diff --git a/.github/workflows/links.yml b/.github/workflows/links.yml
index 39471cf6..bb7bd983 100644
--- a/.github/workflows/links.yml
+++ b/.github/workflows/links.yml
@@ -34,7 +34,9 @@ jobs:
retry_wait_seconds: 60
max_attempts: 3
command: |
- lychee --accept 403,429,500,502,999 \
+ lychee \
+ --scheme https \
+ --accept 403,429,500,502,999 \
--exclude-loopback \
--exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' \
--exclude-path docs/zh \
@@ -48,7 +50,8 @@ jobs:
--exclude-path docs/hi \
--exclude-path docs/ar \
--github-token ${{ secrets.GITHUB_TOKEN }} \
- './**/*.md' './**/*.html'
+ './**/*.md' \
+ './**/*.html'
- name: Test Markdown, HTML, YAML, Python and Notebook links with retry
if: github.event_name == 'workflow_dispatch'
@@ -58,7 +61,9 @@ jobs:
retry_wait_seconds: 60
max_attempts: 3
command: |
- lychee --accept 429,999 \
+ lychee \
+ --scheme https \
+ --accept 429,999 \
--exclude-loopback \
--exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' \
--exclude-path '**/ci.yaml' \
@@ -73,4 +78,9 @@ jobs:
--exclude-path docs/hi \
--exclude-path docs/ar \
--github-token ${{ secrets.GITHUB_TOKEN }} \
- './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb'
+ './**/*.md' \
+ './**/*.html' \
+ './**/*.yml' \
+ './**/*.yaml' \
+ './**/*.py' \
+ './**/*.ipynb'
diff --git a/README.md b/README.md
index e20897ee..798f2489 100644
--- a/README.md
+++ b/README.md
@@ -102,14 +102,14 @@ See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more exa
Ultralytics provides interactive notebooks for YOLOv8, covering training, validation, tracking, and more. Each notebook is paired with a [YouTube](https://youtube.com/ultralytics) tutorial, making it easy to learn and implement advanced YOLOv8 features.
-| Docs | Notebook | YouTube |
-| --------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| YOLOv8 Train, Val, Predict and Export Modes | 
| 
|
-| Ultralytics HUB QuickStart | | |
-| YOLOv8 Multi-Object Tracking in Videos | | |
-| YOLOv8 Object Counting in Videos | | |
-| YOLOv8 Heatmaps in Videos | | |
-| Ultralytics Datasets Explorer with SQL and OpenAI Integration | | Coming Soon |
+| Docs | Notebook | YouTube |
+| --------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| YOLOv8 Train, Val, Predict and Export Modes | | |
+| Ultralytics HUB QuickStart | | |
+| YOLOv8 Multi-Object Tracking in Videos | | |
+| YOLOv8 Object Counting in Videos | | |
+| YOLOv8 Heatmaps in Videos | | |
+| Ultralytics Datasets Explorer with SQL and OpenAI Integration 🚀 New | | Coming Soon |
## Models
diff --git a/README.zh-CN.md b/README.zh-CN.md
index 36a65517..3fb95ba8 100644
--- a/README.zh-CN.md
+++ b/README.zh-CN.md
@@ -104,14 +104,14 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
Ultralytics 提供了 YOLOv8 的交互式笔记本，涵盖训练、验证、跟踪等内容。每个笔记本都配有 [YouTube](https://youtube.com/ultralytics) 教程，使学习和实现高级 YOLOv8 功能变得简单。
-| 文档 | 笔记本 | YouTube |
-| ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| YOLOv8 训练、验证、预测和导出模式 | | |
