From be61a64c47b9324ed2a0c0c1af007c06966da915 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Fri, 21 Apr 2023 23:47:29 +0200 Subject: [PATCH] Update links to https://docs.ultralytics.com (#11412) * Update links * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- README.md | 8 ++++---- README.zh-CN.md | 8 ++++---- classify/tutorial.ipynb | 10 +++++----- hubconf.py | 2 +- segment/train.py | 8 +++++--- segment/tutorial.ipynb | 10 +++++----- train.py | 8 +++++--- tutorial.ipynb | 10 +++++----- utils/dataloaders.py | 2 +- utils/loggers/__init__.py | 2 +- 10 files changed, 36 insertions(+), 32 deletions(-) diff --git a/README.md b/README.md index 7a43245a1f45..7bc7a6ce8ca4 100644 --- a/README.md +++ b/README.md @@ -85,7 +85,7 @@ pip install -r requirements.txt # install
Inference -YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest +YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). ```python @@ -134,7 +134,7 @@ The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5 results. [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are -1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://github.com/ultralytics/yolov5/issues/475) times faster). Use the +1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) times faster). Use the largest `--batch-size` possible, or pass `--batch-size -1` for YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB. @@ -247,7 +247,7 @@ YOLOv5 has been designed to be super easy to get started and simple to learn. We - All checkpoints are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml). - **mAPval** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` - **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.
Reproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1` -- **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.
Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment` +- **TTA** [Test Time Augmentation](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation) includes reflection and scale augmentations.
Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
@@ -484,4 +484,4 @@ For YOLOv5 bug reports and feature requests please visit [GitHub Issues](https:/ -[tta]: https://github.com/ultralytics/yolov5/issues/303 +[tta]: https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation diff --git a/README.zh-CN.md b/README.zh-CN.md index 6396ecffbe79..c7733488bd02 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -80,7 +80,7 @@ pip install -r requirements.txt # install
推理 -使用 YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 推理。最新 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 将自动的从 +使用 YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) 推理。最新 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 将自动的从 YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。 ```python @@ -128,7 +128,7 @@ python detect.py --weights yolov5s.pt --source 0 # 下面的命令重现 YOLOv5 在 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) 数据集上的结果。 最新的 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 和 [数据集](https://github.com/ultralytics/yolov5/tree/master/data) 将自动的从 YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。 -YOLOv5n/s/m/l/x 在 V100 GPU 的训练时间为 1/2/4/6/8 天（ [多GPU](https://github.com/ultralytics/yolov5/issues/475) 训练速度更快）。 +YOLOv5n/s/m/l/x 在 V100 GPU 的训练时间为 1/2/4/6/8 天（ [多GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) 训练速度更快）。 尽可能使用更大的 `--batch-size` ，或通过 `--batch-size -1` 实现 YOLOv5 [自动批处理](https://github.com/ultralytics/yolov5/pull/5092) 。下方显示的 batchsize 适用于 V100-16GB。 @@ -241,7 +241,7 @@ YOLOv5 超级容易上手，简单易学。我们优先考虑现实世界的结 - 所有模型都使用默认配置，训练 300 epochs。n和s模型使用 [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) ，其他模型都使用 [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml) 。 - \*\*mAPval\*\*在单模型单尺度上计算，数据集使用 [COCO val2017](http://cocodataset.org) 。
复现命令 `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` - **推理速度**在 COCO val 图像总体时间上进行平均得到，测试环境使用[AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/)实例。 NMS 时间 (大约 1 ms/img) 不包括在内。
复现命令 `python val.py --data coco.yaml --img 640 --task speed --batch 1` -- **TTA** [测试时数据增强](https://github.com/ultralytics/yolov5/issues/303) 包括反射和尺度变换。
复现命令 `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment` +- **TTA** [测试时数据增强](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation) 包括反射和尺度变换。
复现命令 `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
@@ -479,4 +479,4 @@ YOLOv5 在两种不同的 License 下可用： -[tta]: https://github.com/ultralytics/yolov5/issues/303 +[tta]: https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation diff --git a/classify/tutorial.ipynb b/classify/tutorial.ipynb index 58723608bdbe..ddf67c5519f5 100644 --- a/classify/tutorial.ipynb +++ b/classify/tutorial.ipynb @@ -1350,7 +1350,7 @@ "export COMET_API_KEY= # 2. paste API key\n", "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\n", "```\n", - "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n", + "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n", "\n", "\n", @@ -1372,7 +1372,7 @@ "\n", "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n", "\n", - "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) for details!\n", + "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!