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Signed-off-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>

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* Update README.md

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* Update README.md

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* Update README.zh-CN.md

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---------

Signed-off-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan MunawarUltralyticsAssistantGlenn Jocher
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README.md
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@@ -1,7 +1,7 @@
<div align="center"> <div align="center">
<p> <p>
<a href="https://www.ultralytics.com/events/yolovision" target="_blank"> <a href="https://www.ultralytics.com/blog/all-you-need-to-know-about-ultralytics-yolo11-and-its-applications" target="_blank">
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a> <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO banner"></a>
</p> </p>
[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) [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/). 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 ```bash
git clone https://github.com/ultralytics/yolov5 # clone git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5 cd yolov5
pip install -r requirements.txt # install pip install -r requirements.txt # install
``` ```
</details> </details>
@@ -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`. `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 ```bash
python detect.py --weights yolov5s.pt --source 0 # webcam python detect.py --weights yolov5s.pt --source 0 # webcam
img.jpg # image python detect.py --weights yolov5s.pt --source img.jpg # image
vid.mp4 # video python detect.py --weights yolov5s.pt --source vid.mp4 # video
screen # screenshot python detect.py --weights yolov5s.pt --source screen # screenshot
path/ # directory python detect.py --weights yolov5s.pt --source path/ # directory
list.txt # list of images python detect.py --weights yolov5s.pt --source list.txt # list of images
list.streams # list of streams python detect.py --weights yolov5s.pt --source list.streams # list of streams
'path/*.jpg' # glob python detect.py --weights yolov5s.pt --source 'path/*.jpg' # glob
'https://youtu.be/LNwODJXcvt4' # YouTube python detect.py --weights yolov5s.pt --source 'https://youtu.be/LNwODJXcvt4' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream python detect.py --weights yolov5s.pt --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
``` ```
</details> </details>
@@ -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. 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 ```bash
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 yolov5n.yaml --batch-size 128
yolov5s 64 python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5s.yaml --batch-size 64
yolov5m 40 python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5m.yaml --batch-size 40
yolov5l 24 python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5l.yaml --batch-size 24
yolov5x 16 python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5x.yaml --batch-size 16
``` ```
<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png"> <img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
@@ -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: Validate YOLOv5s-seg mask mAP on COCO dataset:
```bash ```bash
bash data/scripts/get_coco.sh --val --segments # download COCO val segments split (780MB, 5000 images) 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 python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate
``` ```
### Predict ### 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: Validate YOLOv5m-cls accuracy on ImageNet-1k dataset:
```bash ```bash
bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) 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 python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate
``` ```
### Predict ### Predict
README.zh-CN.md
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<div align="center"> <div align="center">
<p> <p>
<a href="https://www.ultralytics.com/events/yolovision" target="_blank"> <a href="https://www.ultralytics.com/blog/all-you-need-to-know-about-ultralytics-yolo11-and-its-applications" target="_blank">
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a> <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO banner"></a>
</p> </p>
[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) [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/) 。 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 ```bash
git clone https://github.com/ultralytics/yolov5 # clone git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5 cd yolov5
pip install -r requirements.txt # install pip install -r requirements.txt # install
``` ```
</details> </details>
@@ -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` 。 `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 ```bash
python detect.py --weights yolov5s.pt --source 0 # webcam python detect.py --weights yolov5s.pt --source 0 # webcam
img.jpg # image python detect.py --weights yolov5s.pt --source img.jpg # image
vid.mp4 # video python detect.py --weights yolov5s.pt --source vid.mp4 # video
screen # screenshot python detect.py --weights yolov5s.pt --source screen # screenshot
path/ # directory python detect.py --weights yolov5s.pt --source path/ # directory
list.txt # list of images python detect.py --weights yolov5s.pt --source list.txt # list of images
list.streams # list of streams python detect.py --weights yolov5s.pt --source list.streams # list of streams
'path/*.jpg' # glob python detect.py --weights yolov5s.pt --source 'path/*.jpg' # glob
'https://youtu.be/LNwODJXcvt4' # YouTube python detect.py --weights yolov5s.pt --source 'https://youtu.be/LNwODJXcvt4' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream python detect.py --weights yolov5s.pt --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
``` ```
</details> </details>
@@ -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。 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 ```bash
