ultralytics 8.0.197 save P, R, F1 curves to metrics (#5354)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: erminkev1 <83356055+erminkev1@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Andy <39454881+yermandy@users.noreply.github.com>

.pre-commit-config.yaml

@@ -11,7 +11,7 @@ ci:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.5.0
     hooks:
       - id: end-of-file-fixer
       - id: trailing-whitespace
@@ -22,7 +22,7 @@ repos:
       - id: detect-private-key
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.14.0
+    rev: v3.15.0
     hooks:
       - id: pyupgrade
         name: Upgrade code
@@ -77,3 +77,8 @@ repos:
 #    rev: v1.5.0
 #    hooks:
 #    -   id: dead
+
+#  - repo: https://github.com/ultralytics/pre-commit
+#    rev: bd60a414f80a53fb8f593d3bfed4701fc47e4b23
+#    hooks:
+#      - id: capitalize-comments

docs/build_reference.py

@@ -6,7 +6,6 @@ reference section of *.md files composed of classes and functions, and also crea
 Note: Must be run from repository root directory. Do not run from docs directory.
 """
 
-import os
 import re
 from collections import defaultdict
 from pathlib import Path
@@ -18,17 +17,9 @@ CODE_DIR = ROOT
 REFERENCE_DIR = ROOT.parent / 'docs/reference'
 
 
-def extract_classes_and_functions(filepath: Path):
-    """
-    Extracts class and function names from a given Python file.
-
-    Args:
-        filepath (Path): The path to the Python file.
-
-    Returns:
-        (tuple): A tuple containing lists of class and function names.
-    """
-    content = Path(filepath).read_text()
+def extract_classes_and_functions(filepath: Path) -> tuple:
+    """Extracts class and function names from a given Python file."""
+    content = filepath.read_text()
     class_pattern = r'(?:^|\n)class\s(\w+)(?:\(|:)'
     func_pattern = r'(?:^|\n)def\s(\w+)\('
 
@@ -39,22 +30,13 @@ def extract_classes_and_functions(filepath: Path):
 
 
 def create_markdown(py_filepath: Path, module_path: str, classes: list, functions: list):
-    """
-    Creates a Markdown file containing the API reference for the given Python module.
-
-    Args:
-        py_filepath (Path): The path to the Python file.
-        module_path (str): The import path for the Python module.
-        classes (list): A list of class names within the module.
-        functions (list): A list of function names within the module.
-    """
+    """Creates a Markdown file containing the API reference for the given Python module."""
     md_filepath = py_filepath.with_suffix('.md')
 
     # Read existing content and keep header content between first two ---
     header_content = ''
     if md_filepath.exists():
-        with open(md_filepath) as file:
-            existing_content = file.read()
+        existing_content = md_filepath.read_text()
         header_parts = existing_content.split('---')
         for part in header_parts:
             if 'description:' in part or 'comments:' in part:
@@ -74,43 +56,24 @@ def create_markdown(py_filepath: Path, module_path: str, classes: list, function
     if not md_content.endswith('\n'):
         md_content += '\n'
 
-    os.makedirs(os.path.dirname(md_filepath), exist_ok=True)
-    with open(md_filepath, 'w') as file:
-        file.write(md_content)
+    md_filepath.parent.mkdir(parents=True, exist_ok=True)
+    md_filepath.write_text(md_content)
 
     return md_filepath.relative_to(NEW_YAML_DIR)
 
 
-def nested_dict():
-    """
-    Creates and returns a nested defaultdict.
-
-    Returns:
-        (defaultdict): A nested defaultdict object.
-    """
+def nested_dict() -> defaultdict:
+    """Creates and returns a nested defaultdict."""
     return defaultdict(nested_dict)
 
 
-def sort_nested_dict(d: dict):
-    """
-    Sorts a nested dictionary recursively.
-
-    Args:
-        d (dict): The dictionary to sort.
-
-    Returns:
-        (dict): The sorted dictionary.
-    """
+def sort_nested_dict(d: dict) -> dict:
+    """Sorts a nested dictionary recursively."""
     return {key: sort_nested_dict(value) if isinstance(value, dict) else value for key, value in sorted(d.items())}
 
 
 def create_nav_menu_yaml(nav_items: list):
-    """
-    Creates a YAML file for the navigation menu based on the provided list of items.
-
-    Args:
-        nav_items (list): A list of relative file paths to Markdown files for the navigation menu.
-    """
+    """Creates a YAML file for the navigation menu based on the provided list of items."""
     nav_tree = nested_dict()
 
     for item_str in nav_items:
@@ -136,18 +99,18 @@ def create_nav_menu_yaml(nav_items: list):
                 yaml_str += f"{indent}- {k}: {str(v).replace('docs/', '')}\n"
         return yaml_str
 
-    with open(NEW_YAML_DIR / 'nav_menu_updated.yml', 'w') as file:
-        yaml_str = _dict_to_yaml(nav_tree_sorted)
-        file.write(yaml_str)
+    # Print updated YAML reference section
+    print('Scan complete, new mkdocs.yaml reference section is:\n\n', _dict_to_yaml(nav_tree_sorted))
+
+    # Save new YAML reference section
+    # (NEW_YAML_DIR / 'nav_menu_updated.yml').write_text(_dict_to_yaml(nav_tree_sorted))
 
 
 def main():
     """Main function to extract class and function names, create Markdown files, and generate a YAML navigation menu."""
     nav_items = []
-    for root, _, files in os.walk(CODE_DIR):
-        for file in files:
-            if file.endswith('.py'):
-                py_filepath = Path(root) / file
+
+    for py_filepath in CODE_DIR.rglob('*.py'):
         classes, functions = extract_classes_and_functions(py_filepath)
 
         if classes or functions:

docs/reference/utils/callbacks/wb.md

@@ -9,6 +9,14 @@ keywords: Ultralytics, callbacks, _log_plots, on_fit_epoch_end, on_train_end
 
     Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/wb.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/wb.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
 
+---
+## ::: ultralytics.utils.callbacks.wb._custom_table
+<br><br>
+
+---
+## ::: ultralytics.utils.callbacks.wb._plot_curve
+<br><br>
+
 ---
 ## ::: ultralytics.utils.callbacks.wb._log_plots
 <br><br>

examples/YOLOv8-Region-Counter/yolov8_region_counter.py

@@ -11,7 +11,7 @@ from ultralytics import YOLO
 from ultralytics.utils.files import increment_path
 from ultralytics.utils.plotting import Annotator, colors
 
-track_history = defaultdict(lambda: [])
+track_history = defaultdict(list)
 
 current_region = None
 counting_regions = [

tests/test_cli.py

@@ -97,8 +97,8 @@ def test_fastsam(task='segment', model=WEIGHTS_DIR / 'FastSAM-s.pt', data='coco8
     ann = prompt_process.text_prompt(text='a photo of a dog')
 
     # Point prompt
-    # points default [[0,0]] [[x1,y1],[x2,y2]]
-    # point_label default [0] [1,0] 0:background, 1:foreground
+    # Points default [[0,0]] [[x1,y1],[x2,y2]]
+    # Point_label default [0] [1,0] 0:background, 1:foreground
     ann = prompt_process.point_prompt(points=[[200, 200]], pointlabel=[1])
     prompt_process.plot(annotations=ann, output='./')
 

tests/test_engine.py

@@ -70,7 +70,7 @@ def test_segment():
     CFG.imgsz = 32
     # YOLO(CFG_SEG).train(**overrides)  # works
 
