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72 lines
5.3 KiB
72 lines
5.3 KiB
--- |
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comments: true |
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description: Dive into YOLOv5 for object detection with our easy-to-follow guide on setup, model training, and image inference using PyTorch. Get started now! |
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keywords: YOLOv5 Tutorial, Object Detection Guide, PyTorch Model Training, Inference with YOLOv5, Ultralytics YOLOv5 Setup |
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--- |
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# YOLOv5 Quickstart 🚀 |
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Embark on your journey into the dynamic realm of real-time object detection with YOLOv5! This guide is crafted to serve as a comprehensive starting point for AI enthusiasts and professionals aiming to master YOLOv5. From initial setup to advanced training techniques, we've got you covered. By the end of this guide, you'll have the knowledge to implement YOLOv5 into your projects confidently. Let's ignite the engines and soar into YOLOv5! |
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## Install |
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Prepare for launch by cloning the repository and establishing the environment. This ensures that all the necessary [requirements](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) are installed. Check that you have [**Python>=3.8.0**](https://www.python.org/) and [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/) ready for takeoff. |
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```bash |
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git clone https://github.com/ultralytics/yolov5 # clone repository |
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cd yolov5 |
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pip install -r requirements.txt # install dependencies |
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``` |
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## Inference with PyTorch Hub |
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Experience the simplicity of YOLOv5 [PyTorch Hub](./tutorials/pytorch_hub_model_loading.md) inference, where [models](https://github.com/ultralytics/yolov5/tree/master/models) are seamlessly downloaded from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). |
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```python |
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import torch |
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# Model loading |
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model = torch.hub.load("ultralytics/yolov5", "yolov5s") # Can be 'yolov5n' - 'yolov5x6', or 'custom' |
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# Inference on images |
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img = "https://ultralytics.com/images/zidane.jpg" # Can be a file, Path, PIL, OpenCV, numpy, or list of images |
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# Run inference |
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results = model(img) |
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# Display results |
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results.print() # Other options: .show(), .save(), .crop(), .pandas(), etc. |
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``` |
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## Inference with detect.py |
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Harness `detect.py` for versatile inference on various sources. It automatically fetches [models](https://github.com/ultralytics/yolov5/tree/master/models) from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saves results with ease. |
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```bash |
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python detect.py --weights yolov5s.pt --source 0 # webcam |
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img.jpg # image |
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vid.mp4 # video |
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screen # screenshot |
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path/ # directory |
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list.txt # list of images |
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list.streams # list of streams |
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'path/*.jpg' # glob |
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'https://youtu.be/LNwODJXcvt4' # YouTube |
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream |
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``` |
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## Training |
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Replicate the YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) benchmarks with the instructions below. The necessary [models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) are pulled directly from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training YOLOv5n/s/m/l/x on a V100 GPU should typically take 1/2/4/6/8 days respectively (note that [Multi-GPU](./tutorials/multi_gpu_training.md) setups work faster). Maximize performance by using the highest possible `--batch-size` or use `--batch-size -1` for the YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092) feature. The following batch sizes are ideal for V100-16GB GPUs. |
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```bash |
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python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128 |
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yolov5s 64 |
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yolov5m 40 |
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yolov5l 24 |
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yolov5x 16 |
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``` |
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<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png" alt="YOLO training curves"> |
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To conclude, YOLOv5 is not only a state-of-the-art tool for object detection but also a testament to the power of machine learning in transforming the way we interact with the world through visual understanding. As you progress through this guide and begin applying YOLOv5 to your projects, remember that you are at the forefront of a technological revolution, capable of achieving remarkable feats. Should you need further insights or support from fellow visionaries, you're invited to our [GitHub repository](https://github.com/ultralytics/yolov5) home to a thriving community of developers and researchers. Keep exploring, keep innovating, and enjoy the marvels of YOLOv5. Happy detecting! 🌠🔍
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