predictor.py
+++ b/ultralytics/yolo/engine/predictor.py
@@ -89,7 +89,7 @@ class BasePredictor:
         self.vid_path, self.vid_writer = None, None
         self.annotator = None
         self.data_path = None
-        self.output = dict()
+        self.output = {}
         self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()})  # add callbacks
         callbacks.add_integration_callbacks(self)
 
@@ -216,7 +216,7 @@ class BasePredictor:
         self.run_callbacks("on_predict_end")
 
     def predict_cli(self, source=None, model=None, return_outputs=False):
-        # as __call__ is a genertor now so have to treat it like a genertor
+        # as __call__ is a generator now so have to treat it like a generator
         for _ in (self.__call__(source, model, return_outputs)):
             pass
 
diff --git a/ultralytics/yolo/engine/trainer.py b/ultralytics/yolo/engine/trainer.py
index d107291..5c6f262 100644
--- a/ultralytics/yolo/engine/trainer.py
+++ b/ultralytics/yolo/engine/trainer.py
@@ -40,7 +40,7 @@ class BaseTrainer:
     """
     BaseTrainer
 
-    > A base class for creating trainers.
+    A base class for creating trainers.
 
     Attributes:
         args (OmegaConf): Configuration for the trainer.
@@ -75,7 +75,7 @@ class BaseTrainer:
 
     def __init__(self, config=DEFAULT_CONFIG, overrides=None):
         """
-        > Initializes the BaseTrainer class.
+        Initializes the BaseTrainer class.
 
         Args:
             config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
@@ -149,13 +149,13 @@ class BaseTrainer:
 
     def add_callback(self, event: str, callback):
         """
-        > Appends the given callback.
+        Appends the given callback.
         """
         self.callbacks[event].append(callback)
 
     def set_callback(self, event: str, callback):
         """
-        > Overrides the existing callbacks with the given callback.
+        Overrides the existing callbacks with the given callback.
         """
         self.callbacks[event] = [callback]
 
@@ -194,7 +194,7 @@ class BaseTrainer:
 
     def _setup_train(self, rank, world_size):
         """
-        > Builds dataloaders and optimizer on correct rank process.
+        Builds dataloaders and optimizer on correct rank process.
         """
         # model
         self.run_callbacks("on_pretrain_routine_start")
@@ -383,13 +383,13 @@ class BaseTrainer:
 
     def get_dataset(self, data):
         """
-        > Get train, val path from data dict if it exists. Returns None if data format is not recognized.
+        Get train, val path from data dict if it exists. Returns None if data format is not recognized.
         """
         return data["train"], data.get("val") or data.get("test")
 
     def setup_model(self):
         """
-        > load/create/download model for any task.
+        load/create/download model for any task.
         """
         if isinstance(self.model, torch.nn.Module):  # if model is loaded beforehand. No setup needed
             return
@@ -415,13 +415,13 @@ class BaseTrainer:
 
     def preprocess_batch(self, batch):
         """
-        > Allows custom preprocessing model inputs and ground truths depending on task type.
+        Allows custom preprocessing model inputs and ground truths depending on task type.
         """
         return batch
 
     def validate(self):
         """
-        > Runs validation on test set using self.validator. The returned dict is expected to contain "fitness" key.
+        Runs validation on test set using self.validator. The returned dict is expected to contain "fitness" key.
         """
         metrics = self.validator(self)
         fitness = metrics.pop("fitness", -self.loss.detach().cpu().numpy())  # use loss as fitness measure if not found
@@ -431,7 +431,7 @@ class BaseTrainer:
 
     def log(self, text, rank=-1):
         """
-        > Logs the given text to given ranks process if provided, otherwise logs to all ranks.
+        Logs the given text to given ranks process if provided, otherwise logs to all ranks.
 
         Args"
             text (str): text to log
@@ -449,13 +449,13 @@ class BaseTrainer:
 
     def get_dataloader(self, dataset_path, batch_size=16, rank=0):
         """
-        > Returns dataloader derived from torch.data.Dataloader.
+        Returns dataloader derived from torch.data.Dataloader.
         """
         raise NotImplementedError("get_dataloader function not implemented in trainer")
 
     def criterion(self, preds, batch):
         """
-        > Returns loss and individual loss items as Tensor.
+        Returns loss and individual loss items as Tensor.
         """
         raise NotImplementedError("criterion function not implemented in trainer")
 
@@ -543,7 +543,7 @@ class BaseTrainer:
     @staticmethod
     def build_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5):
         """
-        > Builds an optimizer with the specified parameters and parameter groups.
+        Builds an optimizer with the specified parameters and parameter groups.
 
