Merge branch 'main' into export-batch-size

.github/workflows/cla.yml

@@ -30,7 +30,7 @@ jobs:
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           # Must be repository secret PAT
-          PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
+          PERSONAL_ACCESS_TOKEN: ${{ secrets._GITHUB_TOKEN }}
         with:
           path-to-signatures: "signatures/version1/cla.json"
           path-to-document: "https://docs.ultralytics.com/help/CLA" # CLA document

.github/workflows/docker.yaml

@@ -182,7 +182,7 @@ jobs:
     steps:
       - name: Trigger Additional GitHub Actions
         env:
-          GH_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
+          GH_TOKEN: ${{ secrets._GITHUB_TOKEN }}
         run: |
           sleep 60
           gh workflow run deploy_cloud_run.yml \

.github/workflows/docs.yml

@@ -34,7 +34,7 @@ jobs:
         uses: actions/checkout@v4
         with:
           repository: ${{ github.event.pull_request.head.repo.full_name || github.repository }}
-          token: ${{ secrets.PERSONAL_ACCESS_TOKEN || secrets.GITHUB_TOKEN }}
+          token: ${{ secrets._GITHUB_TOKEN }}
           ref: ${{ github.head_ref || github.ref }}
           fetch-depth: 0
       - name: Set up Python
@@ -94,5 +94,5 @@ jobs:
           else
             LATEST_HASH=$(git rev-parse --short=7 HEAD)
             git commit -m "Update Docs for 'ultralytics ${{ steps.check_pypi.outputs.version }} - $LATEST_HASH'"
-            git push https://${{ secrets.PERSONAL_ACCESS_TOKEN }}@github.com/ultralytics/docs.git gh-pages
+            git push https://${{ secrets._GITHUB_TOKEN }}@github.com/ultralytics/docs.git gh-pages
           fi

.github/workflows/format.yml

@@ -20,15 +20,14 @@ jobs:
       - name: Run Ultralytics Formatting
         uses: ultralytics/actions@main
         with:
-          token: ${{ secrets.PERSONAL_ACCESS_TOKEN || secrets.GITHUB_TOKEN }} # note GITHUB_TOKEN automatically generated
+          token: ${{ secrets._GITHUB_TOKEN }} # note GITHUB_TOKEN automatically generated
           labels: true # autolabel issues and PRs
           python: true # format Python code and docstrings
           prettier: true # format YAML, JSON, Markdown and CSS
           spelling: true # check spelling
           links: false # check broken links
           summary: true # print PR summary with GPT4o (requires 'openai_api_key')
-          openai_azure_api_key: ${{ secrets.OPENAI_AZURE_API_KEY }}
-          openai_azure_endpoint: ${{ secrets.OPENAI_AZURE_ENDPOINT }}
+          openai_api_key: ${{ secrets.OPENAI_API_KEY }}
           first_issue_response: |
             👋 Hello @${{ github.actor }}, thank you for your interest in Ultralytics 🚀! We recommend a visit to the [Docs](https://docs.ultralytics.com) for new users where you can find many [Python](https://docs.ultralytics.com/usage/python/) and [CLI](https://docs.ultralytics.com/usage/cli/) usage examples and where many of the most common questions may already be answered.
 

.github/workflows/merge-main-into-prs.yml

@@ -33,7 +33,7 @@ jobs:
           import os
           import time
 
-          g = Github("${{ secrets.PERSONAL_ACCESS_TOKEN }}")
+          g = Github("${{ secrets._GITHUB_TOKEN }}")
           repo = g.get_repo("${{ github.repository }}")
 
           # Fetch the default branch name

.github/workflows/publish.yml

@@ -23,7 +23,7 @@ jobs:
       - name: Checkout code
         uses: actions/checkout@v4
         with:
-          token: ${{ secrets.PERSONAL_ACCESS_TOKEN || secrets.GITHUB_TOKEN }} # use your PAT here
+          token: ${{ secrets._GITHUB_TOKEN }} # use your PAT here
       - name: Git config
         run: |
           git config --global user.name "UltralyticsAssistant"
@@ -103,7 +103,7 @@ jobs:
         if: (github.event_name == 'push' || github.event.inputs.pypi == 'true')  && steps.check_pypi.outputs.increment == 'True'
         env:
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          GITHUB_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN || secrets.GITHUB_TOKEN }}
+          GITHUB_TOKEN: ${{ secrets._GITHUB_TOKEN }}
           CURRENT_TAG: ${{ steps.check_pypi.outputs.current_tag }}
           PREVIOUS_TAG: ${{ steps.check_pypi.outputs.previous_tag }}
         run: |
@@ -111,7 +111,7 @@ jobs:
         shell: bash
       - name: Extract PR Details
         env:
-          GH_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN || secrets.GITHUB_TOKEN }}
+          GH_TOKEN: ${{ secrets._GITHUB_TOKEN }}
         run: |
           # Check if the event is a pull request or pull_request_target
           if [ "${{ github.event_name }}" = "pull_request" ] || [ "${{ github.event_name }}" = "pull_request_target" ]; then

