ultralytics 8.0.79 expand Docs reference section (#2053)

Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Fri3dChicken <87434761+AmoghDhaliwal@users.noreply.github.com>

docker/Dockerfile

@@ -41,7 +41,7 @@ ENV OMP_NUM_THREADS=1
 # t=ultralytics/ultralytics:latest && sudo docker build -f docker/Dockerfile -t $t . && sudo docker push $t
 
 # Pull and Run
-# t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host $t
+# t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t
 
 # Pull and Run with local directory access
 # t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t

docker/Dockerfile-jetson

@@ -14,10 +14,6 @@ RUN apt update \
     && apt install --no-install-recommends -y gcc git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg g++
 # RUN alias python=python3
 
-# Security updates
-# https://security.snyk.io/vuln/SNYK-UBUNTU1804-OPENSSL-3314796
-RUN apt upgrade --no-install-recommends -y openssl tar
-
 # Create working directory
 RUN mkdir -p /usr/src/ultralytics
 WORKDIR /usr/src/ultralytics
@@ -38,7 +34,7 @@ ENV OMP_NUM_THREADS=1
 # Usage Examples -------------------------------------------------------------------------------------------------------
 
 # Build and Push
-# t=ultralytics/ultralytics:jetson && sudo docker build -f docker/Dockerfile -t $t . && sudo docker push $t
+# t=ultralytics/ultralytics:latest-jetson && sudo docker build --platform linux/arm64 -f docker/Dockerfile-jetson -t $t . && sudo docker push $t
 
 # Pull and Run
-# t=ultralytics/ultralytics:jetson && sudo docker pull $t && sudo docker run -it --ipc=host $t
+# t=ultralytics/ultralytics:jetson && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t

docs/build_reference.py

@@ -0,0 +1,108 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+"""
+Helper file to build Ultralytics Docs reference section. Recursively walks through ultralytics dir and builds an MkDocs
+reference section of *.md files composed of classes and functions, and also creates a nav menu for use in mkdocs.yaml.
+
+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
+
+TARGET_DIR = Path('..')
+
+
+def extract_classes_and_functions(filepath):
+    with open(filepath, 'r') as file:
+        content = file.read()
+
+    class_pattern = r"(?:^|\n)class\s(\w+)(?:\(|:)"
+    func_pattern = r"(?:^|\n)def\s(\w+)\("
+
+    classes = re.findall(class_pattern, content)
+    functions = re.findall(func_pattern, content)
+
+    return classes, functions
+
+
+def create_markdown(py_filepath, module_path, classes, functions):
+    md_filepath = py_filepath.with_suffix('.md')
+
+    md_content = [f"# {class_name}\n---\n:::{module_path}.{class_name}\n<br><br>\n" for class_name in classes]
+    md_content.extend(f"# {func_name}\n---\n:::{module_path}.{func_name}\n<br><br>\n" for func_name in functions)
+    md_content = "\n".join(md_content)
+
+    os.makedirs(os.path.dirname(md_filepath), exist_ok=True)
+    with open(md_filepath, 'w') as file:
+        file.write(md_content)
+
+    return md_filepath.relative_to(TARGET_DIR)
+
+
+def nested_dict():
+    return defaultdict(nested_dict)
+
+
+def sort_nested_dict(d):
+    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):
+    nav_tree = nested_dict()
+
+    for item_str in nav_items:
+        item = Path(item_str)
+        parts = item.parts
+        current_level = nav_tree['reference']
+        for part in parts[2:-1]:  # skip the first two parts (docs and reference) and the last part (filename)
+            current_level = current_level[part]
+
+        md_file_name = parts[-1].replace('.md', '')
+        current_level[md_file_name] = item
+
+    nav_tree_sorted = sort_nested_dict(nav_tree)
+
+    def _dict_to_yaml(d, level=0):
+        yaml_str = ""
+        indent = "  " * level
+        for k, v in d.items():
+            if isinstance(v, dict):
+                yaml_str += f"{indent}- {k}:\n{_dict_to_yaml(v, level + 1)}"
+            else:
+                yaml_str += f"{indent}- {k}: {str(v).replace('docs/', '')}\n"
+        return yaml_str
+
+    with open(TARGET_DIR / 'nav_menu_updated.yml', 'w') as file:
+        yaml_str = _dict_to_yaml(nav_tree_sorted)
+        file.write(yaml_str)
+
+
+def main():
+    source_dir = Path("../ultralytics")
+    target_dir = Path("reference")
+
+    nav_items = []
+
+    for root, _, files in os.walk(source_dir):
+        for file in files:
+            if file.endswith(".py") and file != "__init__.py":
+                py_filepath = Path(root) / file
+                classes, functions = extract_classes_and_functions(py_filepath)
+
+                if classes or functions:
+                    py_filepath_rel = py_filepath.relative_to(source_dir)
+                    md_filepath = target_dir / py_filepath_rel
+                    module_path = f"ultralytics.{py_filepath_rel.with_suffix('').as_posix().replace('/', '.')}"
+                    md_rel_filepath = create_markdown(md_filepath, module_path, classes, functions)
+                    nav_items.append(str(md_rel_filepath))
+
+    create_nav_menu_yaml(nav_items)
+
+
+if __name__ == "__main__":
+    main()

docs/modes/predict.md

@@ -202,7 +202,7 @@ operations are cached, meaning they're only calculated once per object, and thos
     results[0].probs  # cls prob, (num_class, )
     ```
 
-Class reference documentation for `Results` module and its components can be found [here](../reference/results.md)
+Class reference documentation for `Results` module and its components can be found [here](../reference/yolo/engine/results.md)
 
 ## Plotting results
 

docs/quickstart.md

@@ -25,7 +25,7 @@ See the `ultralytics` [requirements.txt](https://github.com/ultralytics/ultralyt
     PyTorch requirements vary by operating system and CUDA requirements, so it's recommended to install PyTorch first following instructions at [https://pytorch.org/get-started/locally](https://pytorch.org/get-started/locally).
 
     <a href="https://pytorch.org/get-started/locally/">
-        <img width="100%" alt="PyTorch Installation Instructions" src="https://user-images.githubusercontent.com/26833433/228650108-ab0ec98a-b328-4f40-a40d-95355e8a84e3.png">
+        <img width="800" alt="PyTorch Installation Instructions" src="https://user-images.githubusercontent.com/26833433/228650108-ab0ec98a-b328-4f40-a40d-95355e8a84e3.png">
     </a>
 
 

docs/reference/base_pred.md

@@ -1,8 +0,0 @@
-All task Predictors are inherited from `BasePredictors` class that contains the model validation routine boilerplate.
-You can override any function of these Trainers to suit your needs.
-
----
-
-### BasePredictor API Reference
-
-:::ultralytics.yolo.engine.predictor.BasePredictor

docs/reference/base_trainer.md

@@ -1,8 +0,0 @@
-All task Trainers are inherited from `BaseTrainer` class that contains the model training and optimization routine
-boilerplate. You can override any function of these Trainers to suit your needs.
-
----
-
-### BaseTrainer API Reference
-
-:::ultralytics.yolo.engine.trainer.BaseTrainer

docs/reference/base_val.md

@@ -1,8 +0,0 @@
-All task Validators are inherited from `BaseValidator` class that contains the model validation routine boilerplate. You
-can override any function of these Trainers to suit your needs.
-
----
-
-### BaseValidator API Reference
-
-:::ultralytics.yolo.engine.validator.BaseValidator

docs/reference/exporter.md

@@ -1,3 +0,0 @@
-### Exporter API Reference
-
-:::ultralytics.yolo.engine.exporter.Exporter

docs/reference/hub/auth.md

@@ -0,0 +1,4 @@
+# Auth
+---
+:::ultralytics.hub.auth.Auth
+<br><br>

docs/reference/hub/session.md

@@ -0,0 +1,4 @@
+# HUBTrainingSession
+---
+:::ultralytics.hub.session.HUBTrainingSession
+<br><br>

docs/reference/hub/utils.md

@@ -0,0 +1,24 @@
+# Traces
+---
+:::ultralytics.hub.utils.Traces
+<br><br>
+
+# check_dataset_disk_space
+---
+:::ultralytics.hub.utils.check_dataset_disk_space
+<br><br>
+
+# request_with_credentials
+---
+:::ultralytics.hub.utils.request_with_credentials
+<br><br>
+
+# requests_with_progress
+---
+:::ultralytics.hub.utils.requests_with_progress
+<br><br>
+
+# smart_request
+---
+:::ultralytics.hub.utils.smart_request
+<br><br>

docs/reference/model.md

@@ -1 +0,0 @@
-::: ultralytics.yolo.engine.model

docs/reference/nn.md

@@ -1,19 +0,0 @@
-# nn Module
-
-Ultralytics nn module contains 3 main components:
-
-1. **AutoBackend**: A module that can run inference on all popular model formats
-2. **BaseModel**: `BaseModel` class defines the operations supported by tasks like Detection and Segmentation
-3. **modules**: Optimized and reusable neural network blocks built on PyTorch.
-
-## AutoBackend
-
-:::ultralytics.nn.autobackend.AutoBackend
-
-## BaseModel
-
-:::ultralytics.nn.tasks.BaseModel
-
-## Modules
-
-TODO

docs/reference/nn/autobackend.md

@@ -0,0 +1,9 @@
+# AutoBackend
+---
+:::ultralytics.nn.autobackend.AutoBackend
+<br><br>
+
+# check_class_names
+---
+:::ultralytics.nn.autobackend.check_class_names
+<br><br>

docs/reference/nn/autoshape.md

@@ -0,0 +1,9 @@
+# AutoShape
+---
+:::ultralytics.nn.autoshape.AutoShape
+<br><br>
+
+# Detections
+---
+:::ultralytics.nn.autoshape.Detections
+<br><br>

docs/reference/nn/modules.md

@@ -0,0 +1,159 @@
+# Conv
+---
+:::ultralytics.nn.modules.Conv
+<br><br>
+
+# DWConv
+---
+:::ultralytics.nn.modules.DWConv
+<br><br>
+
+# DWConvTranspose2d
+---
+:::ultralytics.nn.modules.DWConvTranspose2d
+<br><br>
+
+# ConvTranspose
+---
+:::ultralytics.nn.modules.ConvTranspose
+<br><br>
+
+# DFL
+---
+:::ultralytics.nn.modules.DFL
+<br><br>
+
+# TransformerLayer
+---
+:::ultralytics.nn.modules.TransformerLayer
+<br><br>
+
+# TransformerBlock
+---
+:::ultralytics.nn.modules.TransformerBlock
+<br><br>
+
+# Bottleneck
+---
+:::ultralytics.nn.modules.Bottleneck
+<br><br>
+
+# BottleneckCSP
+---
+:::ultralytics.nn.modules.BottleneckCSP
+<br><br>
+
+# C3
+---
+:::ultralytics.nn.modules.C3
+<br><br>
+
+# C2
+---
+:::ultralytics.nn.modules.C2
+<br><br>
+
+# C2f
+---
+:::ultralytics.nn.modules.C2f
+<br><br>
+
+# ChannelAttention
+---
+:::ultralytics.nn.modules.ChannelAttention
+<br><br>
+
+# SpatialAttention
+---
+:::ultralytics.nn.modules.SpatialAttention
+<br><br>
+
+# CBAM
+---
+:::ultralytics.nn.modules.CBAM
+<br><br>
+
+# C1
+---
+:::ultralytics.nn.modules.C1
+<br><br>
+
+# C3x
+---
+:::ultralytics.nn.modules.C3x
+<br><br>
+
+# C3TR
+---
+:::ultralytics.nn.modules.C3TR
+<br><br>
+
+# C3Ghost
+---
+:::ultralytics.nn.modules.C3Ghost
+<br><br>
+
+# SPP
+---
+:::ultralytics.nn.modules.SPP
+<br><br>
+
+# SPPF
+---
+:::ultralytics.nn.modules.SPPF
+<br><br>
+
+# Focus
+---
+:::ultralytics.nn.modules.Focus
+<br><br>
+
+# GhostConv
+---
+:::ultralytics.nn.modules.GhostConv
+<br><br>
+
+# GhostBottleneck
+---
+:::ultralytics.nn.modules.GhostBottleneck
+<br><br>
+
+# Concat
+---
+:::ultralytics.nn.modules.Concat
+<br><br>
+
+# Proto
+---
+:::ultralytics.nn.modules.Proto
+<br><br>
+
+# Ensemble
+---
+:::ultralytics.nn.modules.Ensemble
+<br><br>
+
+# Detect
+---
+:::ultralytics.nn.modules.Detect
+<br><br>
+
+# Segment
+---
+:::ultralytics.nn.modules.Segment
+<br><br>
+
+# Pose
+---
+:::ultralytics.nn.modules.Pose
+<br><br>
+
+# Classify
+---
+:::ultralytics.nn.modules.Classify
+<br><br>
+
+# autopad
+---
+:::ultralytics.nn.modules.autopad
+<br><br>

