--- comments: true description: Learn how to prune YOLOv5 models for improved performance. Follow this step-by-step guide to optimize your YOLOv5 models effectively. keywords: YOLOv5 pruning, model pruning, YOLOv5 optimization, YOLOv5 guide, machine learning pruning --- 📚 This guide explains how to apply **pruning** to YOLOv5 🚀 models. ## Before You Start 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/). [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). ```bash git clone https://github.com/ultralytics/yolov5 # clone cd yolov5 pip install -r requirements.txt # install ``` ## Test Normally Before pruning we want to establish a baseline performance to compare to. This command tests YOLOv5x on COCO val2017 at image size 640 pixels. `yolov5x.pt` is the largest and most accurate model available. Other options are `yolov5s.pt`, `yolov5m.pt` and `yolov5l.pt`, or you own checkpoint from training a custom dataset `./weights/best.pt`. For details on all available models please see our README [table](https://github.com/ultralytics/yolov5#pretrained-checkpoints). ```bash python val.py --weights yolov5x.pt --data coco.yaml --img 640 --half ``` Output: ```shell val: data=/content/yolov5/data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True, dnn=False YOLOv5 🚀 v6.0-224-g4c40933 torch 1.10.0+cu111 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB) Fusing layers... Model Summary: 444 layers, 86705005 parameters, 0 gradients val: Scanning '/content/datasets/coco/val2017.cache' images and labels... 4952 found, 48 missing, 0 empty, 0 corrupt: 100% 5000/5000 [00:00<?, ?it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:12<00:00, 2.16it/s] all 5000 36335 0.732 0.628 0.683 0.496 Speed: 0.1ms pre-process, 5.2ms inference, 1.7ms NMS per image at shape (32, 3, 640, 640) # <--- base speed Evaluating pycocotools mAP... saving runs/val/exp2/yolov5x_predictions.json... ... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.507 # <--- base mAP Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.689 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.552 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.345 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.559 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.652 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.381 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.630 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.682 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.526 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.731 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.829 Results saved to runs/val/exp ``` ## Test YOLOv5x on COCO (0.30 sparsity) We repeat the above test with a pruned model by using the `torch_utils.prune()` command. We update `val.py` to prune YOLOv5x to 0.3 sparsity: <img width="894" alt="Screenshot 2022-02-02 at 22 54 18" src="https://user-images.githubusercontent.com/26833433/152243799-b0ac2777-b1a8-47b1-801a-2e4c93c06ead.png"> 30% pruned output: ```bash val: data=/content/yolov5/data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True, dnn=False YOLOv5 🚀 v6.0-224-g4c40933 torch 1.10.0+cu111 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB) Fusing layers... Model Summary: 444 layers, 86705005 parameters, 0 gradients Pruning model... 0.3 global sparsity val: Scanning '/content/datasets/coco/val2017.cache' images and labels... 4952 found, 48 missing, 0 empty, 0 corrupt: 100% 5000/5000 [00:00<?, ?it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:11<00:00, 2.19it/s] all 5000 36335 0.724 0.614 0.671 0.478 Speed: 0.1ms pre-process, 5.2ms inference, 1.7ms NMS per image at shape (32, 3, 640, 640) # <--- prune mAP Evaluating pycocotools mAP... saving runs/val/exp3/yolov5x_predictions.json... ... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.489 # <--- prune mAP Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.677 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.537 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.334 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.542 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.635 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.370 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.612 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.664 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.496 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.722 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.803 Results saved to runs/val/exp3 ``` In the results we can observe that we have achieved a **sparsity of 30%** in our model after pruning, which means that 30% of the model's weight parameters in `nn.Conv2d` layers are equal to 0. **Inference time is essentially unchanged**, while the model's **AP and AR scores a slightly reduced**. ## Supported Environments Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects. - **Free GPU Notebooks**: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> - **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md) - **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md) - **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md) - **Docker**: [Docker Quickstart Guide](../environments/docker_image_quickstart_tutorial.md) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a> ## Project Status <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a> This badge indicates that all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are successfully passing. These CI tests rigorously check the functionality and performance of YOLOv5 across various key aspects: [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py), and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py). They ensure consistent and reliable operation on macOS, Windows, and Ubuntu, with tests conducted every 24 hours and upon each new commit.