Update GitHub banner redirect (#13542)

* Update README.md Signed-off-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com> * Auto-format by https://ultralytics.com/actions * Update README.md Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Auto-format by https://ultralytics.com/actions * Update README.md Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update README.zh-CN.md Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update README.md Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Auto-format by https://ultralytics.com/actions * Update README.md Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update README.md Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update README.zh-CN.md Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> --------- 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>
2025-03-23 11:35:00 +01:00
1 parent 8cc449636d
commit ba1a483345
@@ -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>

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@@ -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
 ```

 </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`.

 ```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
 ```

 </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.

 ```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
 ```

 <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:

 ```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