-    # trainer
+    # Trainer
     trainer = segment.SegmentationTrainer(overrides=overrides)
     trainer.add_callback('on_train_start', test_func)
     assert test_func in trainer.callbacks['on_train_start'], 'callback test failed'

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.196'
+__version__ = '8.0.197'
 
 from ultralytics.models import RTDETR, SAM, YOLO
 from ultralytics.models.fastsam import FastSAM

ultralytics/data/augment.py

@@ -491,7 +491,7 @@ class RandomPerspective:
         border = labels.pop('mosaic_border', self.border)
         self.size = img.shape[1] + border[1] * 2, img.shape[0] + border[0] * 2  # w, h
         # M is affine matrix
-        # scale for func:`box_candidates`
+        # Scale for func:`box_candidates`
         img, M, scale = self.affine_transform(img, border)
 
         bboxes = self.apply_bboxes(instances.bboxes, M)
@@ -894,7 +894,7 @@ class Format:
         return labels
 
     def _format_img(self, img):
-        """Format the image for YOLOv5 from Numpy array to PyTorch tensor."""
+        """Format the image for YOLO from Numpy array to PyTorch tensor."""
         if len(img.shape) < 3:
             img = np.expand_dims(img, -1)
         img = np.ascontiguousarray(img.transpose(2, 0, 1)[::-1])

ultralytics/data/converter.py

@@ -1,14 +1,14 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
 import json
-import shutil
 from collections import defaultdict
 from pathlib import Path
 
 import cv2
 import numpy as np
 
-from ultralytics.utils import TQDM
+from ultralytics.utils import LOGGER, TQDM
+from ultralytics.utils.files import increment_path
 
 
 def coco91_to_coco80_class():
@@ -48,12 +48,12 @@ def coco80_to_coco91_class():  #
 
 
 def convert_coco(labels_dir='../coco/annotations/',
-                 save_dir='.',
+                 save_dir='coco_converted/',
                  use_segments=False,
                  use_keypoints=False,
                  cls91to80=True):
     """
-    Converts COCO dataset annotations to a format suitable for training YOLOv5 models.
+    Converts COCO dataset annotations to a YOLO annotation format  suitable for training YOLO models.
 
     Args:
         labels_dir (str, optional): Path to directory containing COCO dataset annotation files.
@@ -74,9 +74,7 @@ def convert_coco(labels_dir='../coco/annotations/',
     """
 
     # Create dataset directory
-    save_dir = Path(save_dir)
-    if save_dir.exists():
-        shutil.rmtree(save_dir)  # delete dir
+    save_dir = increment_path(save_dir)  # increment if save directory already exists
     for p in save_dir / 'labels', save_dir / 'images':
         p.mkdir(parents=True, exist_ok=True)  # make dir
 
@@ -147,6 +145,8 @@ def convert_coco(labels_dir='../coco/annotations/',
                                  if use_segments and len(segments[i]) > 0 else bboxes[i]),  # cls, box or segments
                     file.write(('%g ' * len(line)).rstrip() % line + '\n')
 
+    LOGGER.info(f'COCO data converted successfully.\nResults saved to {save_dir.resolve()}')
+
 
 def convert_dota_to_yolo_obb(dota_root_path: str):
     """
@@ -271,26 +271,25 @@ def merge_multi_segment(segments):
     segments = [np.array(i).reshape(-1, 2) for i in segments]
     idx_list = [[] for _ in range(len(segments))]
 
-    # record the indexes with min distance between each segment
+    # Record the indexes with min distance between each segment
     for i in range(1, len(segments)):
         idx1, idx2 = min_index(segments[i - 1], segments[i])
         idx_list[i - 1].append(idx1)
         idx_list[i].append(idx2)
 
-    # use two round to connect all the segments
+    # Use two round to connect all the segments
     for k in range(2):
-        # forward connection
+        # Forward connection
         if k == 0:
             for i, idx in enumerate(idx_list):
-                # middle segments have two indexes
-                # reverse the index of middle segments
+                # Middle segments have two indexes, reverse the index of middle segments
                 if len(idx) == 2 and idx[0] > idx[1]:
                     idx = idx[::-1]
                     segments[i] = segments[i][::-1, :]
 
                 segments[i] = np.roll(segments[i], -idx[0], axis=0)
                 segments[i] = np.concatenate([segments[i], segments[i][:1]])
-                # deal with the first segment and the last one
+                # Deal with the first segment and the last one
                 if i in [0, len(idx_list) - 1]:
                     s.append(segments[i])
                 else:

ultralytics/data/dataset.py

@@ -162,7 +162,7 @@ class YOLODataset(BaseDataset):
     def update_labels_info(self, label):
         """Custom your label format here."""
         # NOTE: cls is not with bboxes now, classification and semantic segmentation need an independent cls label
-        # we can make it also support classification and semantic segmentation by add or remove some dict keys there.
+        # We can make it also support classification and semantic segmentation by add or remove some dict keys there.
         bboxes = label.pop('bboxes')
         segments = label.pop('segments')
         keypoints = label.pop('keypoints', None)

ultralytics/engine/trainer.py

@@ -526,7 +526,7 @@ class BaseTrainer:
 
     # TODO: may need to put these following functions into callback
     def plot_training_samples(self, batch, ni):
-        """Plots training samples during YOLOv5 training."""
+        """Plots training samples during YOLO training."""
         pass
 
     def plot_training_labels(self):

ultralytics/hub/session.py

@@ -23,7 +23,7 @@ class HUBTrainingSession:
 
     Attributes:
         agent_id (str): Identifier for the instance communicating with the server.
-        model_id (str): Identifier for the YOLOv5 model being trained.
+        model_id (str): Identifier for the YOLO model being trained.
         model_url (str): URL for the model in Ultralytics HUB.
         api_url (str): API URL for the model in Ultralytics HUB.
         auth_header (dict): Authentication header for the Ultralytics HUB API requests.

ultralytics/models/fastsam/prompt.py

@@ -120,7 +120,7 @@ class FastSAMPrompt:
             result_name = os.path.basename(ann.path)
             image = ann.orig_img[..., ::-1]  # BGR to RGB
             original_h, original_w = ann.orig_shape
-            # for macOS only
+            # For macOS only
             # plt.switch_backend('TkAgg')
             plt.figure(figsize=(original_w / 100, original_h / 100))
             # Add subplot with no margin.

ultralytics/models/fastsam/utils.py

@@ -42,23 +42,23 @@ def bbox_iou(box1, boxes, iou_thres=0.9, image_shape=(640, 640), raw_output=Fals
         high_iou_indices (torch.Tensor): Indices of boxes with IoU > thres
     """
     boxes = adjust_bboxes_to_image_border(boxes, image_shape)
-    # obtain coordinates for intersections
+    # Obtain coordinates for intersections
     x1 = torch.max(box1[0], boxes[:, 0])
     y1 = torch.max(box1[1], boxes[:, 1])
     x2 = torch.min(box1[2], boxes[:, 2])
     y2 = torch.min(box1[3], boxes[:, 3])
 
