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multi-weights for sources #9255
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👋 Hello @zxcvmmm, 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 screenshots and minimum viable code to reproduce your issue, otherwise we can not help you. If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online W&B logging if available. For business inquiries or professional support requests please visit https://ultralytics.com or email support@ultralytics.com. RequirementsPython>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started: git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install EnvironmentsYOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
StatusIf this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), validation (val.py), inference (detect.py) and export (export.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit. |
@zxcvmmm 👋 Hello! Thanks for asking about handling inference results. YOLOv5 🚀 PyTorch Hub models allow for simple model loading and inference in a pure python environment without using You can load any number of YOLOv5 models and perform custom inference this way. Simple Inference ExampleThis example loads a pretrained YOLOv5s model from PyTorch Hub as import torch
# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # yolov5n - yolov5x6 official model
# 'custom', 'path/to/best.pt') # custom model
# Images
im = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, URL, PIL, OpenCV, numpy, list
# Inference
results = model(im)
# Results
results.print() # or .show(), .save(), .crop(), .pandas(), etc.
results.xyxy[0] # im predictions (tensor)
results.pandas().xyxy[0] # im predictions (pandas)
# xmin ymin xmax ymax confidence class name
# 0 749.50 43.50 1148.0 704.5 0.874023 0 person
# 2 114.75 195.75 1095.0 708.0 0.624512 0 person
# 3 986.00 304.00 1028.0 420.0 0.286865 27 tie
results.pandas().xyxy[0].value_counts('name') # class counts (pandas)
# person 2
# tie 1 See YOLOv5 PyTorch Hub Tutorial for details. Good luck 🍀 and let us know if you have any other questions! |
👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs. Access additional YOLOv5 🚀 resources:
Access additional Ultralytics ⚡ resources:
Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed! Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐! |
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Question
Hi there,
I want to show the results by applying different weights to multiple video sources that are read into streams.txt.
(ex. weight#1 for cam 0,1,2 / weight #2 for cam 3,4,5)
Can you help me how to modify the code?
Additional
No response
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