github-actions[bot]
commented
1 year ago 👋 Hello @jose-cisco, 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.
If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.
If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.
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):
- Notebooks with free GPU:
- Google Cloud Deep Learning VM. See GCP Quickstart Guide
- Amazon Deep Learning AMI. See AWS Quickstart Guide
- Docker Image. See Docker Quickstart Guide
Status
If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training, validation, inference, export and benchmarks on macOS, Windows, and Ubuntu every 24 hours and on every commit.
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
Search before asking
YOLOv5 Component
Other
Bug
when we ran this, we saw .007 second inference time. That is 140 FPS on a TESLA P100!
use the best weights!
%cd /content/yolov5/ !python detect.py --weights runs/train/yolov5s_results/weights/best.pt --img 256 --conf 0.4 --source ../test/images
/content/yolov5 detect: weights=['runs/train/yolov5s_results/weights/best.pt'], source=../test/images, data=data/coco128.yaml, imgsz=[256, 256], conf_thres=0.4, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1 YOLOv5 🚀 v7.0-72-g064365d Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)
Fusing layers... custom_YOLOv5s summary: 182 layers, 7260003 parameters, 0 gradients Traceback (most recent call last): File "/content/yolov5/detect.py", line 261, in
main(opt)
File "/content/yolov5/detect.py", line 256, in main
run(*vars(opt))
File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(args, **kwargs)
File "/content/yolov5/detect.py", line 111, in run
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
File "/content/yolov5/utils/dataloaders.py", line 254, in init
raise FileNotFoundError(f'{p} does not exist')
FileNotFoundError: /content/test/images does not exist
what should i have change after i follow up on tutorial.
Environment
-YOLO V.5 On Google Colaboratory
Minimal Reproducible Example
No response
Additional
No response
Are you willing to submit a PR?