-    # compute the area of intersection
+    # Compute the area of intersection
     intersection = (x2 - x1).clamp(0) * (y2 - y1).clamp(0)
 
-    # compute the area of both individual boxes
+    # Compute the area of both individual boxes
     box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1])
     box2_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
 
-    # compute the area of union
+    # Compute the area of union
     union = box1_area + box2_area - intersection
 
-    # compute the IoU
+    # Compute the IoU
     iou = intersection / union  # Should be shape (n, )
     if raw_output:
         return 0 if iou.numel() == 0 else iou

ultralytics/models/rtdetr/val.py

@@ -99,10 +99,10 @@ class RTDETRValidator(DetectionValidator):
         for i, bbox in enumerate(bboxes):  # (300, 4)
             bbox = ops.xywh2xyxy(bbox)
             score, cls = scores[i].max(-1)  # (300, )
-            # Do not need threshold for evaluation as only got 300 boxes here.
+            # Do not need threshold for evaluation as only got 300 boxes here
             # idx = score > self.args.conf
             pred = torch.cat([bbox, score[..., None], cls[..., None]], dim=-1)  # filter
-            # sort by confidence to correctly get internal metrics.
+            # Sort by confidence to correctly get internal metrics
             pred = pred[score.argsort(descending=True)]
             outputs[i] = pred  # [idx]
 

ultralytics/models/sam/modules/encoders.py

@@ -304,11 +304,11 @@ class PositionEmbeddingRandom(nn.Module):
 
     def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
         """Positionally encode points that are normalized to [0,1]."""
-        # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
+        # Assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
         coords = 2 * coords - 1
         coords = coords @ self.positional_encoding_gaussian_matrix
         coords = 2 * np.pi * coords
-        # outputs d_1 x ... x d_n x C shape
+        # Outputs d_1 x ... x d_n x C shape
         return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
 
     def forward(self, size: Tuple[int, int]) -> torch.Tensor:
@@ -429,7 +429,7 @@ class Attention(nn.Module):
         self.use_rel_pos = use_rel_pos
         if self.use_rel_pos:
             assert (input_size is not None), 'Input size must be provided if using relative positional encoding.'
-            # initialize relative positional embeddings
+            # Initialize relative positional embeddings
             self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
             self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
 

ultralytics/models/sam/modules/tiny_encoder.py

@@ -172,7 +172,7 @@ class ConvLayer(nn.Module):
         self.depth = depth
         self.use_checkpoint = use_checkpoint
 
-        # build blocks
+        # Build blocks
         self.blocks = nn.ModuleList([
             MBConv(
                 dim,
@@ -182,7 +182,7 @@ class ConvLayer(nn.Module):
                 drop_path[i] if isinstance(drop_path, list) else drop_path,
             ) for i in range(depth)])
 
-        # patch merging layer
+        # Patch merging layer
         self.downsample = None if downsample is None else downsample(
             input_resolution, dim=dim, out_dim=out_dim, activation=activation)
 
@@ -393,11 +393,11 @@ class TinyViTBlock(nn.Module):
             pH, pW = H + pad_b, W + pad_r
             nH = pH // self.window_size
             nW = pW // self.window_size
-            # window partition
+            # Window partition
             x = x.view(B, nH, self.window_size, nW, self.window_size,
                        C).transpose(2, 3).reshape(B * nH * nW, self.window_size * self.window_size, C)
             x = self.attn(x)
-            # window reverse
+            # Window reverse
             x = x.view(B, nH, nW, self.window_size, self.window_size, C).transpose(2, 3).reshape(B, pH, pW, C)
 
             if padding:
@@ -467,7 +467,7 @@ class BasicLayer(nn.Module):
         self.depth = depth
         self.use_checkpoint = use_checkpoint
 
-        # build blocks
+        # Build blocks
         self.blocks = nn.ModuleList([
             TinyViTBlock(
                 dim=dim,
@@ -481,7 +481,7 @@ class BasicLayer(nn.Module):
                 activation=activation,
             ) for i in range(depth)])
 
-        # patch merging layer
+        # Patch merging layer
         self.downsample = None if downsample is None else downsample(
             input_resolution, dim=dim, out_dim=out_dim, activation=activation)
 
@@ -593,10 +593,10 @@ class TinyViT(nn.Module):
         patches_resolution = self.patch_embed.patches_resolution
         self.patches_resolution = patches_resolution
 
-        # stochastic depth
+        # Stochastic depth
         dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
 
-        # build layers
+        # Build layers
         self.layers = nn.ModuleList()
         for i_layer in range(self.num_layers):
             kwargs = dict(
@@ -628,7 +628,7 @@ class TinyViT(nn.Module):
         self.norm_head = nn.LayerNorm(embed_dims[-1])
         self.head = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else torch.nn.Identity()
 
-        # init weights
+        # Init weights
         self.apply(self._init_weights)
         self.set_layer_lr_decay(layer_lr_decay)
         self.neck = nn.Sequential(
@@ -653,7 +653,7 @@ class TinyViT(nn.Module):
         """Sets the learning rate decay for each layer in the TinyViT model."""
         decay_rate = layer_lr_decay
 
-        # layers -> blocks (depth)
+        # Layers -> blocks (depth)
         depth = sum(self.depths)
         lr_scales = [decay_rate ** (depth - i - 1) for i in range(depth)]
 

ultralytics/models/sam/predict.py

@@ -414,8 +414,7 @@ class Predictor(BasePredictor):
             unchanged = unchanged and not changed
 
             new_masks.append(torch.as_tensor(mask).unsqueeze(0))
-            # Give score=0 to changed masks and score=1 to unchanged masks
-            # so NMS will prefer ones that didn't need postprocessing
+            # Give score=0 to changed masks and 1 to unchanged masks so NMS prefers masks not needing postprocessing
             scores.append(float(unchanged))
 
         # Recalculate boxes and remove any new duplicates

ultralytics/models/utils/loss.py

@@ -66,7 +66,7 @@ class DETRLoss(nn.Module):
 
     def _get_loss_class(self, pred_scores, targets, gt_scores, num_gts, postfix=''):
         """Computes the classification loss based on predictions, target values, and ground truth scores."""
-        # logits: [b, query, num_classes], gt_class: list[[n, 1]]
+        # Logits: [b, query, num_classes], gt_class: list[[n, 1]]
         name_class = f'loss_class{postfix}'
         bs, nq = pred_scores.shape[:2]
         # one_hot = F.one_hot(targets, self.nc + 1)[..., :-1]  # (bs, num_queries, num_classes)
@@ -90,7 +90,7 @@ class DETRLoss(nn.Module):
         """Calculates and returns the bounding box loss and GIoU loss for the predicted and ground truth bounding
         boxes.
         """
-        # boxes: [b, query, 4], gt_bbox: list[[n, 4]]
+        # Boxes: [b, query, 4], gt_bbox: list[[n, 4]]
         name_bbox = f'loss_bbox{postfix}'
         name_giou = f'loss_giou{postfix}'
 

ultralytics/models/utils/ops.py

@@ -188,7 +188,7 @@ def get_cdn_group(batch,
 
     num_group = num_dn // max_nums
     num_group = 1 if num_group == 0 else num_group
-    # pad gt to max_num of a batch
+    # Pad gt to max_num of a batch
     bs = len(gt_groups)
     gt_cls = batch['cls']  # (bs*num, )
     gt_bbox = batch['bboxes']  # bs*num, 4
@@ -204,10 +204,10 @@ def get_cdn_group(batch,
     neg_idx = torch.arange(total_num * num_group, dtype=torch.long, device=gt_bbox.device) + num_group * total_num
 
     if cls_noise_ratio > 0:
-        # half of bbox prob
+        # Half of bbox prob
         mask = torch.rand(dn_cls.shape) < (cls_noise_ratio * 0.5)
         idx = torch.nonzero(mask).squeeze(-1)
-        # randomly put a new one here
+        # Randomly put a new one here
         new_label = torch.randint_like(idx, 0, num_classes, dtype=dn_cls.dtype, device=dn_cls.device)
         dn_cls[idx] = new_label
 