docs/reference/nn/tasks.md

@@ -0,0 +1,59 @@
+# BaseModel
+---
+:::ultralytics.nn.tasks.BaseModel
+<br><br>
+
+# DetectionModel
+---
+:::ultralytics.nn.tasks.DetectionModel
+<br><br>
+
+# SegmentationModel
+---
+:::ultralytics.nn.tasks.SegmentationModel
+<br><br>
+
+# PoseModel
+---
+:::ultralytics.nn.tasks.PoseModel
+<br><br>
+
+# ClassificationModel
+---
+:::ultralytics.nn.tasks.ClassificationModel
+<br><br>
+
+# torch_safe_load
+---
+:::ultralytics.nn.tasks.torch_safe_load
+<br><br>
+
+# attempt_load_weights
+---
+:::ultralytics.nn.tasks.attempt_load_weights
+<br><br>
+
+# attempt_load_one_weight
+---
+:::ultralytics.nn.tasks.attempt_load_one_weight
+<br><br>
+
+# parse_model
+---
+:::ultralytics.nn.tasks.parse_model
+<br><br>
+
+# yaml_model_load
+---
+:::ultralytics.nn.tasks.yaml_model_load
+<br><br>
+
+# guess_model_scale
+---
+:::ultralytics.nn.tasks.guess_model_scale
+<br><br>
+
+# guess_model_task
+---
+:::ultralytics.nn.tasks.guess_model_task
+<br><br>

docs/reference/ops.md

@@ -1,208 +0,0 @@
-This module contains optimized deep learning related operations used in the Ultralytics YOLO framework
-
-## Non-max suppression
-
-:::ultralytics.yolo.utils.ops.non_max_suppression
-handler: python
-options:
-show_source: false
-show_root_toc_entry: false
----
-
-## Scale boxes
-
-:::ultralytics.yolo.utils.ops.scale_boxes
-handler: python
-options:
-show_source: false
-show_root_toc_entry: false
----
-
-## Scale image
-
-:::ultralytics.yolo.utils.ops.scale_image
-handler: python
-options:
-show_source: false
-show_root_toc_entry: false
----
-
-## clip boxes
-
-:::ultralytics.yolo.utils.ops.clip_boxes
-handler: python
-options:
-show_source: false
-show_root_toc_entry: false
----
-
-# Box Format Conversion
-
-## xyxy2xywh
-
-:::ultralytics.yolo.utils.ops.xyxy2xywh
-handler: python
-options:
-show_source: false
-show_root_toc_entry: false
----
-
-## xywh2xyxy
-
-:::ultralytics.yolo.utils.ops.xywh2xyxy
-handler: python
-options:
-show_source: false
-show_root_toc_entry: false
----
-
-## xywhn2xyxy
-
-:::ultralytics.yolo.utils.ops.xywhn2xyxy
-handler: python
-options:
-show_source: false
-show_root_toc_entry: false
----
-
-## xyxy2xywhn
-
-:::ultralytics.yolo.utils.ops.xyxy2xywhn
-handler: python
-options:
-show_source: false
-show_root_toc_entry: false
----
-
-## xyn2xy
-
-:::ultralytics.yolo.utils.ops.xyn2xy
-handler: python
-options:
-show_source: false
-show_root_toc_entry: false
----
-
-## xywh2ltwh
-
-:::ultralytics.yolo.utils.ops.xywh2ltwh
-handler: python
-options:
-show_source: false
-show_root_toc_entry: false
----
-
-## xyxy2ltwh
-
-:::ultralytics.yolo.utils.ops.xyxy2ltwh
-handler: python
-options:
-show_source: false
-show_root_toc_entry: false
----
-
-## ltwh2xywh
-
-:::ultralytics.yolo.utils.ops.ltwh2xywh
-handler: python
-options:
-show_source: false
-show_root_toc_entry: false
----
-
-## ltwh2xyxy
-
-:::ultralytics.yolo.utils.ops.ltwh2xyxy
-handler: python
-options:
-show_source: false
-show_root_toc_entry: false
----
-
-## segment2box
-
-:::ultralytics.yolo.utils.ops.segment2box
-handler: python
-options:
-show_source: false
-show_root_toc_entry: false
----
-
-# Mask Operations
-
-## resample_segments
-
-:::ultralytics.yolo.utils.ops.resample_segments
-handler: python
-options:
-show_source: false
-show_root_toc_entry: false
----
-
-## crop_mask
-
-:::ultralytics.yolo.utils.ops.crop_mask
-handler: python
-options:
-show_source: false
-show_root_toc_entry: false
----
-
-## process_mask_upsample
-
-:::ultralytics.yolo.utils.ops.process_mask_upsample
-handler: python
-options:
-show_source: false
-show_root_toc_entry: false
----
-
-## process_mask
-
-:::ultralytics.yolo.utils.ops.process_mask
-handler: python
-options:
-show_source: false
-show_root_toc_entry: false
----
-
-## process_mask_native
-
-:::ultralytics.yolo.utils.ops.process_mask_native
-handler: python
-options:
-show_source: false
-show_root_toc_entry: false
----
-
-## scale_coords
-
-:::ultralytics.yolo.utils.ops.scale_coords
-handler: python
-options:
-show_source: false
-show_root_toc_entry: false
----
-
-## masks2segments
-
-:::ultralytics.yolo.utils.ops.masks2segments
-handler: python
-options:
-show_source: false
-show_root_toc_entry: false
----
-
-## clip_coords
-
-:::ultralytics.yolo.utils.ops.clip_coords
-handler: python
-options:
-show_source: false
-show_root_toc_entry: false
----
-
-
-
-
-

docs/reference/results.md

@@ -1,11 +0,0 @@
-### Results API Reference
-
-:::ultralytics.yolo.engine.results.Results
-
-### Boxes API Reference
-
-:::ultralytics.yolo.engine.results.Boxes
-
-### Masks API Reference
-
-:::ultralytics.yolo.engine.results.Masks

docs/reference/tracker/track.md

@@ -0,0 +1,14 @@
+# on_predict_start
+---
+:::ultralytics.tracker.track.on_predict_start
+<br><br>
+
+# on_predict_postprocess_end
+---
+:::ultralytics.tracker.track.on_predict_postprocess_end
+<br><br>
+
+# register_tracker
+---
+:::ultralytics.tracker.track.register_tracker
+<br><br>

docs/reference/tracker/trackers/basetrack.md

@@ -0,0 +1,9 @@
+# TrackState
+---
+:::ultralytics.tracker.trackers.basetrack.TrackState
+<br><br>
+
+# BaseTrack
+---
+:::ultralytics.tracker.trackers.basetrack.BaseTrack
+<br><br>

docs/reference/tracker/trackers/bot_sort.md

@@ -0,0 +1,9 @@
+# BOTrack
+---
+:::ultralytics.tracker.trackers.bot_sort.BOTrack
+<br><br>
+
+# BOTSORT
+---
+:::ultralytics.tracker.trackers.bot_sort.BOTSORT
+<br><br>

docs/reference/tracker/trackers/byte_tracker.md

@@ -0,0 +1,9 @@
+# STrack
+---
+:::ultralytics.tracker.trackers.byte_tracker.STrack
+<br><br>
+
+# BYTETracker
+---
+:::ultralytics.tracker.trackers.byte_tracker.BYTETracker
+<br><br>

docs/reference/tracker/utils/gmc.md

@@ -0,0 +1,4 @@
+# GMC
+---
+:::ultralytics.tracker.utils.gmc.GMC
+<br><br>

docs/reference/tracker/utils/kalman_filter.md

@@ -0,0 +1,9 @@
+# KalmanFilterXYAH
+---
+:::ultralytics.tracker.utils.kalman_filter.KalmanFilterXYAH
+<br><br>
+
+# KalmanFilterXYWH
+---
+:::ultralytics.tracker.utils.kalman_filter.KalmanFilterXYWH
+<br><br>

docs/reference/tracker/utils/matching.md

@@ -0,0 +1,59 @@
+# merge_matches
+---
+:::ultralytics.tracker.utils.matching.merge_matches
+<br><br>
+
+# _indices_to_matches
+---
+:::ultralytics.tracker.utils.matching._indices_to_matches
+<br><br>
+
+# linear_assignment
+---
+:::ultralytics.tracker.utils.matching.linear_assignment
+<br><br>
+
+# ious
+---
+:::ultralytics.tracker.utils.matching.ious
+<br><br>
+
+# iou_distance
+---
+:::ultralytics.tracker.utils.matching.iou_distance
+<br><br>
+
+# v_iou_distance
+---
+:::ultralytics.tracker.utils.matching.v_iou_distance
+<br><br>
+
+# embedding_distance
+---
+:::ultralytics.tracker.utils.matching.embedding_distance
+<br><br>
+
+# gate_cost_matrix
+---
+:::ultralytics.tracker.utils.matching.gate_cost_matrix
+<br><br>
+
+# fuse_motion
+---
+:::ultralytics.tracker.utils.matching.fuse_motion
+<br><br>
+
+# fuse_iou
+---
+:::ultralytics.tracker.utils.matching.fuse_iou
+<br><br>
+
+# fuse_score
+---
+:::ultralytics.tracker.utils.matching.fuse_score
+<br><br>
+
+# bbox_ious
+---
+:::ultralytics.tracker.utils.matching.bbox_ious
+<br><br>

docs/reference/yolo/data/augment.md

@@ -0,0 +1,89 @@
+# BaseTransform
+---
+:::ultralytics.yolo.data.augment.BaseTransform
+<br><br>
+
+# Compose
+---
+:::ultralytics.yolo.data.augment.Compose
+<br><br>
+
+# BaseMixTransform
+---
+:::ultralytics.yolo.data.augment.BaseMixTransform
+<br><br>
+
+# Mosaic
+---
+:::ultralytics.yolo.data.augment.Mosaic
+<br><br>
+
+# MixUp
+---
+:::ultralytics.yolo.data.augment.MixUp
+<br><br>
+
+# RandomPerspective
+---
+:::ultralytics.yolo.data.augment.RandomPerspective
+<br><br>
+
+# RandomHSV
+---
+:::ultralytics.yolo.data.augment.RandomHSV
+<br><br>
+
+# RandomFlip
+---
+:::ultralytics.yolo.data.augment.RandomFlip
+<br><br>
+
+# LetterBox
+---
+:::ultralytics.yolo.data.augment.LetterBox
+<br><br>
+
+# CopyPaste
+---
+:::ultralytics.yolo.data.augment.CopyPaste
+<br><br>
+
+# Albumentations
+---
+:::ultralytics.yolo.data.augment.Albumentations
+<br><br>
+
+# Format
+---
+:::ultralytics.yolo.data.augment.Format
+<br><br>
+
+# ClassifyLetterBox
+---
+:::ultralytics.yolo.data.augment.ClassifyLetterBox
+<br><br>
+
+# CenterCrop
+---
+:::ultralytics.yolo.data.augment.CenterCrop
+<br><br>
+
+# ToTensor
+---
+:::ultralytics.yolo.data.augment.ToTensor
+<br><br>
+
+# v8_transforms
+---
+:::ultralytics.yolo.data.augment.v8_transforms
+<br><br>
+
+# classify_transforms
+---
+:::ultralytics.yolo.data.augment.classify_transforms
+<br><br>
+
+# classify_albumentations
+---
+:::ultralytics.yolo.data.augment.classify_albumentations
+<br><br>

docs/reference/yolo/data/base.md

@@ -0,0 +1,4 @@
+# BaseDataset
+---
+:::ultralytics.yolo.data.base.BaseDataset
+<br><br>

docs/reference/yolo/data/build.md

@@ -0,0 +1,34 @@
+# InfiniteDataLoader
+---
+:::ultralytics.yolo.data.build.InfiniteDataLoader
+<br><br>
+
+# _RepeatSampler
+---
+:::ultralytics.yolo.data.build._RepeatSampler
+<br><br>
+
+# seed_worker
+---
+:::ultralytics.yolo.data.build.seed_worker
+<br><br>
+
+# build_dataloader
+---
+:::ultralytics.yolo.data.build.build_dataloader
+<br><br>
+
+# build_classification_dataloader
+---
+:::ultralytics.yolo.data.build.build_classification_dataloader
+<br><br>
+
+# check_source
+---
+:::ultralytics.yolo.data.build.check_source
+<br><br>
+
+# load_inference_source
+---
+:::ultralytics.yolo.data.build.load_inference_source
+<br><br>

docs/reference/yolo/data/dataloaders/stream_loaders.md

@@ -0,0 +1,34 @@
+# SourceTypes
+---
+:::ultralytics.yolo.data.dataloaders.stream_loaders.SourceTypes
+<br><br>
+
+# LoadStreams
+---
+:::ultralytics.yolo.data.dataloaders.stream_loaders.LoadStreams
+<br><br>
+
+# LoadScreenshots
+---
+:::ultralytics.yolo.data.dataloaders.stream_loaders.LoadScreenshots
+<br><br>
+
+# LoadImages
+---
+:::ultralytics.yolo.data.dataloaders.stream_loaders.LoadImages
+<br><br>
+
+# LoadPilAndNumpy
+---
+:::ultralytics.yolo.data.dataloaders.stream_loaders.LoadPilAndNumpy
+<br><br>
+
+# LoadTensor
+---
+:::ultralytics.yolo.data.dataloaders.stream_loaders.LoadTensor
+<br><br>
+
+# autocast_list
+---
+:::ultralytics.yolo.data.dataloaders.stream_loaders.autocast_list
+<br><br>