         Args:
             model (nn.Module): model to optimize
diff --git a/ultralytics/yolo/utils/callbacks/comet.py b/ultralytics/yolo/utils/callbacks/comet.py
index 7133cbb..0f6d4f2 100644
--- a/ultralytics/yolo/utils/callbacks/comet.py
+++ b/ultralytics/yolo/utils/callbacks/comet.py
@@ -10,7 +10,7 @@ except (ModuleNotFoundError, ImportError):
 
 
 def on_pretrain_routine_start(trainer):
-    experiment = comet_ml.Experiment(project_name=trainer.args.project or "YOLOv8",)
+    experiment = comet_ml.Experiment(project_name=trainer.args.project or "YOLOv8")
     experiment.log_parameters(dict(trainer.args))
 
 
diff --git a/ultralytics/yolo/utils/downloads.py b/ultralytics/yolo/utils/downloads.py
index f2bfc53..263845c 100644
--- a/ultralytics/yolo/utils/downloads.py
+++ b/ultralytics/yolo/utils/downloads.py
@@ -12,7 +12,7 @@ from zipfile import ZipFile
 import requests
 import torch
 
-from ultralytics.yolo.utils import LOGGER
+from ultralytics.yolo.utils import LOGGER, SETTINGS
 
 
 def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''):
@@ -59,7 +59,11 @@ def attempt_download(file, repo='ultralytics/assets', release='v0.0.0'):
         return response['tag_name'], [x['name'] for x in response['assets']]  # tag, assets
 
     file = Path(str(file).strip().replace("'", ''))
-    if not file.exists():
+    if file.exists():
+        return str(file)
+    elif (SETTINGS['weights_dir'] / file).exists():
+        return str(SETTINGS['weights_dir'] / file)
+    else:
         # URL specified
         name = Path(urllib.parse.unquote(str(file))).name  # decode '%2F' to '/' etc.
         if str(file).startswith(('http:/', 'https:/')):  # download
@@ -94,7 +98,7 @@ def attempt_download(file, repo='ultralytics/assets', release='v0.0.0'):
                 min_bytes=1E5,
                 error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/{tag} or {url3}')
 
-    return str(file)
+        return str(file)
 
 
 def download(url, dir=Path.cwd(), unzip=True, delete=True, curl=False, threads=1, retry=3):
diff --git a/ultralytics/yolo/v8/classify/predict.py b/ultralytics/yolo/v8/classify/predict.py
index e5a88f4..f4f8cfe 100644
--- a/ultralytics/yolo/v8/classify/predict.py
+++ b/ultralytics/yolo/v8/classify/predict.py
@@ -58,10 +58,9 @@ class ClassificationPredictor(BasePredictor):
 
 @hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
 def predict(cfg):
-    cfg.model = cfg.model or "squeezenet1_0"
+    cfg.model = cfg.model or "yolov8n-cls.pt"  # or "resnet18"
     cfg.imgsz = check_imgsz(cfg.imgsz, min_dim=2)  # check image size
     cfg.source = cfg.source if cfg.source is not None else ROOT / "assets"
-
     predictor = ClassificationPredictor(cfg)
     predictor.predict_cli()
 
diff --git a/ultralytics/yolo/v8/classify/train.py b/ultralytics/yolo/v8/classify/train.py
index 237ae8e..aca9703 100644
--- a/ultralytics/yolo/v8/classify/train.py
+++ b/ultralytics/yolo/v8/classify/train.py
@@ -136,7 +136,7 @@ class ClassificationTrainer(BaseTrainer):
 
 @hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
 def train(cfg):
-    cfg.model = cfg.model or "yolov8n-cls.yaml"  # or "resnet18"
+    cfg.model = cfg.model or "yolov8n-cls.pt"  # or "resnet18"
     cfg.data = cfg.data or "mnist160"  # or yolo.ClassificationDataset("mnist")
     cfg.lr0 = 0.1
     cfg.weight_decay = 5e-5
@@ -151,10 +151,4 @@ def train(cfg):
 
 
 if __name__ == "__main__":
-    """
-    yolo task=classify mode=train model=yolov8n-cls.pt data=mnist160 epochs=10 imgsz=32
-    yolo task=classify mode=val model=runs/classify/train/weights/last.pt data=mnist160 imgsz=32
-    yolo task=classify mode=predict model=runs/classify/train/weights/last.pt imgsz=32 source=ultralytics/assets/bus.jpg
-    yolo mode=export model=runs/classify/train/weights/last.pt imgsz=32 format=torchscript
-    """
     train()
diff --git a/ultralytics/yolo/v8/classify/val.py b/ultralytics/yolo/v8/classify/val.py
index a9895d4..f33ecdf 100644
--- a/ultralytics/yolo/v8/classify/val.py
+++ b/ultralytics/yolo/v8/classify/val.py
@@ -48,8 +48,8 @@ class ClassificationValidator(BaseValidator):
 