@@ -240,9 +240,9 @@ def get_cdn_group(batch,
 
     tgt_size = num_dn + num_queries
     attn_mask = torch.zeros([tgt_size, tgt_size], dtype=torch.bool)
-    # match query cannot see the reconstruct
+    # Match query cannot see the reconstruct
     attn_mask[num_dn:, :num_dn] = True
-    # reconstruct cannot see each other
+    # Reconstruct cannot see each other
     for i in range(num_group):
         if i == 0:
             attn_mask[max_nums * 2 * i:max_nums * 2 * (i + 1), max_nums * 2 * (i + 1):num_dn] = True

ultralytics/nn/modules/block.py

@@ -307,7 +307,7 @@ class Bottleneck(nn.Module):
         self.add = shortcut and c1 == c2
 
     def forward(self, x):
-        """'forward()' applies the YOLOv5 FPN to input data."""
+        """'forward()' applies the YOLO FPN to input data."""
         return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
 
 

ultralytics/nn/modules/head.py

@@ -192,7 +192,7 @@ class RTDETRDecoder(nn.Module):
             dropout=0.,
             act=nn.ReLU(),
             eval_idx=-1,
-            # training args
+            # Training args
             nd=100,  # num denoising
             label_noise_ratio=0.5,
             box_noise_scale=1.0,
@@ -225,7 +225,7 @@ class RTDETRDecoder(nn.Module):
         self.num_queries = nq
         self.num_decoder_layers = ndl
 
-        # backbone feature projection
+        # Backbone feature projection
         self.input_proj = nn.ModuleList(nn.Sequential(nn.Conv2d(x, hd, 1, bias=False), nn.BatchNorm2d(hd)) for x in ch)
         # NOTE: simplified version but it's not consistent with .pt weights.
         # self.input_proj = nn.ModuleList(Conv(x, hd, act=False) for x in ch)
@@ -234,24 +234,24 @@ class RTDETRDecoder(nn.Module):
         decoder_layer = DeformableTransformerDecoderLayer(hd, nh, d_ffn, dropout, act, self.nl, ndp)
         self.decoder = DeformableTransformerDecoder(hd, decoder_layer, ndl, eval_idx)
 
-        # denoising part
+        # Denoising part
         self.denoising_class_embed = nn.Embedding(nc, hd)
         self.num_denoising = nd
         self.label_noise_ratio = label_noise_ratio
         self.box_noise_scale = box_noise_scale
 
-        # decoder embedding
+        # Decoder embedding
         self.learnt_init_query = learnt_init_query
         if learnt_init_query:
             self.tgt_embed = nn.Embedding(nq, hd)
         self.query_pos_head = MLP(4, 2 * hd, hd, num_layers=2)
 
-        # encoder head
+        # Encoder head
         self.enc_output = nn.Sequential(nn.Linear(hd, hd), nn.LayerNorm(hd))
         self.enc_score_head = nn.Linear(hd, nc)
         self.enc_bbox_head = MLP(hd, hd, 4, num_layers=3)
 
-        # decoder head
+        # Decoder head
         self.dec_score_head = nn.ModuleList([nn.Linear(hd, nc) for _ in range(ndl)])
         self.dec_bbox_head = nn.ModuleList([MLP(hd, hd, 4, num_layers=3) for _ in range(ndl)])
 
@@ -261,10 +261,10 @@ class RTDETRDecoder(nn.Module):
         """Runs the forward pass of the module, returning bounding box and classification scores for the input."""
         from ultralytics.models.utils.ops import get_cdn_group
 
-        # input projection and embedding
+        # Input projection and embedding
         feats, shapes = self._get_encoder_input(x)
 
-        # prepare denoising training
+        # Prepare denoising training
         dn_embed, dn_bbox, attn_mask, dn_meta = \
             get_cdn_group(batch,
                           self.nc,
@@ -278,7 +278,7 @@ class RTDETRDecoder(nn.Module):
         embed, refer_bbox, enc_bboxes, enc_scores = \
             self._get_decoder_input(feats, shapes, dn_embed, dn_bbox)
 
-        # decoder
+        # Decoder
         dec_bboxes, dec_scores = self.decoder(embed,
                                               refer_bbox,
                                               feats,
@@ -316,9 +316,9 @@ class RTDETRDecoder(nn.Module):
 
     def _get_encoder_input(self, x):
         """Processes and returns encoder inputs by getting projection features from input and concatenating them."""
-        # get projection features
+        # Get projection features
         x = [self.input_proj[i](feat) for i, feat in enumerate(x)]
-        # get encoder inputs
+        # Get encoder inputs
         feats = []
         shapes = []
         for feat in x:
@@ -335,13 +335,13 @@ class RTDETRDecoder(nn.Module):
     def _get_decoder_input(self, feats, shapes, dn_embed=None, dn_bbox=None):
         """Generates and prepares the input required for the decoder from the provided features and shapes."""
         bs = len(feats)
-        # prepare input for decoder
+        # Prepare input for decoder
         anchors, valid_mask = self._generate_anchors(shapes, dtype=feats.dtype, device=feats.device)
         features = self.enc_output(valid_mask * feats)  # bs, h*w, 256
 
         enc_outputs_scores = self.enc_score_head(features)  # (bs, h*w, nc)
 
-        # query selection
+        # Query selection
         # (bs, num_queries)
         topk_ind = torch.topk(enc_outputs_scores.max(-1).values, self.num_queries, dim=1).indices.view(-1)
         # (bs, num_queries)
@@ -352,7 +352,7 @@ class RTDETRDecoder(nn.Module):
         # (bs, num_queries, 4)
         top_k_anchors = anchors[:, topk_ind].view(bs, self.num_queries, -1)
 
-        # dynamic anchors + static content
+        # Dynamic anchors + static content
         refer_bbox = self.enc_bbox_head(top_k_features) + top_k_anchors
 
         enc_bboxes = refer_bbox.sigmoid()
@@ -373,7 +373,7 @@ class RTDETRDecoder(nn.Module):
     # TODO
     def _reset_parameters(self):
         """Initializes or resets the parameters of the model's various components with predefined weights and biases."""
-        # class and bbox head init
+        # Class and bbox head init
         bias_cls = bias_init_with_prob(0.01) / 80 * self.nc
         # NOTE: the weight initialization in `linear_init_` would cause NaN when training with custom datasets.
         # linear_init_(self.enc_score_head)

ultralytics/nn/modules/transformer.py

@@ -81,7 +81,7 @@ class AIFI(TransformerEncoderLayer):
         """Forward pass for the AIFI transformer layer."""
         c, h, w = x.shape[1:]
         pos_embed = self.build_2d_sincos_position_embedding(w, h, c)
-        # flatten [B, C, H, W] to [B, HxW, C]
+        # Flatten [B, C, H, W] to [B, HxW, C]
         x = super().forward(x.flatten(2).permute(0, 2, 1), pos=pos_embed.to(device=x.device, dtype=x.dtype))
         return x.permute(0, 2, 1).view([-1, c, h, w]).contiguous()
 
@@ -213,7 +213,7 @@ class MSDeformAttn(nn.Module):
         if d_model % n_heads != 0:
             raise ValueError(f'd_model must be divisible by n_heads, but got {d_model} and {n_heads}')
         _d_per_head = d_model // n_heads
-        # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation
+        # Better to set _d_per_head to a power of 2 which is more efficient in a CUDA implementation
         assert _d_per_head * n_heads == d_model, '`d_model` must be divisible by `n_heads`'
 
         self.im2col_step = 64

ultralytics/nn/tasks.py

@@ -277,7 +277,7 @@ class DetectionModel(BaseModel):
         return torch.cat((x, y, wh, cls), dim)
 
     def _clip_augmented(self, y):
-        """Clip YOLOv5 augmented inference tails."""
+        """Clip YOLO augmented inference tails."""
         nl = self.model[-1].nl  # number of detection layers (P3-P5)
         g = sum(4 ** x for x in range(nl))  # grid points
         e = 1  # exclude layer count
@@ -491,7 +491,7 @@ class Ensemble(nn.ModuleList):
         super().__init__()
 
     def forward(self, x, augment=False, profile=False, visualize=False):
-        """Function generates the YOLOv5 network's final layer."""
+        """Function generates the YOLO network's final layer."""
         y = [module(x, augment, profile, visualize)[0] for module in self]
         # y = torch.stack(y).max(0)[0]  # max ensemble
         # y = torch.stack(y).mean(0)  # mean ensemble

ultralytics/utils/callbacks/mlflow.py

@@ -38,7 +38,7 @@ def on_pretrain_routine_end(trainer):
             if not active_run:
                 active_run = mlflow.start_run(experiment_id=experiment.experiment_id, run_name=run_name)
             LOGGER.info(f'{prefix}Using run_id({active_run.info.run_id}) at {mlflow_location}')
-            run.log_params(vars(trainer.model.args))
+            run.log_params(trainer.args)
         except Exception as err:
             LOGGER.error(f'{prefix}Failing init - {repr(err)}')
             LOGGER.warning(f'{prefix}Continuing without Mlflow')

ultralytics/utils/callbacks/wb.py

@@ -8,7 +8,10 @@ try:
     assert SETTINGS['wandb'] is True  # verify integration is enabled
     import wandb as wb
 
-    assert hasattr(wb, '__version__')
+    assert hasattr(wb, '__version__')  # verify package is not directory
+
+    import numpy as np
+    import pandas as pd
 
     _processed_plots = {}
 
@@ -16,6 +19,83 @@ except (ImportError, AssertionError):
     wb = None
 
 
+def _custom_table(x, y, classes, title='Precision Recall Curve', x_axis_title='Recall', y_axis_title='Precision'):
+    """
+    Create and log a custom metric visualization to wandb.plot.pr_curve.
+
+    This function crafts a custom metric visualization that mimics the behavior of wandb's default precision-recall curve
+    while allowing for enhanced customization. The visual metric is useful for monitoring model performance across different classes.
+
+    Args:
+        x (List): Values for the x-axis; expected to have length N.
+        y (List): Corresponding values for the y-axis; also expected to have length N.
+        classes (List): Labels identifying the class of each point; length N.
+        title (str, optional): Title for the plot; defaults to 'Precision Recall Curve'.
+        x_title (str, optional): Label for the x-axis; defaults to 'Recall'.
+        y_title (str, optional): Label for the y-axis; defaults to 'Precision'.
+
+    Returns:
+        (wandb.Object): A wandb object suitable for logging, showcasing the crafted metric visualization.
+    """
+    df = pd.DataFrame({'class': classes, 'y': y, 'x': x}).round(3)
+    fields = {'x': 'x', 'y': 'y', 'class': 'class'}
+    string_fields = {'title': title, 'x-axis-title': x_axis_title, 'y-axis-title': y_axis_title}
+    return wb.plot_table('wandb/area-under-curve/v0',
+                         wb.Table(dataframe=df),
+                         fields=fields,
+                         string_fields=string_fields)
+
+
+def _plot_curve(x,
+                y,
+                names=None,
+                id='precision-recall',
+                title='Precision Recall Curve',
+                x_title='Recall',
+                y_title='Precision',
+                num_x=100,
+                only_mean=False):
+    """
+    Log a metric curve visualization.
+
+    This function generates a metric curve based on input data and logs the visualization to wandb.
+    The curve can represent aggregated data (mean) or individual class data, depending on the 'only_mean' flag.
+
+    Args:
+        x (np.ndarray): Data points for the x-axis with length N.
+        y (np.ndarray): Corresponding data points for the y-axis with shape CxN, where C represents the number of classes.
+        names (list, optional): Names of the classes corresponding to the y-axis data; length C. Defaults to an empty list.
+        id (str, optional): Unique identifier for the logged data in wandb. Defaults to 'precision-recall'.
+        title (str, optional): Title for the visualization plot. Defaults to 'Precision Recall Curve'.
+        x_title (str, optional): Label for the x-axis. Defaults to 'Recall'.
+        y_title (str, optional): Label for the y-axis. Defaults to 'Precision'.
+        num_x (int, optional): Number of interpolated data points for visualization. Defaults to 100.
+        only_mean (bool, optional): Flag to indicate if only the mean curve should be plotted. Defaults to True.
+
+    Note:
+        The function leverages the '_custom_table' function to generate the actual visualization.
+    """
+    # Create new x
+    if names is None:
+        names = []
+    x_new = np.linspace(x[0], x[-1], num_x).round(5)
+
+    # Create arrays for logging
+    x_log = x_new.tolist()
+    y_log = np.interp(x_new, x, np.mean(y, axis=0)).round(3).tolist()
+
+    if only_mean:
+        table = wb.Table(data=list(zip(x_log, y_log)), columns=[x_title, y_title])
+        wb.run.log({title: wb.plot.line(table, x_title, y_title, title=title)})
+    else:
+        classes = ['mean'] * len(x_log)
+        for i, yi in enumerate(y):
+            x_log.extend(x_new)  # add new x
+            y_log.extend(np.interp(x_new, x, yi))  # interpolate y to new x
+            classes.extend([names[i]] * len(x_new))  # add class names
+        wb.log({id: _custom_table(x_log, y_log, classes, title, x_title, y_title)}, commit=False)
+
+
 def _log_plots(plots, step):
     """Logs plots from the input dictionary if they haven't been logged already at the specified step."""
     for name, params in plots.items():
@@ -55,6 +135,17 @@ def on_train_end(trainer):
     if trainer.best.exists():
         art.add_file(trainer.best)
         wb.run.log_artifact(art, aliases=['best'])
+    for curve_name, curve_values in zip(trainer.validator.metrics.curves, trainer.validator.metrics.curves_results):
+        x, y, x_title, y_title = curve_values
+        _plot_curve(
+            x,
+            y,
+            names=list(trainer.validator.metrics.names.values()),
+            id=f'curves/{curve_name}',
+            title=curve_name,
+            x_title=x_title,
+            y_title=y_title,
+        )
     wb.run.finish()  # required or run continues on dashboard
 