docs/reference/yolo/data/dataloaders/v5augmentations.md

@@ -0,0 +1,84 @@
+# Albumentations
+---
+:::ultralytics.yolo.data.dataloaders.v5augmentations.Albumentations
+<br><br>
+
+# LetterBox
+---
+:::ultralytics.yolo.data.dataloaders.v5augmentations.LetterBox
+<br><br>
+
+# CenterCrop
+---
+:::ultralytics.yolo.data.dataloaders.v5augmentations.CenterCrop
+<br><br>
+
+# ToTensor
+---
+:::ultralytics.yolo.data.dataloaders.v5augmentations.ToTensor
+<br><br>
+
+# normalize
+---
+:::ultralytics.yolo.data.dataloaders.v5augmentations.normalize
+<br><br>
+
+# denormalize
+---
+:::ultralytics.yolo.data.dataloaders.v5augmentations.denormalize
+<br><br>
+
+# augment_hsv
+---
+:::ultralytics.yolo.data.dataloaders.v5augmentations.augment_hsv
+<br><br>
+
+# hist_equalize
+---
+:::ultralytics.yolo.data.dataloaders.v5augmentations.hist_equalize
+<br><br>
+
+# replicate
+---
+:::ultralytics.yolo.data.dataloaders.v5augmentations.replicate
+<br><br>
+
+# letterbox
+---
+:::ultralytics.yolo.data.dataloaders.v5augmentations.letterbox
+<br><br>
+
+# random_perspective
+---
+:::ultralytics.yolo.data.dataloaders.v5augmentations.random_perspective
+<br><br>
+
+# copy_paste
+---
+:::ultralytics.yolo.data.dataloaders.v5augmentations.copy_paste
+<br><br>
+
+# cutout
+---
+:::ultralytics.yolo.data.dataloaders.v5augmentations.cutout
+<br><br>
+
+# mixup
+---
+:::ultralytics.yolo.data.dataloaders.v5augmentations.mixup
+<br><br>
+
+# box_candidates
+---
+:::ultralytics.yolo.data.dataloaders.v5augmentations.box_candidates
+<br><br>
+
+# classify_albumentations
+---
+:::ultralytics.yolo.data.dataloaders.v5augmentations.classify_albumentations
+<br><br>
+
+# classify_transforms
+---
+:::ultralytics.yolo.data.dataloaders.v5augmentations.classify_transforms
+<br><br>

docs/reference/yolo/data/dataloaders/v5loader.md

@@ -0,0 +1,89 @@
+# InfiniteDataLoader
+---
+:::ultralytics.yolo.data.dataloaders.v5loader.InfiniteDataLoader
+<br><br>
+
+# _RepeatSampler
+---
+:::ultralytics.yolo.data.dataloaders.v5loader._RepeatSampler
+<br><br>
+
+# LoadScreenshots
+---
+:::ultralytics.yolo.data.dataloaders.v5loader.LoadScreenshots
+<br><br>
+
+# LoadImages
+---
+:::ultralytics.yolo.data.dataloaders.v5loader.LoadImages
+<br><br>
+
+# LoadStreams
+---
+:::ultralytics.yolo.data.dataloaders.v5loader.LoadStreams
+<br><br>
+
+# LoadImagesAndLabels
+---
+:::ultralytics.yolo.data.dataloaders.v5loader.LoadImagesAndLabels
+<br><br>
+
+# ClassificationDataset
+---
+:::ultralytics.yolo.data.dataloaders.v5loader.ClassificationDataset
+<br><br>
+
+# get_hash
+---
+:::ultralytics.yolo.data.dataloaders.v5loader.get_hash
+<br><br>
+
+# exif_size
+---
+:::ultralytics.yolo.data.dataloaders.v5loader.exif_size
+<br><br>
+
+# exif_transpose
+---
+:::ultralytics.yolo.data.dataloaders.v5loader.exif_transpose
+<br><br>
+
+# seed_worker
+---
+:::ultralytics.yolo.data.dataloaders.v5loader.seed_worker
+<br><br>
+
+# create_dataloader
+---
+:::ultralytics.yolo.data.dataloaders.v5loader.create_dataloader
+<br><br>
+
+# img2label_paths
+---
+:::ultralytics.yolo.data.dataloaders.v5loader.img2label_paths
+<br><br>
+
+# flatten_recursive
+---
+:::ultralytics.yolo.data.dataloaders.v5loader.flatten_recursive
+<br><br>
+
+# extract_boxes
+---
+:::ultralytics.yolo.data.dataloaders.v5loader.extract_boxes
+<br><br>
+
+# autosplit
+---
+:::ultralytics.yolo.data.dataloaders.v5loader.autosplit
+<br><br>
+
+# verify_image_label
+---
+:::ultralytics.yolo.data.dataloaders.v5loader.verify_image_label
+<br><br>
+
+# create_classification_dataloader
+---
+:::ultralytics.yolo.data.dataloaders.v5loader.create_classification_dataloader
+<br><br>

docs/reference/yolo/data/dataset.md

@@ -0,0 +1,14 @@
+# YOLODataset
+---
+:::ultralytics.yolo.data.dataset.YOLODataset
+<br><br>
+
+# ClassificationDataset
+---
+:::ultralytics.yolo.data.dataset.ClassificationDataset
+<br><br>
+
+# SemanticDataset
+---
+:::ultralytics.yolo.data.dataset.SemanticDataset
+<br><br>

docs/reference/yolo/data/dataset_wrappers.md

@@ -0,0 +1,4 @@
+# MixAndRectDataset
+---
+:::ultralytics.yolo.data.dataset_wrappers.MixAndRectDataset
+<br><br>

docs/reference/yolo/data/utils.md

@@ -0,0 +1,64 @@
+# HUBDatasetStats
+---
+:::ultralytics.yolo.data.utils.HUBDatasetStats
+<br><br>
+
+# img2label_paths
+---
+:::ultralytics.yolo.data.utils.img2label_paths
+<br><br>
+
+# get_hash
+---
+:::ultralytics.yolo.data.utils.get_hash
+<br><br>
+
+# exif_size
+---
+:::ultralytics.yolo.data.utils.exif_size
+<br><br>
+
+# verify_image_label
+---
+:::ultralytics.yolo.data.utils.verify_image_label
+<br><br>
+
+# polygon2mask
+---
+:::ultralytics.yolo.data.utils.polygon2mask
+<br><br>
+
+# polygons2masks
+---
+:::ultralytics.yolo.data.utils.polygons2masks
+<br><br>
+
+# polygons2masks_overlap
+---
+:::ultralytics.yolo.data.utils.polygons2masks_overlap
+<br><br>
+
+# check_det_dataset
+---
+:::ultralytics.yolo.data.utils.check_det_dataset
+<br><br>
+
+# check_cls_dataset
+---
+:::ultralytics.yolo.data.utils.check_cls_dataset
+<br><br>
+
+# compress_one_image
+---
+:::ultralytics.yolo.data.utils.compress_one_image
+<br><br>
+
+# delete_dsstore
+---
+:::ultralytics.yolo.data.utils.delete_dsstore
+<br><br>
+
+# zip_directory
+---
+:::ultralytics.yolo.data.utils.zip_directory
+<br><br>

docs/reference/yolo/engine/exporter.md

@@ -0,0 +1,29 @@
+# iOSDetectModel
+---
+:::ultralytics.yolo.engine.exporter.iOSDetectModel
+<br><br>
+
+# Exporter
+---
+:::ultralytics.yolo.engine.exporter.Exporter
+<br><br>
+
+# export_formats
+---
+:::ultralytics.yolo.engine.exporter.export_formats
+<br><br>
+
+# gd_outputs
+---
+:::ultralytics.yolo.engine.exporter.gd_outputs
+<br><br>
+
+# try_export
+---
+:::ultralytics.yolo.engine.exporter.try_export
+<br><br>
+
+# export
+---
+:::ultralytics.yolo.engine.exporter.export
+<br><br>

docs/reference/yolo/engine/model.md

@@ -0,0 +1,4 @@
+# YOLO
+---
+:::ultralytics.yolo.engine.model.YOLO
+<br><br>

docs/reference/yolo/engine/predictor.md

@@ -0,0 +1,4 @@
+# BasePredictor
+---
+:::ultralytics.yolo.engine.predictor.BasePredictor
+<br><br>

docs/reference/yolo/engine/results.md

@@ -0,0 +1,19 @@
+# BaseTensor
+---
+:::ultralytics.yolo.engine.results.BaseTensor
+<br><br>
+
+# Results
+---
+:::ultralytics.yolo.engine.results.Results
+<br><br>
+
+# Boxes
+---
+:::ultralytics.yolo.engine.results.Boxes
+<br><br>
+
+# Masks
+---
+:::ultralytics.yolo.engine.results.Masks
+<br><br>

docs/reference/yolo/engine/trainer.md

@@ -0,0 +1,9 @@
+# BaseTrainer
+---
+:::ultralytics.yolo.engine.trainer.BaseTrainer
+<br><br>
+
+# check_amp
+---
+:::ultralytics.yolo.engine.trainer.check_amp
+<br><br>

docs/reference/yolo/engine/validator.md

@@ -0,0 +1,4 @@
+# BaseValidator
+---
+:::ultralytics.yolo.engine.validator.BaseValidator
+<br><br>

docs/reference/yolo/utils/autobatch.md

@@ -0,0 +1,9 @@
+# check_train_batch_size
+---
+:::ultralytics.yolo.utils.autobatch.check_train_batch_size
+<br><br>
+
+# autobatch
+---
+:::ultralytics.yolo.utils.autobatch.autobatch
+<br><br>

docs/reference/yolo/utils/benchmarks.md

@@ -0,0 +1,4 @@
+# benchmark
+---
+:::ultralytics.yolo.utils.benchmarks.benchmark
+<br><br>

docs/reference/yolo/utils/callbacks/base.md

@@ -0,0 +1,134 @@
+# on_pretrain_routine_start
+---
+:::ultralytics.yolo.utils.callbacks.base.on_pretrain_routine_start
+<br><br>
+
+# on_pretrain_routine_end
+---
+:::ultralytics.yolo.utils.callbacks.base.on_pretrain_routine_end
+<br><br>
+
+# on_train_start
+---
+:::ultralytics.yolo.utils.callbacks.base.on_train_start
+<br><br>
+
+# on_train_epoch_start
+---
+:::ultralytics.yolo.utils.callbacks.base.on_train_epoch_start
+<br><br>
+
+# on_train_batch_start
+---
+:::ultralytics.yolo.utils.callbacks.base.on_train_batch_start
+<br><br>
+
+# optimizer_step
+---
+:::ultralytics.yolo.utils.callbacks.base.optimizer_step
+<br><br>
+
+# on_before_zero_grad
+---
+:::ultralytics.yolo.utils.callbacks.base.on_before_zero_grad
+<br><br>
+
+# on_train_batch_end
+---
+:::ultralytics.yolo.utils.callbacks.base.on_train_batch_end
+<br><br>
+
+# on_train_epoch_end
+---
+:::ultralytics.yolo.utils.callbacks.base.on_train_epoch_end
+<br><br>
+
+# on_fit_epoch_end
+---
+:::ultralytics.yolo.utils.callbacks.base.on_fit_epoch_end
+<br><br>
+
+# on_model_save
+---
+:::ultralytics.yolo.utils.callbacks.base.on_model_save
+<br><br>
+
+# on_train_end
+---
+:::ultralytics.yolo.utils.callbacks.base.on_train_end
+<br><br>
+
+# on_params_update
+---
+:::ultralytics.yolo.utils.callbacks.base.on_params_update
+<br><br>
+
+# teardown
+---
+:::ultralytics.yolo.utils.callbacks.base.teardown
+<br><br>
+
+# on_val_start
+---
+:::ultralytics.yolo.utils.callbacks.base.on_val_start
+<br><br>
+
+# on_val_batch_start
+---
+:::ultralytics.yolo.utils.callbacks.base.on_val_batch_start
+<br><br>
+
+# on_val_batch_end
+---
+:::ultralytics.yolo.utils.callbacks.base.on_val_batch_end
+<br><br>
+
+# on_val_end
+---
+:::ultralytics.yolo.utils.callbacks.base.on_val_end
+<br><br>
+
+# on_predict_start
+---
+:::ultralytics.yolo.utils.callbacks.base.on_predict_start
+<br><br>
+
+# on_predict_batch_start
+---
+:::ultralytics.yolo.utils.callbacks.base.on_predict_batch_start
+<br><br>
+
+# on_predict_batch_end
+---
+:::ultralytics.yolo.utils.callbacks.base.on_predict_batch_end
+<br><br>
+
+# on_predict_postprocess_end
+---
+:::ultralytics.yolo.utils.callbacks.base.on_predict_postprocess_end
+<br><br>
+
+# on_predict_end
+---
+:::ultralytics.yolo.utils.callbacks.base.on_predict_end
+<br><br>
+
+# on_export_start
+---
+:::ultralytics.yolo.utils.callbacks.base.on_export_start
+<br><br>
+
+# on_export_end
+---
+:::ultralytics.yolo.utils.callbacks.base.on_export_end
+<br><br>
+
+# get_default_callbacks
+---
+:::ultralytics.yolo.utils.callbacks.base.get_default_callbacks
+<br><br>
+
+# add_integration_callbacks
+---
+:::ultralytics.yolo.utils.callbacks.base.add_integration_callbacks
+<br><br>