 @hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
 def val(cfg):
+    cfg.model = cfg.model or "yolov8n-cls.pt"  # or "resnet18"
     cfg.data = cfg.data or "imagenette160"
-    cfg.model = cfg.model or "resnet18"
     validator = ClassificationValidator(args=cfg)
     validator(model=cfg.model)
 
diff --git a/ultralytics/yolo/v8/detect/train.py b/ultralytics/yolo/v8/detect/train.py
index 817bca9..ea817a9 100644
--- a/ultralytics/yolo/v8/detect/train.py
+++ b/ultralytics/yolo/v8/detect/train.py
@@ -197,7 +197,7 @@ class Loss:
 
 @hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
 def train(cfg):
-    cfg.model = cfg.model or "yolov8n.yaml"
+    cfg.model = cfg.model or "yolov8n.pt"
     cfg.data = cfg.data or "coco128.yaml"  # or yolo.ClassificationDataset("mnist")
     cfg.device = cfg.device if cfg.device is not None else ''
     # trainer = DetectionTrainer(cfg)
@@ -208,11 +208,4 @@ def train(cfg):
 
 
 if __name__ == "__main__":
-    """
-    CLI usage:
-    python ultralytics/yolo/v8/detect/train.py model=yolov8n.yaml data=coco128 epochs=100 imgsz=640
-
-    TODO:
-    yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=100
-    """
     train()
diff --git a/ultralytics/yolo/v8/detect/val.py b/ultralytics/yolo/v8/detect/val.py
index 7eac253..6ec867d 100644
--- a/ultralytics/yolo/v8/detect/val.py
+++ b/ultralytics/yolo/v8/detect/val.py
@@ -234,6 +234,7 @@ class DetectionValidator(BaseValidator):
 
 @hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
 def val(cfg):
+    cfg.model = cfg.model or "yolov8n.pt"
     cfg.data = cfg.data or "coco128.yaml"
     validator = DetectionValidator(args=cfg)
     validator(model=cfg.model)
diff --git a/ultralytics/yolo/v8/segment/train.py b/ultralytics/yolo/v8/segment/train.py
index be41f16..845f3dc 100644
--- a/ultralytics/yolo/v8/segment/train.py
+++ b/ultralytics/yolo/v8/segment/train.py
@@ -143,7 +143,7 @@ class SegLoss(Loss):
 
 @hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
 def train(cfg):
-    cfg.model = cfg.model or "yolov8n-seg.yaml"
+    cfg.model = cfg.model or "yolov8n-seg.pt"
     cfg.data = cfg.data or "coco128-seg.yaml"  # or yolo.ClassificationDataset("mnist")
     cfg.device = cfg.device if cfg.device is not None else ''
     # trainer = SegmentationTrainer(cfg)
@@ -154,11 +154,4 @@ def train(cfg):
 
 
 if __name__ == "__main__":
-    """
-    CLI usage:
-    python ultralytics/yolo/v8/segment/train.py model=yolov8n-seg.yaml data=coco128-segments epochs=100 imgsz=640
-
-    TODO:
-    Direct cli support, i.e, yolov8 classify_train args.epochs 10
-    """
     train()
diff --git a/ultralytics/yolo/v8/segment/val.py b/ultralytics/yolo/v8/segment/val.py
index 32b8a9e..762f540 100644
--- a/ultralytics/yolo/v8/segment/val.py
+++ b/ultralytics/yolo/v8/segment/val.py
@@ -114,8 +114,9 @@ class SegmentationValidator(DetectionValidator):
                                                     masks=True)
                 if self.args.plots:
                     self.confusion_matrix.process_batch(predn, labelsn)
-            self.stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:,
-                                                                               5], cls.squeeze(-1)))  # conf, pcls, tcls
+
+            # Append correct_masks, correct_boxes, pconf, pcls, tcls
+            self.stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:, 5], cls.squeeze(-1)))
 
             pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)
             if self.args.plots and self.batch_i < 3: