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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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authored and GitHub committed 2025-03-23 11:35:00 +01:00
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<div align="center">
<p>
<a href="https://www.ultralytics.com/events/yolovision" target="_blank">
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
<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" alt="Ultralytics YOLO banner"></a>
</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)
@@ -107,15 +107,15 @@ results.print() # or .show(), .save(), .crop(), .pandas(), etc.
```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 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
```
</details>
@@ -127,10 +127,10 @@ The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5
```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 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
```
<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
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@@ -1,7 +1,7 @@
<div align="center">
<p>
<a href="https://www.ultralytics.com/events/yolovision" target="_blank">
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
<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" alt="Ultralytics YOLO banner"></a>
</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)
@@ -105,15 +105,15 @@ results.print() # or .show(), .save(), .crop(), .pandas(), etc.
```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 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
```
</details>
@@ -126,10 +126,10 @@ python detect.py --weights yolov5s.pt --source 0 #
```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 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
```
<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
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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.
```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://<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
@@ -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 <queues_to_listen_to> [--docker]
clearml-agent daemon --queue QUEUES_TO_LISTEN_TO [--docker]
```
### Cloning, Editing And Enqueuing
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@@ -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 <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
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. <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
python train.py \
--resume "comet://<your run path>"
--resume "comet://<your run path>"
```
## Hyperparameter Search with the Comet Optimizer