docs/reference/yolo/utils/callbacks/clearml.md

@@ -0,0 +1,34 @@
+# _log_debug_samples
+---
+:::ultralytics.yolo.utils.callbacks.clearml._log_debug_samples
+<br><br>
+
+# _log_plot
+---
+:::ultralytics.yolo.utils.callbacks.clearml._log_plot
+<br><br>
+
+# on_pretrain_routine_start
+---
+:::ultralytics.yolo.utils.callbacks.clearml.on_pretrain_routine_start
+<br><br>
+
+# on_train_epoch_end
+---
+:::ultralytics.yolo.utils.callbacks.clearml.on_train_epoch_end
+<br><br>
+
+# on_fit_epoch_end
+---
+:::ultralytics.yolo.utils.callbacks.clearml.on_fit_epoch_end
+<br><br>
+
+# on_val_end
+---
+:::ultralytics.yolo.utils.callbacks.clearml.on_val_end
+<br><br>
+
+# on_train_end
+---
+:::ultralytics.yolo.utils.callbacks.clearml.on_train_end
+<br><br>

docs/reference/yolo/utils/callbacks/comet.md

@@ -0,0 +1,84 @@
+# _get_experiment_type
+---
+:::ultralytics.yolo.utils.callbacks.comet._get_experiment_type
+<br><br>
+
+# _create_experiment
+---
+:::ultralytics.yolo.utils.callbacks.comet._create_experiment
+<br><br>
+
+# _fetch_trainer_metadata
+---
+:::ultralytics.yolo.utils.callbacks.comet._fetch_trainer_metadata
+<br><br>
+
+# _scale_bounding_box_to_original_image_shape
+---
+:::ultralytics.yolo.utils.callbacks.comet._scale_bounding_box_to_original_image_shape
+<br><br>
+
+# _format_ground_truth_annotations_for_detection
+---
+:::ultralytics.yolo.utils.callbacks.comet._format_ground_truth_annotations_for_detection
+<br><br>
+
+# _format_prediction_annotations_for_detection
+---
+:::ultralytics.yolo.utils.callbacks.comet._format_prediction_annotations_for_detection
+<br><br>
+
+# _fetch_annotations
+---
+:::ultralytics.yolo.utils.callbacks.comet._fetch_annotations
+<br><br>
+
+# _create_prediction_metadata_map
+---
+:::ultralytics.yolo.utils.callbacks.comet._create_prediction_metadata_map
+<br><br>
+
+# _log_confusion_matrix
+---
+:::ultralytics.yolo.utils.callbacks.comet._log_confusion_matrix
+<br><br>
+
+# _log_images
+---
+:::ultralytics.yolo.utils.callbacks.comet._log_images
+<br><br>
+
+# _log_image_predictions
+---
+:::ultralytics.yolo.utils.callbacks.comet._log_image_predictions
+<br><br>
+
+# _log_plots
+---
+:::ultralytics.yolo.utils.callbacks.comet._log_plots
+<br><br>
+
+# _log_model
+---
+:::ultralytics.yolo.utils.callbacks.comet._log_model
+<br><br>
+
+# on_pretrain_routine_start
+---
+:::ultralytics.yolo.utils.callbacks.comet.on_pretrain_routine_start
+<br><br>
+
+# on_train_epoch_end
+---
+:::ultralytics.yolo.utils.callbacks.comet.on_train_epoch_end
+<br><br>
+
+# on_fit_epoch_end
+---
+:::ultralytics.yolo.utils.callbacks.comet.on_fit_epoch_end
+<br><br>
+
+# on_train_end
+---
+:::ultralytics.yolo.utils.callbacks.comet.on_train_end
+<br><br>

docs/reference/yolo/utils/callbacks/hub.md

@@ -0,0 +1,39 @@
+# on_pretrain_routine_end
+---
+:::ultralytics.yolo.utils.callbacks.hub.on_pretrain_routine_end
+<br><br>
+
+# on_fit_epoch_end
+---
+:::ultralytics.yolo.utils.callbacks.hub.on_fit_epoch_end
+<br><br>
+
+# on_model_save
+---
+:::ultralytics.yolo.utils.callbacks.hub.on_model_save
+<br><br>
+
+# on_train_end
+---
+:::ultralytics.yolo.utils.callbacks.hub.on_train_end
+<br><br>
+
+# on_train_start
+---
+:::ultralytics.yolo.utils.callbacks.hub.on_train_start
+<br><br>
+
+# on_val_start
+---
+:::ultralytics.yolo.utils.callbacks.hub.on_val_start
+<br><br>
+
+# on_predict_start
+---
+:::ultralytics.yolo.utils.callbacks.hub.on_predict_start
+<br><br>
+
+# on_export_start
+---
+:::ultralytics.yolo.utils.callbacks.hub.on_export_start
+<br><br>

docs/reference/yolo/utils/callbacks/mlflow.md

@@ -0,0 +1,19 @@
+# on_pretrain_routine_end
+---
+:::ultralytics.yolo.utils.callbacks.mlflow.on_pretrain_routine_end
+<br><br>
+
+# on_fit_epoch_end
+---
+:::ultralytics.yolo.utils.callbacks.mlflow.on_fit_epoch_end
+<br><br>
+
+# on_model_save
+---
+:::ultralytics.yolo.utils.callbacks.mlflow.on_model_save
+<br><br>
+
+# on_train_end
+---
+:::ultralytics.yolo.utils.callbacks.mlflow.on_train_end
+<br><br>

docs/reference/yolo/utils/callbacks/raytune.md

@@ -0,0 +1,4 @@
+# on_fit_epoch_end
+---
+:::ultralytics.yolo.utils.callbacks.raytune.on_fit_epoch_end
+<br><br>

docs/reference/yolo/utils/callbacks/tensorboard.md

@@ -0,0 +1,19 @@
+# _log_scalars
+---
+:::ultralytics.yolo.utils.callbacks.tensorboard._log_scalars
+<br><br>
+
+# on_pretrain_routine_start
+---
+:::ultralytics.yolo.utils.callbacks.tensorboard.on_pretrain_routine_start
+<br><br>
+
+# on_batch_end
+---
+:::ultralytics.yolo.utils.callbacks.tensorboard.on_batch_end
+<br><br>
+
+# on_fit_epoch_end
+---
+:::ultralytics.yolo.utils.callbacks.tensorboard.on_fit_epoch_end
+<br><br>

docs/reference/yolo/utils/callbacks/wb.md

@@ -0,0 +1,19 @@
+# on_pretrain_routine_start
+---
+:::ultralytics.yolo.utils.callbacks.wb.on_pretrain_routine_start
+<br><br>
+
+# on_fit_epoch_end
+---
+:::ultralytics.yolo.utils.callbacks.wb.on_fit_epoch_end
+<br><br>
+
+# on_train_epoch_end
+---
+:::ultralytics.yolo.utils.callbacks.wb.on_train_epoch_end
+<br><br>
+
+# on_train_end
+---
+:::ultralytics.yolo.utils.callbacks.wb.on_train_end
+<br><br>

docs/reference/yolo/utils/checks.md

@@ -0,0 +1,79 @@
+# is_ascii
+---
+:::ultralytics.yolo.utils.checks.is_ascii
+<br><br>
+
+# check_imgsz
+---
+:::ultralytics.yolo.utils.checks.check_imgsz
+<br><br>
+
+# check_version
+---
+:::ultralytics.yolo.utils.checks.check_version
+<br><br>
+
+# check_latest_pypi_version
+---
+:::ultralytics.yolo.utils.checks.check_latest_pypi_version
+<br><br>
+
+# check_pip_update_available
+---
+:::ultralytics.yolo.utils.checks.check_pip_update_available
+<br><br>
+
+# check_font
+---
+:::ultralytics.yolo.utils.checks.check_font
+<br><br>
+
+# check_python
+---
+:::ultralytics.yolo.utils.checks.check_python
+<br><br>
+
+# check_requirements
+---
+:::ultralytics.yolo.utils.checks.check_requirements
+<br><br>
+
+# check_suffix
+---
+:::ultralytics.yolo.utils.checks.check_suffix
+<br><br>
+
+# check_yolov5u_filename
+---
+:::ultralytics.yolo.utils.checks.check_yolov5u_filename
+<br><br>
+
+# check_file
+---
+:::ultralytics.yolo.utils.checks.check_file
+<br><br>
+
+# check_yaml
+---
+:::ultralytics.yolo.utils.checks.check_yaml
+<br><br>
+
+# check_imshow
+---
+:::ultralytics.yolo.utils.checks.check_imshow
+<br><br>
+
+# check_yolo
+---
+:::ultralytics.yolo.utils.checks.check_yolo
+<br><br>
+
+# git_describe
+---
+:::ultralytics.yolo.utils.checks.git_describe
+<br><br>
+
+# print_args
+---
+:::ultralytics.yolo.utils.checks.print_args
+<br><br>

docs/reference/yolo/utils/dist.md

@@ -0,0 +1,19 @@
+# find_free_network_port
+---
+:::ultralytics.yolo.utils.dist.find_free_network_port
+<br><br>
+
+# generate_ddp_file
+---
+:::ultralytics.yolo.utils.dist.generate_ddp_file
+<br><br>
+
+# generate_ddp_command
+---
+:::ultralytics.yolo.utils.dist.generate_ddp_command
+<br><br>
+
+# ddp_cleanup
+---
+:::ultralytics.yolo.utils.dist.ddp_cleanup
+<br><br>

docs/reference/yolo/utils/downloads.md

@@ -0,0 +1,24 @@
+# is_url
+---
+:::ultralytics.yolo.utils.downloads.is_url
+<br><br>
+
+# unzip_file
+---
+:::ultralytics.yolo.utils.downloads.unzip_file
+<br><br>
+
+# safe_download
+---
+:::ultralytics.yolo.utils.downloads.safe_download
+<br><br>
+
+# attempt_download_asset
+---
+:::ultralytics.yolo.utils.downloads.attempt_download_asset
+<br><br>
+
+# download
+---
+:::ultralytics.yolo.utils.downloads.download
+<br><br>

docs/reference/yolo/utils/errors.md

@@ -0,0 +1,4 @@
+# HUBModelError
+---
+:::ultralytics.yolo.utils.errors.HUBModelError
+<br><br>

docs/reference/yolo/utils/files.md

@@ -0,0 +1,29 @@
+# WorkingDirectory
+---
+:::ultralytics.yolo.utils.files.WorkingDirectory
+<br><br>
+
+# increment_path
+---
+:::ultralytics.yolo.utils.files.increment_path
+<br><br>
+
+# file_age
+---
+:::ultralytics.yolo.utils.files.file_age
+<br><br>
+
+# file_date
+---
+:::ultralytics.yolo.utils.files.file_date
+<br><br>
+
+# file_size
+---
+:::ultralytics.yolo.utils.files.file_size
+<br><br>
+
+# get_latest_run
+---
+:::ultralytics.yolo.utils.files.get_latest_run
+<br><br>

docs/reference/yolo/utils/instance.md

@@ -0,0 +1,14 @@
+# Bboxes
+---
+:::ultralytics.yolo.utils.instance.Bboxes
+<br><br>
+
+# Instances
+---
+:::ultralytics.yolo.utils.instance.Instances
+<br><br>
+
+# _ntuple
+---
+:::ultralytics.yolo.utils.instance._ntuple
+<br><br>

docs/reference/yolo/utils/loss.md

@@ -0,0 +1,14 @@
+# VarifocalLoss
+---
+:::ultralytics.yolo.utils.loss.VarifocalLoss
+<br><br>
+
+# BboxLoss
+---
+:::ultralytics.yolo.utils.loss.BboxLoss
+<br><br>
+
+# KeypointLoss
+---
+:::ultralytics.yolo.utils.loss.KeypointLoss
+<br><br>

docs/reference/yolo/utils/metrics.md

@@ -0,0 +1,94 @@
+# FocalLoss
+---
+:::ultralytics.yolo.utils.metrics.FocalLoss
+<br><br>
+
+# ConfusionMatrix
+---
+:::ultralytics.yolo.utils.metrics.ConfusionMatrix
+<br><br>
+
+# Metric
+---
+:::ultralytics.yolo.utils.metrics.Metric
+<br><br>
+
+# DetMetrics
+---
+:::ultralytics.yolo.utils.metrics.DetMetrics
+<br><br>
+
+# SegmentMetrics
+---
+:::ultralytics.yolo.utils.metrics.SegmentMetrics
+<br><br>
+
+# PoseMetrics
+---
+:::ultralytics.yolo.utils.metrics.PoseMetrics
+<br><br>
+
+# ClassifyMetrics
+---
+:::ultralytics.yolo.utils.metrics.ClassifyMetrics
+<br><br>
+
+# box_area
+---
+:::ultralytics.yolo.utils.metrics.box_area
+<br><br>
+
+# bbox_ioa
+---
+:::ultralytics.yolo.utils.metrics.bbox_ioa
+<br><br>
+
+# box_iou
+---
+:::ultralytics.yolo.utils.metrics.box_iou
+<br><br>
+
+# bbox_iou
+---
+:::ultralytics.yolo.utils.metrics.bbox_iou
+<br><br>
+
+# mask_iou
+---
+:::ultralytics.yolo.utils.metrics.mask_iou
+<br><br>
+
+# kpt_iou
+---
+:::ultralytics.yolo.utils.metrics.kpt_iou
+<br><br>
+
+# smooth_BCE
+---
+:::ultralytics.yolo.utils.metrics.smooth_BCE
+<br><br>
+
+# smooth
+---
+:::ultralytics.yolo.utils.metrics.smooth
+<br><br>
+
+# plot_pr_curve
+---
+:::ultralytics.yolo.utils.metrics.plot_pr_curve
+<br><br>
+
+# plot_mc_curve
+---
+:::ultralytics.yolo.utils.metrics.plot_mc_curve
+<br><br>
+
+# compute_ap
+---
+:::ultralytics.yolo.utils.metrics.compute_ap
+<br><br>
+
+# ap_per_class
+---
+:::ultralytics.yolo.utils.metrics.ap_per_class
+<br><br>