-| Ultralytics HUB 快速开始 | | |
-| YOLOv8 视频中的多对象跟踪 | | |
-| YOLOv8 视频中的对象计数 | | |
-| YOLOv8 视频中的热图 | | |
-| Ultralytics 数据集浏览器，集成 SQL 和 OpenAI | | 即将推出 |
+| 文档 | 笔记本 | YouTube |
+| ------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| YOLOv8 训练、验证、预测和导出模式 | | |
+| Ultralytics HUB 快速开始 | | |
+| YOLOv8 视频中的多对象跟踪 | | |
+| YOLOv8 视频中的对象计数 | | |
+| YOLOv8 视频中的热图 | | |
+| Ultralytics 数据集浏览器，集成 SQL 和 OpenAI 🚀 New | | 即将推出 |
## 模型
diff --git a/docs/en/tasks/obb.md b/docs/en/tasks/obb.md
index 17329ec3..6d56a255 100644
--- a/docs/en/tasks/obb.md
+++ b/docs/en/tasks/obb.md
@@ -42,7 +42,7 @@ YOLOv8 pretrained OBB models are shown here, which are pretrained on the [DOTAv1
## Train
-Train YOLOv8n-obb on the dota8.yaml dataset for 100 epochs at image size 640. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
+Train YOLOv8n-obb on the `dota8.yaml` dataset for 100 epochs at image size 640. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
!!! Example
@@ -57,19 +57,19 @@ Train YOLOv8n-obb on the dota8.yaml dataset for 100 epochs at image size 640. Fo
model = YOLO('yolov8n-obb.yaml').load('yolov8n.pt') # build from YAML and transfer weights
# Train the model
- results = model.train(data='dota8-obb.yaml', epochs=100, imgsz=640)
+ results = model.train(data='dota8.yaml', epochs=100, imgsz=640)
```
=== "CLI"
```bash
# Build a new model from YAML and start training from scratch
- yolo obb train data=dota8-obb.yaml model=yolov8n-obb.yaml epochs=100 imgsz=640
+ yolo obb train data=dota8.yaml model=yolov8n-obb.yaml epochs=100 imgsz=640
# Start training from a pretrained *.pt model
- yolo obb train data=dota8-obb.yaml model=yolov8n-obb.pt epochs=100 imgsz=640
+ yolo obb train data=dota8.yaml model=yolov8n-obb.pt epochs=100 imgsz=640
# Build a new model from YAML, transfer pretrained weights to it and start training
- yolo obb train data=dota8-obb.yaml model=yolov8n-obb.yaml pretrained=yolov8n-obb.pt epochs=100 imgsz=640
+ yolo obb train data=dota8.yaml model=yolov8n-obb.yaml pretrained=yolov8n-obb.pt epochs=100 imgsz=640
```
### Dataset format
@@ -78,7 +78,7 @@ OBB dataset format can be found in detail in the [Dataset Guide](../datasets/obb
## Val
-Validate trained YOLOv8n-obb model accuracy on the dota8-obb dataset. No argument need to passed as the `model`
+Validate trained YOLOv8n-obb model accuracy on the DOTA8 dataset. No argument need to passed as the `model`
retains it's training `data` and arguments as model attributes.
!!! Example
@@ -93,7 +93,7 @@ retains it's training `data` and arguments as model attributes.
model = YOLO('path/to/best.pt') # load a custom model
# Validate the model
- metrics = model.val() # no arguments needed, dataset and settings remembered
+ metrics = model.val(data='dota8.yaml') # no arguments needed, dataset and settings remembered
metrics.box.map # map50-95(B)
metrics.box.map50 # map50(B)
metrics.box.map75 # map75(B)
@@ -102,8 +102,8 @@ retains it's training `data` and arguments as model attributes.
