From ba1a483345e5d8e1e3982514aad130d6f073cf73 Mon Sep 17 00:00:00 2001 From: Muhammad Rizwan Munawar Date: Sun, 23 Mar 2025 15:35:00 +0500 Subject: [PATCH] Update GitHub banner redirect (#13542) * Update README.md Signed-off-by: Muhammad Rizwan Munawar * Auto-format by https://ultralytics.com/actions * Update README.md Signed-off-by: Glenn Jocher * Auto-format by https://ultralytics.com/actions * Update README.md Signed-off-by: Glenn Jocher * Update README.zh-CN.md Signed-off-by: Glenn Jocher * Update README.md Signed-off-by: Glenn Jocher * Auto-format by https://ultralytics.com/actions * Update README.md Signed-off-by: Glenn Jocher * Update README.md Signed-off-by: Glenn Jocher * Update README.zh-CN.md Signed-off-by: Glenn Jocher --------- Signed-off-by: Muhammad Rizwan Munawar Signed-off-by: Glenn Jocher Co-authored-by: UltralyticsAssistant Co-authored-by: Glenn Jocher --- README.md | 46 +++++++++++----------- README.zh-CN.md | 46 +++++++++++----------- utils/loggers/clearml/README.md | 6 +-- utils/loggers/comet/README.md | 70 ++++++++++++++++----------------- 4 files changed, 84 insertions(+), 84 deletions(-) diff --git a/README.md b/README.md index 46b2a83..a308bbe 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@

- - + + Ultralytics YOLO banner

[中文](https://docs.ultralytics.com/zh) | [한국어](https://docs.ultralytics.com/ko) | [日本語](https://docs.ultralytics.com/ja) | [Русский](https://docs.ultralytics.com/ru) | [Deutsch](https://docs.ultralytics.com/de) | [Français](https://docs.ultralytics.com/fr) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt) | [Türkçe](https://docs.ultralytics.com/tr) | [Tiếng Việt](https://docs.ultralytics.com/vi) | [العربية](https://docs.ultralytics.com/ar) @@ -70,9 +70,9 @@ See the [YOLOv5 Docs](https://docs.ultralytics.com/yolov5/) for full documentati Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a [**Python>=3.8.0**](https://www.python.org/) environment, including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). ```bash -git clone https://github.com/ultralytics/yolov5 # clone +git clone https://github.com/ultralytics/yolov5 # clone cd yolov5 -pip install -r requirements.txt # install +pip install -r requirements.txt # install ``` @@ -106,16 +106,16 @@ results.print() # or .show(), .save(), .crop(), .pandas(), etc. `detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`. ```bash -python detect.py --weights yolov5s.pt --source 0 # webcam - img.jpg # image - vid.mp4 # video - screen # screenshot - path/ # directory - list.txt # list of images - list.streams # list of streams - 'path/*.jpg' # glob - 'https://youtu.be/LNwODJXcvt4' # YouTube - 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream +python detect.py --weights yolov5s.pt --source 0 # webcam +python detect.py --weights yolov5s.pt --source img.jpg # image +python detect.py --weights yolov5s.pt --source vid.mp4 # video +python detect.py --weights yolov5s.pt --source screen # screenshot +python detect.py --weights yolov5s.pt --source path/ # directory +python detect.py --weights yolov5s.pt --source list.txt # list of images +python detect.py --weights yolov5s.pt --source list.streams # list of streams +python detect.py --weights yolov5s.pt --source 'path/*.jpg' # glob +python detect.py --weights yolov5s.pt --source 'https://youtu.be/LNwODJXcvt4' # YouTube +python detect.py --weights yolov5s.pt --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream ``` @@ -126,11 +126,11 @@ python detect.py --weights yolov5s.pt --source 0 # The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) 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://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. ```bash -python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128 - yolov5s 64 - yolov5m 40 - yolov5l 24 - yolov5x 16 +python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128 +python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5s.yaml --batch-size 64 +python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5m.yaml --batch-size 40 +python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5l.yaml --batch-size 24 +python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5x.yaml --batch-size 16 ``` @@ -289,8 +289,8 @@ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train Validate YOLOv5s-seg mask mAP on COCO dataset: ```bash -bash data/scripts/get_coco.sh --val --segments # download COCO val segments split (780MB, 5000 images) -python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate +bash data/scripts/get_coco.sh --val --segments # download COCO val segments split (780MB, 5000 images) +python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate ``` ### Predict @@ -380,8 +380,8 @@ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/trai Validate YOLOv5m-cls accuracy on ImageNet-1k dataset: ```bash -bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) -python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate +bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) +python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate ``` ### Predict diff --git a/README.zh-CN.md b/README.zh-CN.md index b76c66d..062bf8f 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -1,7 +1,7 @@

- - + + Ultralytics YOLO banner

[中文](https://docs.ultralytics.com/zh) | [한국어](https://docs.ultralytics.com/ko) | [日本語](https://docs.ultralytics.com/ja) | [Русский](https://docs.ultralytics.com/ru) | [Deutsch](https://docs.ultralytics.com/de) | [Français](https://docs.ultralytics.com/fr) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt) | [Türkçe](https://docs.ultralytics.com/tr) | [Tiếng Việt](https://docs.ultralytics.com/vi) | [العربية](https://docs.ultralytics.com/ar) @@ -68,9 +68,9 @@ pip install ultralytics 克隆 repo，并要求在 [**Python>=3.8.0**](https://www.python.org/) 环境中安装 [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) ，且要求 [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/) 。 ```bash -git clone https://github.com/ultralytics/yolov5 # clone +git clone https://github.com/ultralytics/yolov5 # clone cd yolov5 -pip install -r requirements.txt # install +pip install -r requirements.txt # install ``` @@ -104,16 +104,16 @@ results.print() # or .show(), .save(), .crop(), .pandas(), etc. `detect.py` 在各种来源上运行推理， [模型](https://github.com/ultralytics/yolov5/tree/master/models) 自动从 最新的YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载，并将结果保存到 `runs/detect` 。 ```bash -python detect.py --weights yolov5s.pt --source 0 # webcam - img.jpg # image - vid.mp4 # video - screen # screenshot - path/ # directory - list.txt # list of images - list.streams # list of streams - 'path/*.jpg' # glob - 'https://youtu.be/LNwODJXcvt4' # YouTube - 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream +python detect.py --weights yolov5s.pt --source 0 # webcam +python detect.py --weights yolov5s.pt --source img.jpg # image +python detect.py --weights yolov5s.pt --source vid.mp4 # video +python detect.py --weights yolov5s.pt --source screen # screenshot +python detect.py --weights yolov5s.pt --source path/ # directory +python detect.py --weights yolov5s.pt --source list.txt # list of images +python detect.py --weights yolov5s.pt --source list.streams # list of streams +python detect.py --weights yolov5s.pt --source 'path/*.jpg' # glob +python detect.py --weights yolov5s.pt --source 'https://youtu.be/LNwODJXcvt4' # YouTube +python detect.py --weights yolov5s.pt --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream ``` @@ -125,11 +125,11 @@ python detect.py --weights yolov5s.pt --source 0 # 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://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。 ```bash -python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128 - yolov5s 64 - yolov5m 40 - yolov5l 24 - yolov5x 16 +python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128 +python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5s.yaml --batch-size 64 +python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5m.yaml --batch-size 40 +python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5l.yaml --batch-size 24 +python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5x.yaml --batch-size 16 ``` @@ -290,8 +290,8 @@ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train 在 COCO 数据集上验证 YOLOv5s-seg mask mAP： ```bash -bash data/scripts/get_coco.sh --val --segments # 下载 COCO val segments 数据集 (780MB, 5000 images) -python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # 验证 +bash data/scripts/get_coco.sh --val --segments # 下载 COCO val segments 数据集 (780MB, 5000 images) +python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # 验证 ``` ### 预测 @@ -380,8 +380,8 @@ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/trai 在 ImageNet-1k 数据集上验证 YOLOv5m-cls 的准确性： ```bash -bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) -python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate +bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) +python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate ``` ### 预测 diff --git a/utils/loggers/clearml/README.md b/utils/loggers/clearml/README.md index 374765d..dddefa1 100644 --- a/utils/loggers/clearml/README.md +++ b/utils/loggers/clearml/README.md @@ -45,7 +45,7 @@ That's it! You're done 😎 To enable ClearML experiment tracking, simply install the ClearML pip package. ```bash -pip install clearml>=1.2.0 +pip install clearml ``` This will enable integration with the YOLOv5 training script. Every training run from now on, will be captured and stored by the ClearML experiment manager. @@ -144,7 +144,7 @@ clearml-data close Now that you have a ClearML dataset, you can very simply use it to train custom YOLOv5 🚀 models! ```bash -python train.py --img 640 --batch 16 --epochs 3 --data clearml:// --weights yolov5s.pt --cache +python train.py --img 640 --batch 16 --epochs 3 --data clearml:// yolov5s.pt --cache YOUR_DATASET_ID --weights ``` ## 👀 Hyperparameter Optimization @@ -177,7 +177,7 @@ In short: every experiment tracked by the experiment manager contains enough inf You can turn any machine (a cloud VM, a local GPU machine, your own laptop ... ) into a ClearML agent by simply running: ```bash -clearml-agent daemon --queue [--docker] +clearml-agent daemon --queue QUEUES_TO_LISTEN_TO [--docker] ``` ### Cloning, Editing And Enqueuing diff --git a/utils/loggers/comet/README.md b/utils/loggers/comet/README.md index 52f344d..353698d 100644 --- a/utils/loggers/comet/README.md +++ b/utils/loggers/comet/README.md @@ -102,12 +102,12 @@ Logging Models to Comet is disabled by default. To enable it, pass the `save-per ```shell python train.py \ ---img 640 \ ---batch 16 \ ---epochs 5 \ ---data coco128.yaml \ ---weights yolov5s.pt \ ---save-period 1 + --img 640 \ + --batch 16 \ + --epochs 5 \ + --data coco128.yaml \ + --weights yolov5s.pt \ + --save-period 1 ``` ## Logging Model Predictions @@ -122,12 +122,12 @@ Here is an [example project using the Panel](https://www.comet.com/examples/come ```shell python train.py \ ---img 640 \ ---batch 16 \ ---epochs 5 \ ---data coco128.yaml \ ---weights yolov5s.pt \ ---bbox_interval 2 + --img 640 \ + --batch 16 \ + --epochs 5 \ + --data coco128.yaml \ + --weights yolov5s.pt \ + --bbox_interval 2 ``` ### Controlling the number of Prediction Images logged to Comet @@ -136,12 +136,12 @@ When logging predictions from YOLOv5, Comet will log the images associated with ```shell env COMET_MAX_IMAGE_UPLOADS=200 python train.py \ ---img 640 \ ---batch 16 \ ---epochs 5 \ ---data coco128.yaml \ ---weights yolov5s.pt \ ---bbox_interval 1 + --img 640 \ + --batch 16 \ + --epochs 5 \ + --data coco128.yaml \ + --weights yolov5s.pt \ + --bbox_interval 1 ``` ### Logging Class Level Metrics @@ -150,11 +150,11 @@ Use the `COMET_LOG_PER_CLASS_METRICS` environment variable to log mAP, precision ```shell env COMET_LOG_PER_CLASS_METRICS=true python train.py \ ---img 640 \ ---batch 16 \ ---epochs 5 \ ---data coco128.yaml \ ---weights yolov5s.pt + --img 640 \ + --batch 16 \ + --epochs 5 \ + --data coco128.yaml \ + --weights yolov5s.pt ``` ## Uploading a Dataset to Comet Artifacts @@ -165,12 +165,12 @@ The dataset be organized in the way described in the [YOLOv5 documentation](http ```shell python train.py \ ---img 640 \ ---batch 16 \ ---epochs 5 \ ---data coco128.yaml \ ---weights yolov5s.pt \ ---upload_dataset + --img 640 \ + --batch 16 \ + --epochs 5 \ + --data coco128.yaml \ + --weights yolov5s.pt \ + --upload_dataset ``` You can find the uploaded dataset in the Artifacts tab in your Comet Workspace artifact-1 @@ -192,11 +192,11 @@ Then pass this file to your training script in the following way ```shell python train.py \ ---img 640 \ ---batch 16 \ ---epochs 5 \ ---data artifact.yaml \ ---weights yolov5s.pt + --img 640 \ + --batch 16 \ + --epochs 5 \ + --data artifact.yaml \ + --weights yolov5s.pt ``` Artifacts also allow you to track the lineage of data as it flows through your Experimentation workflow. Here you can see a graph that shows you all the experiments that have used your uploaded dataset. artifact-4 @@ -211,7 +211,7 @@ This will restore the run to its state before the interruption, which includes r ```shell python train.py \ ---resume "comet://" + --resume "comet://" ``` ## Hyperparameter Search with the Comet Optimizer