 

ultralytics/utils/checks.py

@@ -165,16 +165,16 @@ def check_version(current: str = '0.0.0',
 
     Example:
         ```python
-        # check if current version is exactly 22.04
+        # Check if current version is exactly 22.04
         check_version(current='22.04', required='==22.04')
 
-        # check if current version is greater than or equal to 22.04
+        # Check if current version is greater than or equal to 22.04
         check_version(current='22.10', required='22.04')  # assumes '>=' inequality if none passed
 
-        # check if current version is less than or equal to 22.04
+        # Check if current version is less than or equal to 22.04
         check_version(current='22.04', required='<=22.04')
 
-        # check if current version is between 20.04 (inclusive) and 22.04 (exclusive)
+        # Check if current version is between 20.04 (inclusive) and 22.04 (exclusive)
         check_version(current='21.10', required='>20.04,<22.04')
         ```
     """

ultralytics/utils/files.py

@@ -45,7 +45,7 @@ def spaces_in_path(path):
         with ultralytics.utils.files import spaces_in_path
 
         with spaces_in_path('/path/with spaces') as new_path:
-            # your code here
+            # Your code here
         ```
     """
 

ultralytics/utils/instance.py

@@ -219,7 +219,7 @@ class Instances:
         self.normalized = normalized
 
         if len(segments) > 0:
-            # list[np.array(1000, 2)] * num_samples
+            # List[np.array(1000, 2)] * num_samples
             segments = resample_segments(segments)
             # (N, 1000, 2)
             segments = np.stack(segments, axis=0)

ultralytics/utils/loss.py

@@ -176,13 +176,13 @@ class v8DetectionLoss:
         imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0]  # image size (h,w)
         anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)
 
-        # targets
+        # Targets
         targets = torch.cat((batch['batch_idx'].view(-1, 1), batch['cls'].view(-1, 1), batch['bboxes']), 1)
         targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
         gt_labels, gt_bboxes = targets.split((1, 4), 2)  # cls, xyxy
         mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
 
-        # pboxes
+        # Pboxes
         pred_bboxes = self.bbox_decode(anchor_points, pred_distri)  # xyxy, (b, h*w, 4)
 
         _, target_bboxes, target_scores, fg_mask, _ = self.assigner(
@@ -191,11 +191,11 @@ class v8DetectionLoss:
 
         target_scores_sum = max(target_scores.sum(), 1)
 
-        # cls loss
+        # Cls loss
         # loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum  # VFL way
         loss[1] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum  # BCE
 
-        # bbox loss
+        # Bbox loss
         if fg_mask.sum():
             target_bboxes /= stride_tensor
             loss[0], loss[2] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores,
@@ -224,7 +224,7 @@ class v8SegmentationLoss(v8DetectionLoss):
         pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
             (self.reg_max * 4, self.nc), 1)
 
-        # b, grids, ..
+        # B, grids, ..
         pred_scores = pred_scores.permute(0, 2, 1).contiguous()
         pred_distri = pred_distri.permute(0, 2, 1).contiguous()
         pred_masks = pred_masks.permute(0, 2, 1).contiguous()
@@ -233,7 +233,7 @@ class v8SegmentationLoss(v8DetectionLoss):
         imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0]  # image size (h,w)
         anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)
 
-        # targets
+        # Targets
         try:
             batch_idx = batch['batch_idx'].view(-1, 1)
             targets = torch.cat((batch_idx, batch['cls'].view(-1, 1), batch['bboxes']), 1)
@@ -247,7 +247,7 @@ class v8SegmentationLoss(v8DetectionLoss):
                             "correctly formatted 'segment' dataset using 'data=coco128-seg.yaml' "
                             'as an example.\nSee https://docs.ultralytics.com/tasks/segment/ for help.') from e
 
-        # pboxes
+        # Pboxes
         pred_bboxes = self.bbox_decode(anchor_points, pred_distri)  # xyxy, (b, h*w, 4)
 
         _, target_bboxes, target_scores, fg_mask, target_gt_idx = self.assigner(
@@ -256,15 +256,15 @@ class v8SegmentationLoss(v8DetectionLoss):
 
         target_scores_sum = max(target_scores.sum(), 1)
 
-        # cls loss
+        # Cls loss
         # loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum  # VFL way
         loss[2] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum  # BCE
 
         if fg_mask.sum():
-            # bbox loss
+            # Bbox loss
             loss[0], loss[3] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes / stride_tensor,
                                               target_scores, target_scores_sum, fg_mask)
-            # masks loss
+            # Masks loss
             masks = batch['masks'].to(self.device).float()
             if tuple(masks.shape[-2:]) != (mask_h, mask_w):  # downsample
                 masks = F.interpolate(masks[None], (mask_h, mask_w), mode='nearest')[0]
@@ -344,13 +344,13 @@ class v8SegmentationLoss(v8DetectionLoss):
         _, _, mask_h, mask_w = proto.shape
         loss = 0
 
-        # normalize to 0-1
+        # Normalize to 0-1
         target_bboxes_normalized = target_bboxes / imgsz[[1, 0, 1, 0]]
 
-        # areas of target bboxes
+        # Areas of target bboxes
         marea = xyxy2xywh(target_bboxes_normalized)[..., 2:].prod(2)
 
-        # normalize to mask size
+        # Normalize to mask size
         mxyxy = target_bboxes_normalized * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=proto.device)
 
         for i, single_i in enumerate(zip(fg_mask, target_gt_idx, pred_masks, proto, mxyxy, marea, masks)):
@@ -393,7 +393,7 @@ class v8PoseLoss(v8DetectionLoss):
         pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
             (self.reg_max * 4, self.nc), 1)
 
-        # b, grids, ..
+        # B, grids, ..
         pred_scores = pred_scores.permute(0, 2, 1).contiguous()
         pred_distri = pred_distri.permute(0, 2, 1).contiguous()
         pred_kpts = pred_kpts.permute(0, 2, 1).contiguous()
@@ -402,7 +402,7 @@ class v8PoseLoss(v8DetectionLoss):
         imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0]  # image size (h,w)
         anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)
 
-        # targets
+        # Targets
         batch_size = pred_scores.shape[0]
         batch_idx = batch['batch_idx'].view(-1, 1)
         targets = torch.cat((batch_idx, batch['cls'].view(-1, 1), batch['bboxes']), 1)
@@ -410,7 +410,7 @@ class v8PoseLoss(v8DetectionLoss):
         gt_labels, gt_bboxes = targets.split((1, 4), 2)  # cls, xyxy
         mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
 