=== "CLI"
```bash
- yolo obb val model=yolov8n-obb.pt # val official model
- yolo obb val model=path/to/best.pt # val custom model
+ yolo obb val model=yolov8n-obb.pt data=dota8.yaml # val official model
+ yolo obb val model=path/to/best.pt data=path/to/data.yaml # val custom model
```
## Predict
diff --git a/examples/heatmaps.ipynb b/examples/heatmaps.ipynb
index b09a8e69..0cc520c5 100644
--- a/examples/heatmaps.ipynb
+++ b/examples/heatmaps.ipynb
@@ -83,20 +83,19 @@
"model = YOLO(\"yolov8n.pt\")\n",
"cap = cv2.VideoCapture(\"path/to/video/file.mp4\")\n",
"assert cap.isOpened(), \"Error reading video file\"\n",
- "\n",
"w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))\n",
"\n",
"# Video writer\n",
- "video_writer = cv2.VideoWriter(\"/content/people walking gray.mp4\",\n",
+ "video_writer = cv2.VideoWriter(\"heatmap_output.avi\",\n",
" cv2.VideoWriter_fourcc(*'mp4v'),\n",
" fps,\n",
- " (w, h))\n",
+ " (w, h))\n",
"\n",
"# Init heatmap\n",
"heatmap_obj = heatmap.Heatmap()\n",
- "heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA ,\n",
- " imw=cap.get(4), # should same as cap height\n",
- " imh=cap.get(3), # should same as cap width\n",
+ "heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA,\n",
+ " imw=w,\n",
+ " imh=h,\n",
" view_img=True,\n",
" shape=\"circle\")\n",
"\n",
@@ -143,4 +142,4 @@
}
}
]
-}
+}
\ No newline at end of file
diff --git a/examples/object_counting.ipynb b/examples/object_counting.ipynb
index 27744263..ce11044d 100644
--- a/examples/object_counting.ipynb
+++ b/examples/object_counting.ipynb
@@ -83,11 +83,10 @@
"model = YOLO(\"yolov8n.pt\")\n",
"cap = cv2.VideoCapture(\"path/to/video/file.mp4\")\n",
"assert cap.isOpened(), \"Error reading video file\"\n",
- "\n",
"w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))\n",
"\n",
- "# Define line points\n",
- "line_points = [(20, 400), (1080, 400)]\n",
+ "# Define region points\n",
+ "region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]\n",
"\n",
"# Video writer\n",
"video_writer = cv2.VideoWriter(\"object_counting_output.avi\",\n",
@@ -98,7 +97,7 @@
"# Init Object Counter\n",
"counter = object_counter.ObjectCounter()\n",
"counter.set_args(view_img=True,\n",
- " reg_pts=line_points,\n",
+ " reg_pts=region_points,\n",
" classes_names=model.names,\n",
" draw_tracks=True)\n",
"\n",
@@ -145,4 +144,4 @@
}
}
]
-}
+}
\ No newline at end of file
diff --git a/ultralytics/cfg/__init__.py b/ultralytics/cfg/__init__.py
index 7d9aca7e..313d6518 100644
--- a/ultralytics/cfg/__init__.py
+++ b/ultralytics/cfg/__init__.py
@@ -37,7 +37,7 @@ TASK2DATA = {
"segment": "coco8-seg.yaml",
"classify": "imagenet10",
"pose": "coco8-pose.yaml",
- "obb": "dota8-obb.yaml",
+ "obb": "dota8.yaml",
}
TASK2MODEL = {
"detect": "yolov8n.pt",
diff --git a/ultralytics/utils/callbacks/tensorboard.py b/ultralytics/utils/callbacks/tensorboard.py
index 0a39d094..b78412f7 100644
--- a/ultralytics/utils/callbacks/tensorboard.py
+++ b/ultralytics/utils/callbacks/tensorboard.py
@@ -10,8 +10,9 @@ try:
assert SETTINGS["tensorboard"] is True # verify integration is enabled
WRITER = None # TensorBoard SummaryWriter instance
-except (ImportError, AssertionError, TypeError):
+except (ImportError, AssertionError, TypeError, AttributeError):
# TypeError for handling 'Descriptors cannot not be created directly.' protobuf errors in Windows
+ # AttributeError: module 'tensorflow' has no attribute 'io' if 'tensorflow' not installed
SummaryWriter = None