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 yolov5n.yaml --batch-size 128
yolov5s 64 python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5s.yaml --batch-size 64
yolov5m 40 python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5m.yaml --batch-size 40
yolov5l 24 python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5l.yaml --batch-size 24
yolov5x 16 python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5x.yaml --batch-size 16
``` ```
<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png"> <img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
@@ -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： 在 COCO 数据集上验证 YOLOv5s-seg mask mAP：
```bash ```bash
bash data/scripts/get_coco.sh --val --segments # 下载 COCO val segments 数据集 (780MB, 5000 images) 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 python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # 验证
``` ```
### 预测 ### 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： 在 ImageNet-1k 数据集上验证 YOLOv5m-cls 的准确性：
```bash ```bash
bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) 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 python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate
``` ```
### 预测 ### 预测
utils/loggers/clearml/README.md
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@@ -45,7 +45,7 @@ That's it! You're done 😎
To enable ClearML experiment tracking, simply install the ClearML pip package. To enable ClearML experiment tracking, simply install the ClearML pip package.
```bash ```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. 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! Now that you have a ClearML dataset, you can very simply use it to train custom YOLOv5 🚀 models!
```bash ```bash
python train.py --img 640 --batch 16 --epochs 3 --data clearml://<your_dataset_id> --weights yolov5s.pt --cache python train.py --img 640 --batch 16 --epochs 3 --data clearml:// yolov5s.pt --cache YOUR_DATASET_ID --weights
``` ```
## 👀 Hyperparameter Optimization ## 👀 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: You can turn any machine (a cloud VM, a local GPU machine, your own laptop ... ) into a ClearML agent by simply running:
```bash ```bash
clearml-agent daemon --queue <queues_to_listen_to> [--docker] clearml-agent daemon --queue QUEUES_TO_LISTEN_TO [--docker]
``` ```
### Cloning, Editing And Enqueuing ### Cloning, Editing And Enqueuing
utils/loggers/comet/README.md
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@@ -102,12 +102,12 @@ Logging Models to Comet is disabled by default. To enable it, pass the `save-per
```shell ```shell
python train.py \ python train.py \
--img 640 \ --img 640 \
--batch 16 \ --batch 16 \
--epochs 5 \ --epochs 5 \
--data coco128.yaml \ --data coco128.yaml \
--weights yolov5s.pt \ --weights yolov5s.pt \
--save-period 1 --save-period 1
``` ```
## Logging Model Predictions ## Logging Model Predictions
@@ -122,12 +122,12 @@ Here is an [example project using the Panel](https://www.comet.com/examples/come
```shell ```shell
python train.py \ python train.py \
--img 640 \ --img 640 \
--batch 16 \ --batch 16 \
--epochs 5 \ --epochs 5 \
--data coco128.yaml \ --data coco128.yaml \
--weights yolov5s.pt \ --weights yolov5s.pt \
--bbox_interval 2 --bbox_interval 2
``` ```
### Controlling the number of Prediction Images logged to Comet ### 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 ```shell
env COMET_MAX_IMAGE_UPLOADS=200 python train.py \ env COMET_MAX_IMAGE_UPLOADS=200 python train.py \
--img 640 \ --img 640 \
--batch 16 \ --batch 16 \
--epochs 5 \ --epochs 5 \
--data coco128.yaml \ --data coco128.yaml \
--weights yolov5s.pt \ --weights yolov5s.pt \
--bbox_interval 1 --bbox_interval 1
``` ```
### Logging Class Level Metrics ### Logging Class Level Metrics
@@ -150,11 +150,11 @@ Use the `COMET_LOG_PER_CLASS_METRICS` environment variable to log mAP, precision
```shell ```shell
env COMET_LOG_PER_CLASS_METRICS=true python train.py \ env COMET_LOG_PER_CLASS_METRICS=true python train.py \
--img 640 \ --img 640 \
--batch 16 \ --batch 16 \
--epochs 5 \ --epochs 5 \
--data coco128.yaml \ --data coco128.yaml \
--weights yolov5s.pt --weights yolov5s.pt
``` ```
## Uploading a Dataset to Comet Artifacts ## Uploading a Dataset to Comet Artifacts
@@ -165,12 +165,12 @@ The dataset be organized in the way described in the [YOLOv5 documentation](http
```shell ```shell
python train.py \ python train.py \
--img 640 \ --img 640 \
--batch 16 \ --batch 16 \
--epochs 5 \ --epochs 5 \
--data coco128.yaml \ --data coco128.yaml \
--weights yolov5s.pt \ --weights yolov5s.pt \
--upload_dataset --upload_dataset
``` ```
You can find the uploaded dataset in the Artifacts tab in your Comet Workspace <img width="1073" alt="artifact-1" src="https://user-images.githubusercontent.com/7529846/186929193-162718bf-ec7b-4eb9-8c3b-86b3763ef8ea.png"> You can find the uploaded dataset in the Artifacts tab in your Comet Workspace <img width="1073" alt="artifact-1" src="https://user-images.githubusercontent.com/7529846/186929193-162718bf-ec7b-4eb9-8c3b-86b3763ef8ea.png">
@@ -192,11 +192,11 @@ Then pass this file to your training script in the following way
```shell ```shell
python train.py \ python train.py \
--img 640 \ --img 640 \
--batch 16 \ --batch 16 \
--epochs 5 \ --epochs 5 \
--data artifact.yaml \ --data artifact.yaml \
--weights yolov5s.pt --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. <img width="1391" alt="artifact-4" src="https://user-images.githubusercontent.com/7529846/186929264-4c4014fa-fe51-4f3c-a5c5-f6d24649b1b4.png"> 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. <img width="1391" alt="artifact-4" src="https://user-images.githubusercontent.com/7529846/186929264-4c4014fa-fe51-4f3c-a5c5-f6d24649b1b4.png">
@@ -211,7 +211,7 @@ This will restore the run to its state before the interruption, which includes r
```shell ```shell
python train.py \ python train.py \
--resume "comet://<your run path>" --resume "comet://<your run path>"
``` ```
## Hyperparameter Search with the Comet Optimizer ## Hyperparameter Search with the Comet Optimizer