docs/reference/yolo/utils/ops.md

@@ -0,0 +1,134 @@
+# Profile
+---
+:::ultralytics.yolo.utils.ops.Profile
+<br><br>
+
+# coco80_to_coco91_class
+---
+:::ultralytics.yolo.utils.ops.coco80_to_coco91_class
+<br><br>
+
+# segment2box
+---
+:::ultralytics.yolo.utils.ops.segment2box
+<br><br>
+
+# scale_boxes
+---
+:::ultralytics.yolo.utils.ops.scale_boxes
+<br><br>
+
+# make_divisible
+---
+:::ultralytics.yolo.utils.ops.make_divisible
+<br><br>
+
+# non_max_suppression
+---
+:::ultralytics.yolo.utils.ops.non_max_suppression
+<br><br>
+
+# clip_boxes
+---
+:::ultralytics.yolo.utils.ops.clip_boxes
+<br><br>
+
+# clip_coords
+---
+:::ultralytics.yolo.utils.ops.clip_coords
+<br><br>
+
+# scale_image
+---
+:::ultralytics.yolo.utils.ops.scale_image
+<br><br>
+
+# xyxy2xywh
+---
+:::ultralytics.yolo.utils.ops.xyxy2xywh
+<br><br>
+
+# xywh2xyxy
+---
+:::ultralytics.yolo.utils.ops.xywh2xyxy
+<br><br>
+
+# xywhn2xyxy
+---
+:::ultralytics.yolo.utils.ops.xywhn2xyxy
+<br><br>
+
+# xyxy2xywhn
+---
+:::ultralytics.yolo.utils.ops.xyxy2xywhn
+<br><br>
+
+# xyn2xy
+---
+:::ultralytics.yolo.utils.ops.xyn2xy
+<br><br>
+
+# xywh2ltwh
+---
+:::ultralytics.yolo.utils.ops.xywh2ltwh
+<br><br>
+
+# xyxy2ltwh
+---
+:::ultralytics.yolo.utils.ops.xyxy2ltwh
+<br><br>
+
+# ltwh2xywh
+---
+:::ultralytics.yolo.utils.ops.ltwh2xywh
+<br><br>
+
+# ltwh2xyxy
+---
+:::ultralytics.yolo.utils.ops.ltwh2xyxy
+<br><br>
+
+# segments2boxes
+---
+:::ultralytics.yolo.utils.ops.segments2boxes
+<br><br>
+
+# resample_segments
+---
+:::ultralytics.yolo.utils.ops.resample_segments
+<br><br>
+
+# crop_mask
+---
+:::ultralytics.yolo.utils.ops.crop_mask
+<br><br>
+
+# process_mask_upsample
+---
+:::ultralytics.yolo.utils.ops.process_mask_upsample
+<br><br>
+
+# process_mask
+---
+:::ultralytics.yolo.utils.ops.process_mask
+<br><br>
+
+# process_mask_native
+---
+:::ultralytics.yolo.utils.ops.process_mask_native
+<br><br>
+
+# scale_coords
+---
+:::ultralytics.yolo.utils.ops.scale_coords
+<br><br>
+
+# masks2segments
+---
+:::ultralytics.yolo.utils.ops.masks2segments
+<br><br>
+
+# clean_str
+---
+:::ultralytics.yolo.utils.ops.clean_str
+<br><br>

docs/reference/yolo/utils/plotting.md

@@ -0,0 +1,34 @@
+# Colors
+---
+:::ultralytics.yolo.utils.plotting.Colors
+<br><br>
+
+# Annotator
+---
+:::ultralytics.yolo.utils.plotting.Annotator
+<br><br>
+
+# plot_labels
+---
+:::ultralytics.yolo.utils.plotting.plot_labels
+<br><br>
+
+# save_one_box
+---
+:::ultralytics.yolo.utils.plotting.save_one_box
+<br><br>
+
+# plot_images
+---
+:::ultralytics.yolo.utils.plotting.plot_images
+<br><br>
+
+# plot_results
+---
+:::ultralytics.yolo.utils.plotting.plot_results
+<br><br>
+
+# output_to_target
+---
+:::ultralytics.yolo.utils.plotting.output_to_target
+<br><br>

docs/reference/yolo/utils/tal.md

@@ -0,0 +1,29 @@
+# TaskAlignedAssigner
+---
+:::ultralytics.yolo.utils.tal.TaskAlignedAssigner
+<br><br>
+
+# select_candidates_in_gts
+---
+:::ultralytics.yolo.utils.tal.select_candidates_in_gts
+<br><br>
+
+# select_highest_overlaps
+---
+:::ultralytics.yolo.utils.tal.select_highest_overlaps
+<br><br>
+
+# make_anchors
+---
+:::ultralytics.yolo.utils.tal.make_anchors
+<br><br>
+
+# dist2bbox
+---
+:::ultralytics.yolo.utils.tal.dist2bbox
+<br><br>
+
+# bbox2dist
+---
+:::ultralytics.yolo.utils.tal.bbox2dist
+<br><br>

docs/reference/yolo/utils/torch_utils.md

@@ -0,0 +1,119 @@
+# ModelEMA
+---
+:::ultralytics.yolo.utils.torch_utils.ModelEMA
+<br><br>
+
+# EarlyStopping
+---
+:::ultralytics.yolo.utils.torch_utils.EarlyStopping
+<br><br>
+
+# torch_distributed_zero_first
+---
+:::ultralytics.yolo.utils.torch_utils.torch_distributed_zero_first
+<br><br>
+
+# smart_inference_mode
+---
+:::ultralytics.yolo.utils.torch_utils.smart_inference_mode
+<br><br>
+
+# select_device
+---
+:::ultralytics.yolo.utils.torch_utils.select_device
+<br><br>
+
+# time_sync
+---
+:::ultralytics.yolo.utils.torch_utils.time_sync
+<br><br>
+
+# fuse_conv_and_bn
+---
+:::ultralytics.yolo.utils.torch_utils.fuse_conv_and_bn
+<br><br>
+
+# fuse_deconv_and_bn
+---
+:::ultralytics.yolo.utils.torch_utils.fuse_deconv_and_bn
+<br><br>
+
+# model_info
+---
+:::ultralytics.yolo.utils.torch_utils.model_info
+<br><br>
+
+# get_num_params
+---
+:::ultralytics.yolo.utils.torch_utils.get_num_params
+<br><br>
+
+# get_num_gradients
+---
+:::ultralytics.yolo.utils.torch_utils.get_num_gradients
+<br><br>
+
+# get_flops
+---
+:::ultralytics.yolo.utils.torch_utils.get_flops
+<br><br>
+
+# initialize_weights
+---
+:::ultralytics.yolo.utils.torch_utils.initialize_weights
+<br><br>
+
+# scale_img
+---
+:::ultralytics.yolo.utils.torch_utils.scale_img
+<br><br>
+
+# make_divisible
+---
+:::ultralytics.yolo.utils.torch_utils.make_divisible
+<br><br>
+
+# copy_attr
+---
+:::ultralytics.yolo.utils.torch_utils.copy_attr
+<br><br>
+
+# get_latest_opset
+---
+:::ultralytics.yolo.utils.torch_utils.get_latest_opset
+<br><br>
+
+# intersect_dicts
+---
+:::ultralytics.yolo.utils.torch_utils.intersect_dicts
+<br><br>
+
+# is_parallel
+---
+:::ultralytics.yolo.utils.torch_utils.is_parallel
+<br><br>
+
+# de_parallel
+---
+:::ultralytics.yolo.utils.torch_utils.de_parallel
+<br><br>
+
+# one_cycle
+---
+:::ultralytics.yolo.utils.torch_utils.one_cycle
+<br><br>
+
+# init_seeds
+---
+:::ultralytics.yolo.utils.torch_utils.init_seeds
+<br><br>
+
+# strip_optimizer
+---
+:::ultralytics.yolo.utils.torch_utils.strip_optimizer
+<br><br>
+
+# profile
+---
+:::ultralytics.yolo.utils.torch_utils.profile
+<br><br>

docs/reference/yolo/v8/classify/predict.md

@@ -0,0 +1,9 @@
+# ClassificationPredictor
+---
+:::ultralytics.yolo.v8.classify.predict.ClassificationPredictor
+<br><br>
+
+# predict
+---
+:::ultralytics.yolo.v8.classify.predict.predict
+<br><br>

docs/reference/yolo/v8/classify/train.md

@@ -0,0 +1,9 @@
+# ClassificationTrainer
+---
+:::ultralytics.yolo.v8.classify.train.ClassificationTrainer
+<br><br>
+
+# train
+---
+:::ultralytics.yolo.v8.classify.train.train
+<br><br>

docs/reference/yolo/v8/classify/val.md

@@ -0,0 +1,9 @@
+# ClassificationValidator
+---
+:::ultralytics.yolo.v8.classify.val.ClassificationValidator
+<br><br>
+
+# val
+---
+:::ultralytics.yolo.v8.classify.val.val
+<br><br>

docs/reference/yolo/v8/detect/predict.md

@@ -0,0 +1,9 @@
+# DetectionPredictor
+---
+:::ultralytics.yolo.v8.detect.predict.DetectionPredictor
+<br><br>
+
+# predict
+---
+:::ultralytics.yolo.v8.detect.predict.predict
+<br><br>

docs/reference/yolo/v8/detect/train.md

@@ -0,0 +1,14 @@
+# DetectionTrainer
+---
+:::ultralytics.yolo.v8.detect.train.DetectionTrainer
+<br><br>
+
+# Loss
+---
+:::ultralytics.yolo.v8.detect.train.Loss
+<br><br>
+
+# train
+---
+:::ultralytics.yolo.v8.detect.train.train
+<br><br>

docs/reference/yolo/v8/detect/val.md

@@ -0,0 +1,9 @@
+# DetectionValidator
+---
+:::ultralytics.yolo.v8.detect.val.DetectionValidator
+<br><br>
+
+# val
+---
+:::ultralytics.yolo.v8.detect.val.val
+<br><br>

docs/reference/yolo/v8/pose/predict.md

@@ -0,0 +1,9 @@
+# PosePredictor
+---
+:::ultralytics.yolo.v8.pose.predict.PosePredictor
+<br><br>
+
+# predict
+---
+:::ultralytics.yolo.v8.pose.predict.predict
+<br><br>

docs/reference/yolo/v8/pose/train.md

@@ -0,0 +1,14 @@
+# PoseTrainer
+---
+:::ultralytics.yolo.v8.pose.train.PoseTrainer
+<br><br>
+
+# PoseLoss
+---
+:::ultralytics.yolo.v8.pose.train.PoseLoss
+<br><br>
+
+# train
+---
+:::ultralytics.yolo.v8.pose.train.train
+<br><br>

docs/reference/yolo/v8/pose/val.md

@@ -0,0 +1,9 @@
+# PoseValidator
+---
+:::ultralytics.yolo.v8.pose.val.PoseValidator
+<br><br>
+
+# val
+---
+:::ultralytics.yolo.v8.pose.val.val
+<br><br>

docs/reference/yolo/v8/segment/predict.md

@@ -0,0 +1,9 @@
+# SegmentationPredictor
+---
+:::ultralytics.yolo.v8.segment.predict.SegmentationPredictor
+<br><br>
+
+# predict
+---
+:::ultralytics.yolo.v8.segment.predict.predict
+<br><br>

docs/reference/yolo/v8/segment/train.md

@@ -0,0 +1,14 @@
+# SegmentationTrainer
+---
+:::ultralytics.yolo.v8.segment.train.SegmentationTrainer
+<br><br>
+
+# SegLoss
+---
+:::ultralytics.yolo.v8.segment.train.SegLoss
+<br><br>
+
+# train
+---
+:::ultralytics.yolo.v8.segment.train.train
+<br><br>

docs/reference/yolo/v8/segment/val.md

@@ -0,0 +1,9 @@
+# SegmentationValidator
+---
+:::ultralytics.yolo.v8.segment.val.SegmentationValidator
+<br><br>
+
+# val
+---
+:::ultralytics.yolo.v8.segment.val.val
+<br><br>

docs/usage/engine.md

@@ -9,7 +9,7 @@ custom model and dataloader by just overriding these functions:
 
 * `get_model(cfg, weights)` - The function that builds the model to be trained
 * `get_dataloder()` - The function that builds the dataloader
-  More details and source code can be found in [`BaseTrainer` Reference](../reference/base_trainer.md)
+  More details and source code can be found in [`BaseTrainer` Reference](../reference/yolo/engine/trainer.md)
 
 ## DetectionTrainer
 

mkdocs.yml

@@ -37,8 +37,9 @@ theme:
     - search.share
     - search.suggest
     - toc.follow
+    - toc.integrate
     - navigation.top
-    - navigation.expand
+    - navigation.tabs
     - navigation.footer
     - navigation.tracking
     - navigation.instant
@@ -143,16 +144,88 @@ nav:
       - Advanced Customization: usage/engine.md
   - Ultralytics HUB: hub.md
   - iOS and Android App: app.md
+
   - Reference:
-      - Engine:
-          - Model: reference/model.md
-          - Trainer: reference/base_trainer.md
-          - Validator: reference/base_val.md
-          - Predictor: reference/base_pred.md
-          - Exporter: reference/exporter.md
-          - Results: reference/results.md
-      - ultralytics.nn: reference/nn.md
-      - Operations: reference/ops.md
+    - hub:
+      - auth: reference/hub/auth.md
+      - session: reference/hub/session.md
+      - utils: reference/hub/utils.md
+    - nn:
+      - autobackend: reference/nn/autobackend.md
+      - autoshape: reference/nn/autoshape.md
+      - modules: reference/nn/modules.md
+      - tasks: reference/nn/tasks.md
+    - tracker:
+      - track: reference/tracker/track.md
+      - trackers:
+        - basetrack: reference/tracker/trackers/basetrack.md
+        - bot_sort: reference/tracker/trackers/bot_sort.md
+        - byte_tracker: reference/tracker/trackers/byte_tracker.md
+      - utils:
+        - gmc: reference/tracker/utils/gmc.md
+        - kalman_filter: reference/tracker/utils/kalman_filter.md
+        - matching: reference/tracker/utils/matching.md
+    - yolo:
+      - data:
+        - augment: reference/yolo/data/augment.md
+        - base: reference/yolo/data/base.md
+        - build: reference/yolo/data/build.md
+        - dataloaders:
+          - stream_loaders: reference/yolo/data/dataloaders/stream_loaders.md
+          - v5augmentations: reference/yolo/data/dataloaders/v5augmentations.md
+          - v5loader: reference/yolo/data/dataloaders/v5loader.md
+        - dataset: reference/yolo/data/dataset.md
+        - dataset_wrappers: reference/yolo/data/dataset_wrappers.md
+        - utils: reference/yolo/data/utils.md
+      - engine:
+        - exporter: reference/yolo/engine/exporter.md
+        - model: reference/yolo/engine/model.md
+        - predictor: reference/yolo/engine/predictor.md
+        - results: reference/yolo/engine/results.md
+        - trainer: reference/yolo/engine/trainer.md
+        - validator: reference/yolo/engine/validator.md
+      - utils:
+        - autobatch: reference/yolo/utils/autobatch.md
+        - benchmarks: reference/yolo/utils/benchmarks.md
+        - callbacks:
+          - base: reference/yolo/utils/callbacks/base.md
+          - clearml: reference/yolo/utils/callbacks/clearml.md
+          - comet: reference/yolo/utils/callbacks/comet.md
+          - hub: reference/yolo/utils/callbacks/hub.md
+          - mlflow: reference/yolo/utils/callbacks/mlflow.md
+          - raytune: reference/yolo/utils/callbacks/raytune.md
+          - tensorboard: reference/yolo/utils/callbacks/tensorboard.md
+          - wb: reference/yolo/utils/callbacks/wb.md
+        - checks: reference/yolo/utils/checks.md
+        - dist: reference/yolo/utils/dist.md
+        - downloads: reference/yolo/utils/downloads.md
+        - errors: reference/yolo/utils/errors.md
+        - files: reference/yolo/utils/files.md
+        - instance: reference/yolo/utils/instance.md
+        - loss: reference/yolo/utils/loss.md
+        - metrics: reference/yolo/utils/metrics.md
+        - ops: reference/yolo/utils/ops.md
+        - plotting: reference/yolo/utils/plotting.md
+        - tal: reference/yolo/utils/tal.md
+        - torch_utils: reference/yolo/utils/torch_utils.md
+      - v8:
+        - classify:
+          - predict: reference/yolo/v8/classify/predict.md
+          - train: reference/yolo/v8/classify/train.md
+          - val: reference/yolo/v8/classify/val.md
+        - detect:
+          - predict: reference/yolo/v8/detect/predict.md
+          - train: reference/yolo/v8/detect/train.md
+          - val: reference/yolo/v8/detect/val.md
+        - pose:
+          - predict: reference/yolo/v8/pose/predict.md
+          - train: reference/yolo/v8/pose/train.md
+          - val: reference/yolo/v8/pose/val.md
+        - segment:
+          - predict: reference/yolo/v8/segment/predict.md
+          - train: reference/yolo/v8/segment/train.md
+          - val: reference/yolo/v8/segment/val.md
+
   - YOLOv5:
       - yolov5/index.md
       - Train Custom Data: yolov5/train_custom_data.md
@@ -171,5 +244,4 @@ nav:
       - Neural Magic's DeepSparse: yolov5/neural_magic.md
       - Comet Logging: yolov5/comet.md
       - Clearml Logging: yolov5/clearml.md
-
   - Security: SECURITY.md

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.78'
+__version__ = '8.0.79'
 
 from ultralytics.hub import start
 from ultralytics.yolo.engine.model import YOLO

ultralytics/hub/auth.py

@@ -111,7 +111,7 @@ class Auth:
         Get the authentication header for making API requests.
 
         Returns:
-            dict: The authentication header if id_token or API key is set, None otherwise.
+            (dict): The authentication header if id_token or API key is set, None otherwise.
         """
         if self.id_token:
             return {'authorization': f'Bearer {self.id_token}'}

ultralytics/hub/utils.py

@@ -30,7 +30,7 @@ def check_dataset_disk_space(url='https://ultralytics.com/assets/coco128.zip', s
         sf (float, optional): Safety factor, the multiplier for the required free space. Defaults to 2.0.
 
     Returns:
-        bool: True if there is sufficient disk space, False otherwise.
+        (bool): True if there is sufficient disk space, False otherwise.
     """
     gib = 1 << 30  # bytes per GiB
     data = int(requests.head(url).headers['Content-Length']) / gib  # dataset size (GB)
@@ -51,7 +51,7 @@ def request_with_credentials(url: str) -> any:
         url (str): The URL to make the request to.
 
     Returns:
-        any: The response data from the AJAX request.
+        (any): The response data from the AJAX request.
 
     Raises:
         OSError: If the function is not run in a Google Colab environment.
@@ -87,11 +87,14 @@ def requests_with_progress(method, url, **kwargs):
     Args:
         method (str): The HTTP method to use (e.g. 'GET', 'POST').
         url (str): The URL to send the request to.
-        progress (bool, optional): Whether to display a progress bar. Defaults to False.
-        **kwargs: Additional keyword arguments to pass to the underlying `requests.request` function.
+        **kwargs (dict): Additional keyword arguments to pass to the underlying `requests.request` function.
 
     Returns:
-        requests.Response: The response from the HTTP request.
+        (requests.Response): The response object from the HTTP request.
+
+    Note:
+        If 'progress' is set to True, the progress bar will display the download progress
+        for responses with a known content length.
     """
     progress = kwargs.pop('progress', False)
     if not progress:
@@ -118,10 +121,10 @@ def smart_request(method, url, retry=3, timeout=30, thread=True, code=-1, verbos
         code (int, optional): An identifier for the request, used for logging purposes. Default is -1.
         verbose (bool, optional): A flag to determine whether to print out to console or not. Default is True.
         progress (bool, optional): Whether to show a progress bar during the request. Default is False.
-        **kwargs: Keyword arguments to be passed to the requests function specified in method.
+        **kwargs (dict): Keyword arguments to be passed to the requests function specified in method.
 
     Returns:
-        requests.Response: The HTTP response object. If the request is executed in a separate thread, returns None.
+        (requests.Response): The HTTP response object. If the request is executed in a separate thread, returns None.
     """
     retry_codes = (408, 500)  # retry only these codes
 

ultralytics/nn/tasks.py

@@ -337,7 +337,7 @@ def torch_safe_load(weight):
         weight (str): The file path of the PyTorch model.
 
     Returns:
-        The loaded PyTorch model.
+        (dict): The loaded PyTorch model.
     """
     from ultralytics.yolo.utils.downloads import attempt_download_asset
 
@@ -398,7 +398,7 @@ def attempt_load_weights(weights, device=None, inplace=True, fuse=False):
     for k in 'names', 'nc', 'yaml':
         setattr(ensemble, k, getattr(ensemble[0], k))
     ensemble.stride = ensemble[torch.argmax(torch.tensor([m.stride.max() for m in ensemble])).int()].stride
-    assert all(ensemble[0].nc == m.nc for m in ensemble), f'Models differ in class counts: {[m.nc for m in ensemble]}'
+    assert all(ensemble[0].nc == m.nc for m in ensemble), f'Models differ in class counts {[m.nc for m in ensemble]}'
     return ensemble
 
 
@@ -520,7 +520,7 @@ def guess_model_scale(model_path):
     which is denoted by n, s, m, l, or x. The function returns the size character of the model scale as a string.
 
     Args:
-        model_path (str or Path): The path to the YOLO model's YAML file.
+        model_path (str) or (Path): The path to the YOLO model's YAML file.
 
     Returns:
         (str): The size character of the model's scale, which can be n, s, m, l, or x.
@@ -539,7 +539,7 @@ def guess_model_task(model):
         model (nn.Module) or (dict): PyTorch model or model configuration in YAML format.
 
     Returns:
-        str: Task of the model ('detect', 'segment', 'classify', 'pose').
+        (str): Task of the model ('detect', 'segment', 'classify', 'pose').
 
     Raises:
         SyntaxError: If the task of the model could not be determined.

ultralytics/tracker/utils/matching.py

@@ -190,11 +190,24 @@ def fuse_score(cost_matrix, detections):
 
 
 def bbox_ious(box1, box2, eps=1e-7):
-    """Boxes are x1y1x2y2
-    box1:       np.array of shape(nx4)
-    box2:       np.array of shape(mx4)
-    returns:    np.array of shape(nxm)
     """
+    Calculate the Intersection over Union (IoU) between pairs of bounding boxes.
+
+    Args:
+        box1 (np.array): A numpy array of shape (n, 4) representing 'n' bounding boxes.
+                         Each row is in the format (x1, y1, x2, y2).
+        box2 (np.array): A numpy array of shape (m, 4) representing 'm' bounding boxes.
+                         Each row is in the format (x1, y1, x2, y2).
+        eps (float, optional): A small constant to prevent division by zero. Defaults to 1e-7.
+
+    Returns:
+        (np.array): A numpy array of shape (n, m) representing the IoU scores for each pair
+                    of bounding boxes from box1 and box2.
+
+    Note:
+        The bounding box coordinates are expected to be in the format (x1, y1, x2, y2).
+    """
+
     # Get the coordinates of bounding boxes
     b1_x1, b1_y1, b1_x2, b1_y2 = box1.T
     b2_x1, b2_y1, b2_x2, b2_y2 = box2.T

ultralytics/yolo/data/augment.py

@@ -262,13 +262,15 @@ class RandomPerspective:
         return img, M, s
 
     def apply_bboxes(self, bboxes, M):
-        """apply affine to bboxes only.
+        """
+        Apply affine to bboxes only.
 
         Args:
-            bboxes(ndarray): list of bboxes, xyxy format, with shape (num_bboxes, 4).
-            M(ndarray): affine matrix.
+            bboxes (ndarray): list of bboxes, xyxy format, with shape (num_bboxes, 4).
+            M (ndarray): affine matrix.
+
         Returns:
-            new_bboxes(ndarray): bboxes after affine, [num_bboxes, 4].
+            new_bboxes (ndarray): bboxes after affine, [num_bboxes, 4].
         """
         n = len(bboxes)
         if n == 0:
@@ -285,14 +287,16 @@ class RandomPerspective:
         return np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1)), dtype=bboxes.dtype).reshape(4, n).T
 
     def apply_segments(self, segments, M):
-        """apply affine to segments and generate new bboxes from segments.
+        """
+        Apply affine to segments and generate new bboxes from segments.
 
         Args:
-            segments(ndarray): list of segments, [num_samples, 500, 2].
-            M(ndarray): affine matrix.
+            segments (ndarray): list of segments, [num_samples, 500, 2].
+            M (ndarray): affine matrix.
+
         Returns:
-            new_segments(ndarray): list of segments after affine, [num_samples, 500, 2].
-            new_bboxes(ndarray): bboxes after affine, [N, 4].
+            new_segments (ndarray): list of segments after affine, [num_samples, 500, 2].
+            new_bboxes (ndarray): bboxes after affine, [N, 4].
         """
         n, num = segments.shape[:2]
         if n == 0:
@@ -308,13 +312,15 @@ class RandomPerspective:
         return bboxes, segments
 
     def apply_keypoints(self, keypoints, M):
-        """apply affine to keypoints.
+        """
+        Apply affine to keypoints.
 
         Args:
-            keypoints(ndarray): keypoints, [N, 17, 3].
-            M(ndarray): affine matrix.
+            keypoints (ndarray): keypoints, [N, 17, 3].
+            M (ndarray): affine matrix.
+
         Return:
-            new_keypoints(ndarray): keypoints after affine, [N, 17, 3].
+            new_keypoints (ndarray): keypoints after affine, [N, 17, 3].
         """
         n, nkpt = keypoints.shape[:2]
         if n == 0:
@@ -333,7 +339,7 @@ class RandomPerspective:
         Affine images and targets.
 