-        # pboxes
+        # Pboxes
         pred_bboxes = self.bbox_decode(anchor_points, pred_distri)  # xyxy, (b, h*w, 4)
         pred_kpts = self.kpts_decode(anchor_points, pred_kpts.view(batch_size, -1, *self.kpt_shape))  # (b, h*w, 17, 3)
 
@@ -420,11 +420,11 @@ class v8PoseLoss(v8DetectionLoss):
 
         target_scores_sum = max(target_scores.sum(), 1)
 
-        # cls loss
+        # Cls loss
         # loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum  # VFL way
         loss[3] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum  # BCE
 
-        # bbox loss
+        # Bbox loss
         if fg_mask.sum():
             target_bboxes /= stride_tensor
             loss[0], loss[4] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores,

ultralytics/utils/metrics.py

@@ -36,7 +36,7 @@ def bbox_ioa(box1, box2, iou=False, eps=1e-7):
     inter_area = (np.minimum(b1_x2[:, None], b2_x2) - np.maximum(b1_x1[:, None], b2_x1)).clip(0) * \
                  (np.minimum(b1_y2[:, None], b2_y2) - np.maximum(b1_y1[:, None], b2_y1)).clip(0)
 
-    # box2 area
+    # Box2 area
     area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
     if iou:
         box1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
@@ -440,13 +440,18 @@ def ap_per_class(tp,
 
     Returns:
         (tuple): A tuple of six arrays and one array of unique classes, where:
-            tp (np.ndarray): True positive counts for each class.
-            fp (np.ndarray): False positive counts for each class.
-            p (np.ndarray): Precision values at each confidence threshold.
-            r (np.ndarray): Recall values at each confidence threshold.
-            f1 (np.ndarray): F1-score values at each confidence threshold.
-            ap (np.ndarray): Average precision for each class at different IoU thresholds.
-            unique_classes (np.ndarray): An array of unique classes that have data.
+            tp (np.ndarray): True positive counts at threshold given by max F1 metric for each class.Shape: (nc,).
+            fp (np.ndarray): False positive counts at threshold given by max F1 metric for each class. Shape: (nc,).
+            p (np.ndarray): Precision values at threshold given by max F1 metric for each class. Shape: (nc,).
+            r (np.ndarray): Recall values at threshold given by max F1 metric for each class. Shape: (nc,).
+            f1 (np.ndarray): F1-score values at threshold given by max F1 metric for each class. Shape: (nc,).
+            ap (np.ndarray): Average precision for each class at different IoU thresholds. Shape: (nc, 10).
+            unique_classes (np.ndarray): An array of unique classes that have data. Shape: (nc,).
+            p_curve (np.ndarray): Precision curves for each class. Shape: (nc, 1000).
+            r_curve (np.ndarray): Recall curves for each class. Shape: (nc, 1000).
+            f1_curve (np.ndarray): F1-score curves for each class. Shape: (nc, 1000).
+            x (np.ndarray): X-axis values for the curves. Shape: (1000,).
+            prec_values: Precision values at mAP@0.5 for each class. Shape: (nc, 1000).
     """
 
     # Sort by objectness
@@ -458,8 +463,10 @@ def ap_per_class(tp,
     nc = unique_classes.shape[0]  # number of classes, number of detections
 
     # Create Precision-Recall curve and compute AP for each class
-    px, py = np.linspace(0, 1, 1000), []  # for plotting
-    ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
+    x, prec_values = np.linspace(0, 1, 1000), []
+
+    # Average precision, precision and recall curves
+    ap, p_curve, r_curve = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
     for ci, c in enumerate(unique_classes):
         i = pred_cls == c
         n_l = nt[ci]  # number of labels
@@ -473,33 +480,35 @@ def ap_per_class(tp,
 
         # Recall
         recall = tpc / (n_l + eps)  # recall curve
-        r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0)  # negative x, xp because xp decreases
+        r_curve[ci] = np.interp(-x, -conf[i], recall[:, 0], left=0)  # negative x, xp because xp decreases
 
         # Precision
         precision = tpc / (tpc + fpc)  # precision curve
-        p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1)  # p at pr_score
+        p_curve[ci] = np.interp(-x, -conf[i], precision[:, 0], left=1)  # p at pr_score
 
         # AP from recall-precision curve
         for j in range(tp.shape[1]):
             ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
             if plot and j == 0:
-                py.append(np.interp(px, mrec, mpre))  # precision at mAP@0.5
+                prec_values.append(np.interp(x, mrec, mpre))  # precision at mAP@0.5
+
+    prec_values = np.array(prec_values)  # (nc, 1000)
 
     # Compute F1 (harmonic mean of precision and recall)
-    f1 = 2 * p * r / (p + r + eps)
+    f1_curve = 2 * p_curve * r_curve / (p_curve + r_curve + eps)
     names = [v for k, v in names.items() if k in unique_classes]  # list: only classes that have data
     names = dict(enumerate(names))  # to dict
     if plot:
-        plot_pr_curve(px, py, ap, save_dir / f'{prefix}PR_curve.png', names, on_plot=on_plot)
-        plot_mc_curve(px, f1, save_dir / f'{prefix}F1_curve.png', names, ylabel='F1', on_plot=on_plot)
-        plot_mc_curve(px, p, save_dir / f'{prefix}P_curve.png', names, ylabel='Precision', on_plot=on_plot)
-        plot_mc_curve(px, r, save_dir / f'{prefix}R_curve.png', names, ylabel='Recall', on_plot=on_plot)
+        plot_pr_curve(x, prec_values, ap, save_dir / f'{prefix}PR_curve.png', names, on_plot=on_plot)
+        plot_mc_curve(x, f1_curve, save_dir / f'{prefix}F1_curve.png', names, ylabel='F1', on_plot=on_plot)
+        plot_mc_curve(x, p_curve, save_dir / f'{prefix}P_curve.png', names, ylabel='Precision', on_plot=on_plot)
+        plot_mc_curve(x, r_curve, save_dir / f'{prefix}R_curve.png', names, ylabel='Recall', on_plot=on_plot)
 
-    i = smooth(f1.mean(0), 0.1).argmax()  # max F1 index
-    p, r, f1 = p[:, i], r[:, i], f1[:, i]
+    i = smooth(f1_curve.mean(0), 0.1).argmax()  # max F1 index
+    p, r, f1 = p_curve[:, i], r_curve[:, i], f1_curve[:, i]  # max-F1 precision, recall, F1 values
     tp = (r * nt).round()  # true positives
     fp = (tp / (p + eps) - tp).round()  # false positives
-    return tp, fp, p, r, f1, ap, unique_classes.astype(int)
+    return tp, fp, p, r, f1, ap, unique_classes.astype(int), p_curve, r_curve, f1_curve, x, prec_values
 
 
 class Metric(SimpleClass):
@@ -645,7 +654,19 @@ class Metric(SimpleClass):
             Updates the class attributes `self.p`, `self.r`, `self.f1`, `self.all_ap`, and `self.ap_class_index` based
             on the values provided in the `results` tuple.
         """
-        self.p, self.r, self.f1, self.all_ap, self.ap_class_index = results
+        (self.p, self.r, self.f1, self.all_ap, self.ap_class_index, self.p_curve, self.r_curve, self.f1_curve, self.px,
+         self.prec_values) = results
+
+    @property
+    def curves(self):
+        """Returns a list of curves for accessing specific metrics curves."""
+        return []
+
+    @property
+    def curves_results(self):
+        """Returns a list of curves for accessing specific metrics curves."""
+        return [[self.px, self.prec_values, 'Recall', 'Precision'], [self.px, self.f1_curve, 'Confidence', 'F1'],
+                [self.px, self.p_curve, 'Confidence', 'Precision'], [self.px, self.r_curve, 'Confidence', 'Recall']]
 
 
 class DetMetrics(SimpleClass):
@@ -676,6 +697,8 @@ class DetMetrics(SimpleClass):
         fitness: Computes the fitness score based on the computed detection metrics.
         ap_class_index: Returns a list of class indices sorted by their average precision (AP) values.
         results_dict: Returns a dictionary that maps detection metric keys to their computed values.
+        curves: TODO
+        curves_results: TODO
     """
 
     def __init__(self, save_dir=Path('.'), plot=False, on_plot=None, names=()) -> None:
@@ -686,6 +709,7 @@ class DetMetrics(SimpleClass):
         self.names = names
         self.box = Metric()
         self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
+        self.task = 'detect'
 
     def process(self, tp, conf, pred_cls, target_cls):
         """Process predicted results for object detection and update metrics."""
@@ -733,6 +757,16 @@ class DetMetrics(SimpleClass):
         """Returns dictionary of computed performance metrics and statistics."""
         return dict(zip(self.keys + ['fitness'], self.mean_results() + [self.fitness]))
 
+    @property
+    def curves(self):
+        """Returns a list of curves for accessing specific metrics curves."""
+        return ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
+
+    @property
+    def curves_results(self):
+        """Returns dictionary of computed performance metrics and statistics."""
+        return self.box.curves_results
+
 
 class SegmentMetrics(SimpleClass):
     """
@@ -772,6 +806,7 @@ class SegmentMetrics(SimpleClass):
         self.box = Metric()
         self.seg = Metric()
         self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
+        self.task = 'segment'
 
     def process(self, tp_b, tp_m, conf, pred_cls, target_cls):
         """
@@ -843,6 +878,18 @@ class SegmentMetrics(SimpleClass):
         """Returns results of object detection model for evaluation."""
         return dict(zip(self.keys + ['fitness'], self.mean_results() + [self.fitness]))
 
+    @property
+    def curves(self):
+        """Returns a list of curves for accessing specific metrics curves."""
+        return [
+            'Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)',
+            'Precision-Recall(M)', 'F1-Confidence(M)', 'Precision-Confidence(M)', 'Recall-Confidence(M)']
+
+    @property
+    def curves_results(self):
+        """Returns dictionary of computed performance metrics and statistics."""
+        return self.box.curves_results + self.seg.curves_results
+
 
 class PoseMetrics(SegmentMetrics):
     """
@@ -883,6 +930,7 @@ class PoseMetrics(SegmentMetrics):
         self.box = Metric()
         self.pose = Metric()
         self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
+        self.task = 'pose'
 
     def process(self, tp_b, tp_p, conf, pred_cls, target_cls):
         """
@@ -944,6 +992,18 @@ class PoseMetrics(SegmentMetrics):
         """Computes classification metrics and speed using the `targets` and `pred` inputs."""
         return self.pose.fitness() + self.box.fitness()
 
+    @property
+    def curves(self):
+        """Returns a list of curves for accessing specific metrics curves."""
+        return [
+            'Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)',
+            'Precision-Recall(P)', 'F1-Confidence(P)', 'Precision-Confidence(P)', 'Recall-Confidence(P)']
+
+    @property
+    def curves_results(self):
+        """Returns dictionary of computed performance metrics and statistics."""
+        return self.box.curves_results + self.pose.curves_results
+
 
 class ClassifyMetrics(SimpleClass):
     """
@@ -968,6 +1028,7 @@ class ClassifyMetrics(SimpleClass):
         self.top1 = 0
         self.top5 = 0
         self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
+        self.task = 'classify'
 
     def process(self, targets, pred):
         """Target classes and predicted classes."""
@@ -990,3 +1051,13 @@ class ClassifyMetrics(SimpleClass):
     def keys(self):
         """Returns a list of keys for the results_dict property."""
         return ['metrics/accuracy_top1', 'metrics/accuracy_top5']
+
+    @property
+    def curves(self):
+        """Returns a list of curves for accessing specific metrics curves."""
+        return []
+
+    @property
+    def curves_results(self):
+        """Returns a list of curves for accessing specific metrics curves."""
+        return []

ultralytics/utils/tal.py

@@ -193,7 +193,7 @@ class TaskAlignedAssigner(nn.Module):
             # Expand topk_idxs for each value of k and add 1 at the specified positions
             count_tensor.scatter_add_(-1, topk_idxs[:, :, k:k + 1], ones)
         # count_tensor.scatter_add_(-1, topk_idxs, torch.ones_like(topk_idxs, dtype=torch.int8, device=topk_idxs.device))
-        # filter invalid bboxes
+        # Filter invalid bboxes
         count_tensor.masked_fill_(count_tensor > 1, 0)
 
         return count_tensor.to(metrics.dtype)

ultralytics/utils/tuner.py

@@ -45,6 +45,7 @@ def run_ray_tune(model,
     try:
         subprocess.run('pip install ray[tune]'.split(), check=True)
 
+        import ray
         from ray import tune
         from ray.air import RunConfig
         from ray.air.integrations.wandb import WandbLoggerCallback
@@ -83,6 +84,10 @@ def run_ray_tune(model,
         'mixup': tune.uniform(0.0, 1.0),  # image mixup (probability)
         'copy_paste': tune.uniform(0.0, 1.0)}  # segment copy-paste (probability)
 
+    # Put the model in ray store
+    task = model.task
+    model_in_store = ray.put(model)
+
     def _tune(config):
         """
         Trains the YOLO model with the specified hyperparameters and additional arguments.
@@ -93,9 +98,10 @@ def run_ray_tune(model,
         Returns:
             None.
         """
-        model.reset_callbacks()
+        model_to_train = ray.get(model_in_store)  # get the model from ray store for tuning
+        model_to_train.reset_callbacks()
         config.update(train_args)
-        results = model.train(**config)
+        results = model_to_train.train(**config)
         return results.results_dict
 
     # Get search space
@@ -104,7 +110,7 @@ def run_ray_tune(model,
         LOGGER.warning('WARNING ⚠️ search space not provided, using default search space.')
 
     # Get dataset
-    data = train_args.get('data', TASK2DATA[model.task])
+    data = train_args.get('data', TASK2DATA[task])
     space['data'] = data
     if 'data' not in train_args:
         LOGGER.warning(f'WARNING ⚠️ data not provided, using default "data={data}".')
@@ -114,7 +120,7 @@ def run_ray_tune(model,
 
     # Define the ASHA scheduler for hyperparameter search
     asha_scheduler = ASHAScheduler(time_attr='epoch',
-                                   metric=TASK2METRIC[model.task],
+                                   metric=TASK2METRIC[task],
                                    mode='max',
                                    max_t=train_args.get('epochs') or DEFAULT_CFG_DICT['epochs'] or 100,
                                    grace_period=grace_period,