\n", "\n", "\n", "\"ClearML" @@ -1404,9 +1404,9 @@ "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n", "\n", "- **Notebooks** with free GPU: \"Run \"Open \"Open\n", - "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n", - "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n", - "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) \"Docker\n" + "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n", + "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n", + "- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) \"Docker\n" ] }, { diff --git a/hubconf.py b/hubconf.py index 9d820a54f290..73caf06685da 100644 --- a/hubconf.py +++ b/hubconf.py @@ -73,7 +73,7 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo return model.to(device) except Exception as e: - help_url = 'https://github.com/ultralytics/yolov5/issues/36' + help_url = 'https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading' s = f'{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help.' raise Exception(s) from e diff --git a/segment/train.py b/segment/train.py index 7e600f77d571..073fc742005b 100644 --- a/segment/train.py +++ b/segment/train.py @@ -12,7 +12,7 @@ Models: https://github.com/ultralytics/yolov5/tree/master/models Datasets: https://github.com/ultralytics/yolov5/tree/master/data -Tutorial: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data +Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data """ import argparse @@ -167,8 +167,10 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # DP mode if cuda and RANK == -1 and torch.cuda.device_count() > 1: - LOGGER.warning('WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n' - 'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.') + LOGGER.warning( + 'WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n' + 'See Multi-GPU Tutorial at https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training to get started.' + ) model = torch.nn.DataParallel(model) # SyncBatchNorm diff --git a/segment/tutorial.ipynb b/segment/tutorial.ipynb index cb52045bcb25..6e5caf53b8ff 100644 --- a/segment/tutorial.ipynb +++ b/segment/tutorial.ipynb @@ -463,7 +463,7 @@ "export COMET_API_KEY= # 2. paste API key\n", "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\n", "```\n", - "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n", + "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n", "\n", "\n", @@ -485,7 +485,7 @@ "\n", "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n", "\n", - "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) for details!\n", + "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!\n", "\n", "\n", "\"ClearML" @@ -517,9 +517,9 @@ "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n", "\n", "- **Notebooks** with free GPU: \"Run \"Open \"Open\n", - "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n", - "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n", - "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) \"Docker\n" + "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n", + "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n", + "- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) \"Docker\n" ] }, { diff --git a/train.py b/train.py index 7c403ee6d680..216da6399028 100644 --- a/train.py +++ b/train.py @@ -12,7 +12,7 @@ Models: https://github.com/ultralytics/yolov5/tree/master/models Datasets: https://github.com/ultralytics/yolov5/tree/master/data -Tutorial: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data +Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data """ import argparse @@ -175,8 +175,10 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # DP mode if cuda and RANK == -1 and torch.cuda.device_count() > 1: - LOGGER.warning('WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n' - 'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.') + LOGGER.warning( + 'WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n' + 'See Multi-GPU Tutorial at https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training to get started.' + ) model = torch.nn.DataParallel(model) # SyncBatchNorm diff --git a/tutorial.ipynb b/tutorial.ipynb index d2b54c9c60ef..be87068822af 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -498,7 +498,7 @@ "export COMET_API_KEY= # 2. paste API key\n", "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\n", "```\n", - "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n", + "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n", "\n", "\n", @@ -520,7 +520,7 @@ "\n", "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n", "\n", - "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) for details!\n", + "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!\n", "\n", "\n", "\"ClearML" @@ -555,9 +555,9 @@ "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n", "\n", "- **Notebooks** with free GPU: \"Run \"Open \"Open\n", - "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n", - "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n", - "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) \"Docker\n" + "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n", + "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n", + "- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) \"Docker\n" ] }, { diff --git a/utils/dataloaders.py b/utils/dataloaders.py index a5bd86d49d7e..26201c3c78fc 100644 --- a/utils/dataloaders.py +++ b/utils/dataloaders.py @@ -36,7 +36,7 @@ from utils.torch_utils import torch_distributed_zero_first # Parameters -HELP_URL = 'See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data' +HELP_URL = 'See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data' IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm' # include image suffixes VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv' # include video suffixes LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html diff --git a/utils/loggers/__init__.py b/utils/loggers/__init__.py index 9b4c1d13b778..c7c283b728ac 100644 --- a/utils/loggers/__init__.py +++ b/utils/loggers/__init__.py @@ -118,7 +118,7 @@ def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, self.clearml = None prefix = colorstr('ClearML: ') LOGGER.warning(f'{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging.' - f' See https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml#readme') + f' See https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration#readme') else: self.clearml = None