         Args:
-            labels(Dict): a dict of `bboxes`, `segments`, `keypoints`.
+            labels (dict): a dict of `bboxes`, `segments`, `keypoints`.
         """
         if self.pre_transform and 'mosaic_border' not in labels:
             labels = self.pre_transform(labels)

ultralytics/yolo/data/base.py

@@ -18,11 +18,30 @@ from .utils import HELP_URL, IMG_FORMATS
 
 
 class BaseDataset(Dataset):
-    """Base Dataset.
+    """
+    Base dataset class for loading and processing image data.
+
     Args:
-        img_path (str): image path.
-        pipeline (dict): a dict of image transforms.
-        label_path (str): label path, this can also be an ann_file or other custom label path.
+        img_path (str): Image path.
+        imgsz (int): Target image size for resizing. Default is 640.
+        cache (bool): Cache images in memory or on disk for faster loading. Default is False.
+        augment (bool): Apply data augmentation. Default is True.
+        hyp (dict): Dictionary of hyperparameters for data augmentation. Default is None.
+        prefix (str): Prefix for file paths. Default is an empty string.
+        rect (bool): Enable rectangular training. Default is False.
+        batch_size (int): Batch size for rectangular training. Default is None.
+        stride (int): Stride for rectangular training. Default is 32.
+        pad (float): Padding for rectangular training. Default is 0.5.
+        single_cls (bool): Use a single class for all labels. Default is False.
+        classes (list): List of included classes. Default is None.
+
+    Attributes:
+        im_files (list): List of image file paths.
+        labels (list): List of label data dictionaries.
+        ni (int): Number of images in the dataset.
+        ims (list): List of loaded images.
+        npy_files (list): List of numpy file paths.
+        transforms (callable): Image transformation function.
     """
 
     def __init__(self,

ultralytics/yolo/data/build.py

@@ -21,10 +21,7 @@ from .utils import PIN_MEMORY
 
 
 class InfiniteDataLoader(dataloader.DataLoader):
-    """Dataloader that reuses workers
-
-    Uses same syntax as vanilla DataLoader
-    """
+    """Dataloader that reuses workers. Uses same syntax as vanilla DataLoader."""
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -40,10 +37,11 @@ class InfiniteDataLoader(dataloader.DataLoader):
 
 
 class _RepeatSampler:
-    """Sampler that repeats forever
+    """
+    Sampler that repeats forever.
 
     Args:
-        sampler (Sampler)
+        sampler (Dataset.sampler): The sampler to repeat.
     """
 
     def __init__(self, sampler):
@@ -173,7 +171,7 @@ def load_inference_source(source=None, transforms=None, imgsz=640, vid_stride=1,
         auto (bool, optional): Automatically apply pre-processing. Default is True.
 
     Returns:
-        dataset: A dataset object for the specified input source.
+        dataset (Dataset): A dataset object for the specified input source.
     """
     source, webcam, screenshot, from_img, in_memory, tensor = check_source(source)
     source_type = source.source_type if in_memory else SourceTypes(webcam, screenshot, from_img, tensor)

ultralytics/yolo/data/dataloaders/v5loader.py

@@ -178,7 +178,7 @@ class _RepeatSampler:
     """ Sampler that repeats forever
 
     Args:
-        sampler (Sampler)
+        sampler (Dataset.sampler): The sampler to repeat.
     """
 
     def __init__(self, sampler):

ultralytics/yolo/data/dataset.py

@@ -17,31 +17,31 @@ from .utils import HELP_URL, LOGGER, get_hash, img2label_paths, verify_image_lab
 
 
 class YOLODataset(BaseDataset):
-    cache_version = '1.0.2'  # dataset labels *.cache version, >= 1.0.0 for YOLOv8
-    rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4]
     """
-    Dataset class for loading images object detection and/or segmentation labels in YOLO format.
+    Dataset class for loading object detection and/or segmentation labels in YOLO format.
 
     Args:
-        img_path (str): path to the folder containing images.
-        imgsz (int): image size (default: 640).
-        cache (bool): if True, a cache file of the labels is created to speed up future creation of dataset instances
-        (default: False).
-        augment (bool): if True, data augmentation is applied (default: True).
-        hyp (dict): hyperparameters to apply data augmentation (default: None).
-        prefix (str): prefix to print in log messages (default: '').
-        rect (bool): if True, rectangular training is used (default: False).
-        batch_size (int): size of batches (default: None).
-        stride (int): stride (default: 32).
-        pad (float): padding (default: 0.0).
-        single_cls (bool): if True, single class training is used (default: False).
-        use_segments (bool): if True, segmentation masks are used as labels (default: False).
-        use_keypoints (bool): if True, keypoints are used as labels (default: False).
-        names (dict): A dictionary of class names. (default: None).
+        img_path (str): Path to the folder containing images.
+        imgsz (int, optional): Image size. Defaults to 640.
+        cache (bool, optional): Cache images to RAM or disk during training. Defaults to False.
+        augment (bool, optional): If True, data augmentation is applied. Defaults to True.
+        hyp (dict, optional): Hyperparameters to apply data augmentation. Defaults to None.
+        prefix (str, optional): Prefix to print in log messages. Defaults to ''.
+        rect (bool, optional): If True, rectangular training is used. Defaults to False.
+        batch_size (int, optional): Size of batches. Defaults to None.
+        stride (int, optional): Stride. Defaults to 32.
+        pad (float, optional): Padding. Defaults to 0.0.
+        single_cls (bool, optional): If True, single class training is used. Defaults to False.
+        use_segments (bool, optional): If True, segmentation masks are used as labels. Defaults to False.
+        use_keypoints (bool, optional): If True, keypoints are used as labels. Defaults to False.
+        data (dict, optional): A dataset YAML dictionary. Defaults to None.
+        classes (list): List of included classes. Default is None.
 
     Returns:
-        A PyTorch dataset object that can be used for training an object detection or segmentation model.
+        (torch.utils.data.Dataset): A PyTorch dataset object that can be used for training an object detection model.
     """
+    cache_version = '1.0.2'  # dataset labels *.cache version, >= 1.0.0 for YOLOv8
+    rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4]
 
     def __init__(self,
                  img_path,

ultralytics/yolo/data/dataset_wrappers.py

@@ -7,21 +7,36 @@ from .augment import LetterBox
 
 
 class MixAndRectDataset:
-    """A wrapper of multiple images mixed dataset.
+    """
+    A dataset class that applies mosaic and mixup transformations as well as rectangular training.
 
-    Args:
-        dataset (:obj:`BaseDataset`): The dataset to be mixed.
-        transforms (Sequence[dict]): config dict to be composed.
+    Attributes:
+        dataset: The base dataset.
+        imgsz: The size of the images in the dataset.
     """
 
     def __init__(self, dataset):
+        """
+        Args:
+            dataset (BaseDataset): The base dataset to apply transformations to.
+        """
         self.dataset = dataset
         self.imgsz = dataset.imgsz
 
     def __len__(self):
+        """Returns the number of items in the dataset."""
         return len(self.dataset)
 
     def __getitem__(self, index):
+        """
+        Applies mosaic, mixup and rectangular training transformations to an item in the dataset.
+
+        Args:
+            index (int): Index of the item in the dataset.
+
+        Returns:
+            (dict): A dictionary containing the transformed item data.
+        """
         labels = deepcopy(self.dataset[index])
         for transform in self.dataset.transforms.tolist():
             # mosaic and mixup

ultralytics/yolo/data/utils.py

@@ -270,9 +270,8 @@ def check_cls_dataset(dataset: str):
     """
     Check a classification dataset such as Imagenet.
 
-    Copy code
     This function takes a `dataset` name as input and returns a dictionary containing information about the dataset.
-    If the dataset is not found, it attempts to download the dataset from the internet and save it to the local file system.
+    If the dataset is not found, it attempts to download the dataset from the internet and save it locally.
 
     Args:
         dataset (str): Name of the dataset.
@@ -306,7 +305,8 @@ def check_cls_dataset(dataset: str):
 
 
 class HUBDatasetStats():
-    """ Class for generating HUB dataset JSON and `-hub` dataset directory
+    """
+    Class for generating HUB dataset JSON and `-hub` dataset directory
 
     Arguments
         path:           Path to data.yaml or data.zip (with data.yaml inside data.zip)
@@ -427,9 +427,6 @@ def compress_one_image(f, f_new=None, max_dim=1920, quality=50):
         max_dim (int, optional): The maximum dimension (width or height) of the output image. Default is 1920 pixels.
         quality (int, optional): The image compression quality as a percentage. Default is 50%.
 
-    Returns:
-        None
-
     Usage:
         from pathlib import Path
         from ultralytics.yolo.data.utils import compress_one_image
@@ -459,9 +456,6 @@ def delete_dsstore(path):
     Args:
         path (str, optional): The directory path where the ".DS_store" files should be deleted.
 
-    Returns:
-        None
-
     Usage:
         from ultralytics.yolo.data.utils import delete_dsstore
         delete_dsstore('/Users/glennjocher/Downloads/dataset')
@@ -478,15 +472,13 @@ def delete_dsstore(path):
 
 
 def zip_directory(dir, use_zipfile_library=True):
-    """Zips a directory and saves the archive to the specified output path.
+    """
+    Zips a directory and saves the archive to the specified output path.
 
     Args:
         dir (str): The path to the directory to be zipped.
         use_zipfile_library (bool): Whether to use zipfile library or shutil for zipping.
 
-    Returns:
-        None
-
     Usage:
         from ultralytics.yolo.data.utils import zip_directory
         zip_directory('/Users/glennjocher/Downloads/playground')

ultralytics/yolo/engine/exporter.py

@@ -73,7 +73,7 @@ ARM64 = platform.machine() in ('arm64', 'aarch64')
 
 
 def export_formats():
-    # YOLOv8 export formats
+    """YOLOv8 export formats"""
     import pandas
     x = [
         ['PyTorch', '-', '.pt', True, True],
@@ -92,7 +92,7 @@ def export_formats():
 
 
 def gd_outputs(gd):
-    # TensorFlow GraphDef model output node names
+    """TensorFlow GraphDef model output node names"""
     name_list, input_list = [], []
     for node in gd.node:  # tensorflow.core.framework.node_def_pb2.NodeDef
         name_list.append(node.name)
@@ -101,7 +101,7 @@ def gd_outputs(gd):
 
 
 def try_export(inner_func):
-    # YOLOv8 export decorator, i..e @try_export
+    """YOLOv8 export decorator, i..e @try_export"""
     inner_args = get_default_args(inner_func)
 
     def outer_func(*args, **kwargs):
@@ -118,10 +118,26 @@ def try_export(inner_func):
     return outer_func
 
 
+class iOSDetectModel(torch.nn.Module):
+    """Wrap an Ultralytics YOLO model for iOS export"""
+
+    def __init__(self, model, im):
+        super().__init__()
+        b, c, h, w = im.shape  # batch, channel, height, width
+        self.model = model
+        self.nc = len(model.names)  # number of classes
+        if w == h:
+            self.normalize = 1.0 / w  # scalar
+        else:
+            self.normalize = torch.tensor([1.0 / w, 1.0 / h, 1.0 / w, 1.0 / h])  # broadcast (slower, smaller)
+
+    def forward(self, x):
+        xywh, cls = self.model(x)[0].transpose(0, 1).split((4, self.nc), 1)
+        return cls, xywh * self.normalize  # confidence (3780, 80), coordinates (3780, 4)
+
+
 class Exporter:
     """
-    Exporter
-
     A class for exporting a model.
 
     Attributes:
@@ -136,6 +152,7 @@ class Exporter:
         Args:
             cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CFG.
             overrides (dict, optional): Configuration overrides. Defaults to None.
+            _callbacks (list, optional): List of callback functions. Defaults to None.
         """
         self.args = get_cfg(cfg, overrides)
         self.callbacks = _callbacks or callbacks.get_default_callbacks()
@@ -385,22 +402,6 @@ class Exporter:
         check_requirements('coremltools>=6.0')
         import coremltools as ct  # noqa
 
-        class iOSDetectModel(torch.nn.Module):
-            # Wrap an Ultralytics YOLO model for iOS export
-            def __init__(self, model, im):
-                super().__init__()
-                b, c, h, w = im.shape  # batch, channel, height, width
-                self.model = model
-                self.nc = len(model.names)  # number of classes
-                if w == h:
-                    self.normalize = 1.0 / w  # scalar
-                else:
-                    self.normalize = torch.tensor([1.0 / w, 1.0 / h, 1.0 / w, 1.0 / h])  # broadcast (slower, smaller)
-
-            def forward(self, x):
-                xywh, cls = self.model(x)[0].transpose(0, 1).split((4, self.nc), 1)
-                return cls, xywh * self.normalize  # confidence (3780, 80), coordinates (3780, 4)
-
         LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
         f = self.file.with_suffix('.mlmodel')
 

ultralytics/yolo/engine/model.py

@@ -400,7 +400,7 @@ class YOLO:
             train_args (dict, optional): Additional arguments to pass to the `train()` method. Defaults to {}.
 
         Returns:
-            A dictionary containing the results of the hyperparameter search.
+            (dict): A dictionary containing the results of the hyperparameter search.
 