docs/en/guides/model-deployment-practices.md

@@ -106,7 +106,7 @@ When deploying [machine learning](https://www.ultralytics.com/glossary/machine-l
 - **Profile the Inference Pipeline:** Identifying bottlenecks in the inference pipeline can help pinpoint the source of delays. Use profiling tools to analyze each step of the inference process, identifying and addressing any stages that cause significant delays, such as inefficient layers or data transfer issues.
 - **Use Appropriate Precision:** Using higher precision than necessary can slow down inference times. Experiment with using lower precision, such as FP16 (half-precision), instead of FP32 (full-precision). While FP16 can reduce inference time, also keep in mind that it can impact model accuracy.
 
-If you are facing this issue while deploying YOLO11, consider that YOLO11 offers [various model sizes](../models/yolov8.md), such as YOLO11n (nano) for devices with lower memory capacity and YOLOv8x (extra-large) for more powerful GPUs. Choosing the right model variant for your hardware can help balance memory usage and processing time.
+If you are facing this issue while deploying YOLO11, consider that YOLO11 offers [various model sizes](../models/yolo11.md), such as YOLO11n (nano) for devices with lower memory capacity and YOLO11x (extra-large) for more powerful GPUs. Choosing the right model variant for your hardware can help balance memory usage and processing time.
 
 Also keep in mind that the size of the input images directly impacts memory usage and processing time. Lower resolutions reduce memory usage and speed up inference, while higher resolutions improve accuracy but require more memory and processing power.
 

docs/en/integrations/weights-biases.md

@@ -210,7 +210,7 @@ These features help in tracking experiments, optimizing models, and collaboratin
 After running your training script with W&B integration:
 
 1. A link to your W&B dashboard will be provided in the console output.
-2. Click on the link or go to [wandb.ai](https://wandb.ai) and log in to your account.
+2. Click on the link or go to [wandb.ai](https://wandb.ai/) and log in to your account.
 3. Navigate to your project to view detailed metrics, visualizations, and model performance data.
 
 The dashboard offers insights into your model's training process, allowing you to analyze and improve your YOLO11 models effectively.

examples/tutorial.ipynb

@@ -582,7 +582,7 @@
         "from ultralytics import YOLO\n",
         "\n",
         "model = YOLO('yolo11n-obb.pt')  # load a pretrained YOLO OBB model\n",
-        "model.train(data='coco8-dota.yaml', epochs=3)  # train the model\n",
+        "model.train(data='dota8.yaml', epochs=3)  # train the model\n",
         "model('https://ultralytics.com/images/bus.jpg')  # predict on an image"
       ],
       "metadata": {

ultralytics/cfg/__init__.py

@@ -639,7 +639,7 @@ def smart_value(v):
     else:
         try:
             return eval(v)
-        except:  # noqa E722
+        except Exception:
             return v
 
 

ultralytics/data/utils.py

@@ -65,7 +65,7 @@ def exif_size(img: Image.Image):
                 rotation = exif.get(274, None)  # the EXIF key for the orientation tag is 274
                 if rotation in {6, 8}:  # rotation 270 or 90
                     s = s[1], s[0]
-        except:  # noqa E722
+        except Exception:
             pass
     return s
 

ultralytics/nn/autobackend.py

@@ -46,7 +46,7 @@ def default_class_names(data=None):
     if data:
         try:
             return yaml_load(check_yaml(data))["names"]
-        except:  # noqa E722
+        except Exception:
             pass
     return {i: f"class{i}" for i in range(999)}  # return default if above errors
 

ultralytics/nn/tasks.py

@@ -963,7 +963,6 @@ def parse_model(d, ch, verbose=True):  # model_dict, input_channels(3)
                     args[j] = locals()[a] if a in locals() else ast.literal_eval(a)
                 except ValueError:
                     pass
-
         n = n_ = max(round(n * depth), 1) if n > 1 else n  # depth gain
         if m in {
             Classify,
@@ -1102,7 +1101,7 @@ def guess_model_scale(model_path):
         (str): The size character of the model's scale, which can be n, s, m, l, or x.
     """
     try:
-        return re.search(r"yolo[v]?\d+([nslmx])", Path(model_path).stem).group(1)  # n, s, m, l, or x
+        return re.search(r"yolo[v]?\d+([nslmx])", Path(model_path).stem).group(1)  # noqa, returns n, s, m, l, or x
     except AttributeError:
         return ""
 
@@ -1139,7 +1138,7 @@ def guess_model_task(model):
     if isinstance(model, dict):
         try:
             return cfg2task(model)
-        except:  # noqa E722
+        except Exception:
             pass
 
     # Guess from PyTorch model
@@ -1147,12 +1146,12 @@ def guess_model_task(model):
         for x in "model.args", "model.model.args", "model.model.model.args":
             try:
                 return eval(x)["task"]
-            except:  # noqa E722
+            except Exception:
                 pass
         for x in "model.yaml", "model.model.yaml", "model.model.model.yaml":
             try:
                 return cfg2task(eval(x))
-            except:  # noqa E722
+            except Exception:
                 pass
 
         for m in model.modules():

ultralytics/solutions/analytics.py

@@ -61,11 +61,11 @@ class Analytics(BaseSolution):
         self.extract_tracks(im0)  # Extract tracks
 
         if self.type == "line":
-            for box in self.boxes:
+            for _ in self.boxes:
                 self.total_counts += 1
             im0 = self.update_graph(frame_number=frame_number)
             self.total_counts = 0
-        elif self.type == "pie" or self.type == "bar" or self.type == "area":
+        elif self.type in {"pie", "bar", "area"}:
             self.clswise_count = {}
             for box, cls in zip(self.boxes, self.clss):
                 if self.names[int(cls)] in self.clswise_count:

ultralytics/solutions/heatmap.py

@@ -52,7 +52,8 @@ class Heatmap(ObjectCounter):
         Returns:
             im0 (ndarray): Processed image for further usage
         """
-        self.heatmap = np.zeros_like(im0, dtype=np.float32) * 0.99 if not self.initialized else self.heatmap
+        if not self.initialized:
+            self.heatmap = np.zeros_like(im0, dtype=np.float32) * 0.99
         self.initialized = True  # Initialize heatmap only once
 
         self.annotator = Annotator(im0, line_width=self.line_width)  # Initialize annotator

ultralytics/utils/__init__.py

@@ -526,7 +526,7 @@ def read_device_model() -> str:
     try:
         with open("/proc/device-tree/model") as f:
             return f.read()
-    except:  # noqa E722
+    except Exception:
         return ""
 
 
@@ -584,7 +584,7 @@ def is_docker() -> bool:
     try:
         with open("/proc/self/cgroup") as f:
             return "docker" in f.read()
-    except:  # noqa E722
+    except Exception:
         return False
 
 
@@ -623,7 +623,7 @@ def is_online() -> bool:
         for dns in ("1.1.1.1", "8.8.8.8"):  # check Cloudflare and Google DNS
             socket.create_connection(address=(dns, 80), timeout=2.0).close()
             return True
-    except:  # noqa E722
+    except Exception:
         return False
 
 

ultralytics/utils/callbacks/tensorboard.py

@@ -50,7 +50,7 @@ def _log_tensorboard_graph(trainer):
             LOGGER.info(f"{PREFIX}model graph visualization added ✅")
             return
 
-        except:  # noqa E722
+        except Exception:
             # Fallback to TorchScript export steps (RTDETR)
             try:
                 model = deepcopy(de_parallel(trainer.model))

ultralytics/utils/checks.py

@@ -277,7 +277,7 @@ def check_latest_pypi_version(package_name="ultralytics"):
         response = requests.get(f"https://pypi.org/pypi/{package_name}/json", timeout=3)
         if response.status_code == 200:
             return response.json()["info"]["version"]
-    except:  # noqa E722
+    except Exception:
         return None
 
 
@@ -299,7 +299,7 @@ def check_pip_update_available():
                     f"Update with 'pip install -U ultralytics'"
                 )
                 return True
-        except:  # noqa E722
+        except Exception:
             pass
     return False
 
@@ -715,7 +715,7 @@ def git_describe(path=ROOT):  # path must be a directory
     """Return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe."""
     try:
         return subprocess.check_output(f"git -C {path} describe --tags --long --always", shell=True).decode()[:-1]
-    except:  # noqa E722
+    except Exception:
         return ""
 
 

ultralytics/utils/downloads.py

@@ -60,7 +60,7 @@ def is_url(url, check=False):
             with request.urlopen(url) as response:
                 return response.getcode() == 200  # check if exists online
         return True
-    except:  # noqa E722
+    except Exception:
         return False
 
 

ultralytics/utils/plotting.py

@@ -1117,7 +1117,7 @@ def plot_images(
                             im[y : y + h, x : x + w, :][mask] = (
                                 im[y : y + h, x : x + w, :][mask] * 0.4 + np.array(color) * 0.6
                             )
-                        except:  # noqa E722
+                        except Exception:
                             pass
                 annotator.fromarray(im)
     if not save:

ultralytics/utils/torch_utils.py

@@ -119,7 +119,7 @@ def get_cpu_info():
             info = cpuinfo.get_cpu_info()  # info dict
             string = info.get(k[0] if k[0] in info else k[1] if k[1] in info else k[2], "unknown")
             PERSISTENT_CACHE["cpu_info"] = string.replace("(R)", "").replace("CPU ", "").replace("@ ", "")
-        except:  # noqa E722
+        except Exception:
             pass
     return PERSISTENT_CACHE.get("cpu_info", "unknown")