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7 months ago 👋 Hello @paulikoe, 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
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YOLOv5 Component
Other
Bug
TypeError Traceback (most recent call last)
3 frames /usr/local/lib/python3.10/dist-packages/ultralytics/nn/autobackend.py in init(self, weights, device, dnn, data, fp16, batch, fuse, verbose) 141 if nn_module: 142 model = weights.to(device) --> 143 model = model.fuse(verbose=verbose) if fuse else model 144 if hasattr(model, "kpt_shape"): 145 kpt_shape = model.kpt_shape # pose-only
TypeError: BaseModel.fuse() got an unexpected keyword argument 'verbose'
Environment
No response
Minimal Reproducible Example
`from ultralytics import YOLO model = YOLO('/content/gdrive/MyDrive/yolov5_diplomka/yolov5/runs/train/exp3/weights/best.pt') source = '/content/gdrive/MyDrive/Data/Vid_and_pictures/20240227_102420.jpg' results = model.predict(source, conf=0.5, imgsz=320, save=True, save_txt = True, save_conf=True) # list of Results objects
for result in results: boxes = result.boxes # Boxes object for bounding box outputs masks = result.masks # Masks object for segmentation masks outputs keypoints = result.keypoints # Keypoints object for pose outputs probs = result.probs # Probs object for classification outputs result.show() # display to screen result.save(filename='result_predict.jpg') # save to disk`
Sorry I am new in neural networks. Can you please solve my problem? I am trying to use my model on a picture.
Are you willing to submit a PR?