         Raises:
             ModuleNotFoundError: If Ray Tune is not installed.

ultralytics/yolo/engine/predictor.py

@@ -127,7 +127,10 @@ class BasePredictor:
         log_string += result.verbose()
 
         if self.args.save or self.args.show:  # Add bbox to image
-            plot_args = dict(line_width=self.args.line_thickness, boxes=self.args.boxes)
+            plot_args = dict(line_width=self.args.line_thickness,
+                             boxes=self.args.boxes,
+                             conf=self.args.show_conf,
+                             labels=self.args.show_labels)
             if not self.args.retina_masks:
                 plot_args['im_gpu'] = im[idx]
             self.plotted_img = result.plot(**plot_args)

ultralytics/yolo/engine/trainer.py

@@ -621,7 +621,7 @@ def check_amp(model):
         model (nn.Module): A YOLOv8 model instance.
 
     Returns:
-        bool: Returns True if the AMP functionality works correctly with YOLOv8 model, else False.
+        (bool): Returns True if the AMP functionality works correctly with YOLOv8 model, else False.
 
     Raises:
         AssertionError: If the AMP checks fail, indicating anomalies with the AMP functionality on the system.

ultralytics/yolo/utils/autobatch.py

@@ -22,7 +22,7 @@ def check_train_batch_size(model, imgsz=640, amp=True):
         amp (bool): If True, use automatic mixed precision (AMP) for training.
 
     Returns:
-        int: Optimal batch size computed using the autobatch() function.
+        (int): Optimal batch size computed using the autobatch() function.
     """
 
     with torch.cuda.amp.autocast(amp):
@@ -34,13 +34,13 @@ def autobatch(model, imgsz=640, fraction=0.67, batch_size=16):
     Automatically estimate the best YOLO batch size to use a fraction of the available CUDA memory.
 
     Args:
-        model: YOLO model to compute batch size for.
+        model (torch.nn.module): YOLO model to compute batch size for.
         imgsz (int, optional): The image size used as input for the YOLO model. Defaults to 640.
         fraction (float, optional): The fraction of available CUDA memory to use. Defaults to 0.67.
         batch_size (int, optional): The default batch size to use if an error is detected. Defaults to 16.
 
     Returns:
-        int: The optimal batch size.
+        (int): The optimal batch size.
     """
 
     # Check device

ultralytics/yolo/utils/callbacks/clearml.py

@@ -19,14 +19,14 @@ except (ImportError, AssertionError):
     clearml = None
 
 
-def _log_debug_samples(files, title='Debug Samples'):
+def _log_debug_samples(files, title='Debug Samples') -> None:
     """
-        Log files (images) as debug samples in the ClearML task.
+    Log files (images) as debug samples in the ClearML task.
 
-        arguments:
-        files (List(PosixPath)) a list of file paths in PosixPath format
-        title (str) A title that groups together images with the same values
-        """
+    Args:
+        files (list): A list of file paths in PosixPath format.
+        title (str): A title that groups together images with the same values.
+    """
     task = Task.current_task()
     if task:
         for f in files:
@@ -39,20 +39,23 @@ def _log_debug_samples(files, title='Debug Samples'):
                                                iteration=iteration)
 
 
-def _log_plot(title, plot_path):
+def _log_plot(title, plot_path) -> None:
     """
-        Log image as plot in the plot section of ClearML
+    Log an image as a plot in the plot section of ClearML.
 
-        arguments:
-        title (str) Title of the plot
-        plot_path (PosixPath or str) Path to the saved image file
-        """
+    Args:
+        title (str): The title of the plot.
+        plot_path (str): The path to the saved image file.
+    """
     img = mpimg.imread(plot_path)
     fig = plt.figure()
     ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect='auto', xticks=[], yticks=[])  # no ticks
     ax.imshow(img)
 
-    Task.current_task().get_logger().report_matplotlib_figure(title, '', figure=fig, report_interactive=False)
+    Task.current_task().get_logger().report_matplotlib_figure(title=title,
+                                                              series='',
+                                                              figure=fig,
+                                                              report_interactive=False)
 
 
 def on_pretrain_routine_start(trainer):

ultralytics/yolo/utils/checks.py

@@ -47,13 +47,13 @@ def check_imgsz(imgsz, stride=32, min_dim=1, max_dim=2, floor=0):
     stride, update it to the nearest multiple of the stride that is greater than or equal to the given floor value.
 
     Args:
-        imgsz (int or List[int]): Image size.
+        imgsz (int) or (cList[int]): Image size.
         stride (int): Stride value.
         min_dim (int): Minimum number of dimensions.
         floor (int): Minimum allowed value for image size.
 
     Returns:
-        List[int]: Updated image size.
+        (List[int]): Updated image size.
     """
     # Convert stride to integer if it is a tensor
     stride = int(stride.max() if isinstance(stride, torch.Tensor) else stride)
@@ -106,7 +106,7 @@ def check_version(current: str = '0.0.0',
         verbose (bool): If True, print warning message if minimum version is not met.
 
     Returns:
-        bool: True if minimum version is met, False otherwise.
+        (bool): True if minimum version is met, False otherwise.
     """
     current, minimum = (pkg.parse_version(x) for x in (current, minimum))
     result = (current == minimum) if pinned else (current >= minimum)  # bool
@@ -126,7 +126,7 @@ def check_latest_pypi_version(package_name='ultralytics'):
         package_name (str): The name of the package to find the latest version for.
 
     Returns:
-        str: The latest version of the package.
+        (str): The latest version of the package.
     """
     requests.packages.urllib3.disable_warnings()  # Disable the InsecureRequestWarning
     response = requests.get(f'https://pypi.org/pypi/{package_name}/json', verify=False)
@@ -140,7 +140,7 @@ def check_pip_update_available():
     Checks if a new version of the ultralytics package is available on PyPI.
 
     Returns:
-        bool: True if an update is available, False otherwise.
+        (bool): True if an update is available, False otherwise.
     """
     if ONLINE and is_pip_package():
         with contextlib.suppress(Exception):
@@ -206,9 +206,6 @@ def check_requirements(requirements=ROOT.parent / 'requirements.txt', exclude=()
         exclude (Tuple[str]): Tuple of package names to exclude from checking.
         install (bool): If True, attempt to auto-update packages that don't meet requirements.
         cmds (str): Additional commands to pass to the pip install command when auto-updating.
-
-    Returns:
-        None
     """
     prefix = colorstr('red', 'bold', 'requirements:')
     check_python()  # check python version

ultralytics/yolo/utils/downloads.py

@@ -67,21 +67,21 @@ def safe_download(url,
                   min_bytes=1E0,
                   progress=True):
     """
-    Function for downloading files from a URL, with options for retrying, unzipping, and deleting the downloaded file.
+    Downloads files from a URL, with options for retrying, unzipping, and deleting the downloaded file.
 
     Args:
-        url: str: The URL of the file to be downloaded.
-        file: str, optional: The filename of the downloaded file.
+        url (str): The URL of the file to be downloaded.
+        file (str, optional): The filename of the downloaded file.
             If not provided, the file will be saved with the same name as the URL.
-        dir: str, optional: The directory to save the downloaded file.
+        dir (str, optional): The directory to save the downloaded file.
             If not provided, the file will be saved in the current working directory.
-        unzip: bool, optional: Whether to unzip the downloaded file. Default: True.
-        delete: bool, optional: Whether to delete the downloaded file after unzipping. Default: False.
-        curl: bool, optional: Whether to use curl command line tool for downloading. Default: False.
-        retry: int, optional: The number of times to retry the download in case of failure. Default: 3.
-        min_bytes: float, optional: The minimum number of bytes that the downloaded file should have, to be considered
+        unzip (bool, optional): Whether to unzip the downloaded file. Default: True.
+        delete (bool, optional): Whether to delete the downloaded file after unzipping. Default: False.
+        curl (bool, optional): Whether to use curl command line tool for downloading. Default: False.
+        retry (int, optional): The number of times to retry the download in case of failure. Default: 3.
+        min_bytes (float, optional): The minimum number of bytes that the downloaded file should have, to be considered
             a successful download. Default: 1E0.
-        progress: bool, optional: Whether to display a progress bar during the download. Default: True.
+        progress (bool, optional): Whether to display a progress bar during the download. Default: True.
     """
     if '://' not in str(url) and Path(url).is_file():  # exists ('://' check required in Windows Python<3.10)
         f = Path(url)  # filename

ultralytics/yolo/utils/files.py

@@ -30,13 +30,13 @@ def increment_path(path, exist_ok=False, sep='', mkdir=False):
     directory if it does not already exist.
 
     Args:
-    path (str or pathlib.Path): Path to increment.
-    exist_ok (bool, optional): If True, the path will not be incremented and will be returned as-is. Defaults to False.
-    sep (str, optional): Separator to use between the path and the incrementation number. Defaults to an empty string.
-    mkdir (bool, optional): If True, the path will be created as a directory if it does not exist. Defaults to False.
+        path (str, pathlib.Path): Path to increment.
+        exist_ok (bool, optional): If True, the path will not be incremented and returned as-is. Defaults to False.
+        sep (str, optional): Separator to use between the path and the incrementation number. Defaults to ''.
+        mkdir (bool, optional): Create a directory if it does not exist. Defaults to False.
 
     Returns:
-    pathlib.Path: Incremented path.
+        (pathlib.Path): Incremented path.
     """
     path = Path(path)  # os-agnostic
     if path.exists() and not exist_ok:

ultralytics/yolo/utils/instance.py

@@ -98,7 +98,7 @@ class Bboxes:
     def mul(self, scale):
         """
         Args:
-            scale (tuple | List | int): the scale for four coords.
+            scale (tuple) or (list) or (int): the scale for four coords.
         """
         if isinstance(scale, Number):
             scale = to_4tuple(scale)
@@ -112,7 +112,7 @@ class Bboxes:
     def add(self, offset):
         """
         Args:
-            offset (tuple | List | int): the offset for four coords.
+            offset (tuple) or (list) or (int): the offset for four coords.
         """
         if isinstance(offset, Number):
             offset = to_4tuple(offset)
@@ -129,13 +129,18 @@ class Bboxes:
     @classmethod
     def concatenate(cls, boxes_list: List['Bboxes'], axis=0) -> 'Bboxes':
         """
-        Concatenates a list of Boxes into a single Bboxes
+        Concatenate a list of Bboxes objects into a single Bboxes object.
 
-        Arguments:
-            boxes_list (list[Bboxes])
+        Args:
+            boxes_list (List[Bboxes]): A list of Bboxes objects to concatenate.
+            axis (int, optional): The axis along which to concatenate the bounding boxes.
+                                   Defaults to 0.
 
         Returns:
-            Bboxes: the concatenated Boxes
+            Bboxes: A new Bboxes object containing the concatenated bounding boxes.
+
+        Note:
+            The input should be a list or tuple of Bboxes objects.
         """
         assert isinstance(boxes_list, (list, tuple))
         if not boxes_list:
@@ -148,11 +153,21 @@ class Bboxes:
 
     def __getitem__(self, index) -> 'Bboxes':
         """
+        Retrieve a specific bounding box or a set of bounding boxes using indexing.
+
         Args:
-            index: int, slice, or a BoolArray
+            index (int, slice, or np.ndarray): The index, slice, or boolean array to select
+                                               the desired bounding boxes.
 
         Returns:
-            Bboxes: Create a new :class:`Bboxes` by indexing.
+            Bboxes: A new Bboxes object containing the selected bounding boxes.
+
+        Raises:
+            AssertionError: If the indexed bounding boxes do not form a 2-dimensional matrix.
+
+        Note:
+            When using boolean indexing, make sure to provide a boolean array with the same
+            length as the number of bounding boxes.
         """
         if isinstance(index, int):
             return Bboxes(self.bboxes[index].view(1, -1))
@@ -236,11 +251,19 @@ class Instances:
 
     def __getitem__(self, index) -> 'Instances':
         """
+        Retrieve a specific instance or a set of instances using indexing.
+
         Args:
-            index: int, slice, or a BoolArray
+            index (int, slice, or np.ndarray): The index, slice, or boolean array to select
+                                               the desired instances.
 
         Returns:
-            Instances: Create a new :class:`Instances` by indexing.
+            Instances: A new Instances object containing the selected bounding boxes,
+                       segments, and keypoints if present.
+
+        Note:
+            When using boolean indexing, make sure to provide a boolean array with the same
+            length as the number of instances.
         """
         segments = self.segments[index] if len(self.segments) else self.segments
         keypoints = self.keypoints[index] if self.keypoints is not None else None
@@ -305,14 +328,20 @@ class Instances:
     @classmethod
     def concatenate(cls, instances_list: List['Instances'], axis=0) -> 'Instances':
         """
-        Concatenates a list of Boxes into a single Bboxes
+        Concatenates a list of Instances objects into a single Instances object.
 
-        Arguments:
-            instances_list (list[Bboxes])
-            axis
+        Args:
+            instances_list (List[Instances]): A list of Instances objects to concatenate.
+            axis (int, optional): The axis along which the arrays will be concatenated. Defaults to 0.
 
         Returns:
-            Boxes: the concatenated Boxes
+            Instances: A new Instances object containing the concatenated bounding boxes,
+                       segments, and keypoints if present.
+
+        Note:
+            The `Instances` objects in the list should have the same properties, such as
+            the format of the bounding boxes, whether keypoints are present, and if the
+            coordinates are normalized.
         """
         assert isinstance(instances_list, (list, tuple))
         if not instances_list: