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1 year ago 👋 Hello @hyeok94, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.
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Requirements
Python>=3.8.0 with all requirements.txt installed including PyTorch>=1.8. To get started:
git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # installEnvironments
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
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- Google Cloud Deep Learning VM. See GCP Quickstart Guide
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- Docker Image. See Docker Quickstart Guide
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Introducing YOLOv8 🚀
We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 🚀!
Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.
Check out our YOLOv8 Docs for details and get started with:
pip install ultralytics
glenn-jocher
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Question
hello there i'm on the project about real time detection service (wasp & mite) for the beekeeper tried all five model but it doesn't show huge difference so i choose yolov5n cause it's lightest and fastest collected 8928 images with using opencv (video frame capture) and then box labelled one by one
give hyper parameter like this (to be honest didn't fully understand how they gonna affect the result )
[hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0 ]
and finally run the yolov5 model
[ !python train.py --img 1024 --batch 32 --epochs 500 --data /content/drive/MyDrive/data.yaml --cfg /content/yolov5/models/custom_yolov5n.yaml --weights yolov5n.pt --name yolov5n_result --cache ]
result was like this
when i tried to predict the video using the best.pt it wasn't satisfying but it worked anyhow
the problem happend when using webcam
first. it shows low frame rate ( but i fixed uisng cuda )
second. it doesn't catch object well (real problem)
so the question is how can i enhance the webcam or anyother camera detection rate? possibility?
Additional
No response