Comments (2)
👋 Hello @SiYue1252, 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 # install
Environments
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
from yolov5.
Hello! 😊 It appears there's a small misunderstanding in how the model is loaded from the .pt
file. torch.load('yolov5_path/best.pt')
loads a pre-trained YOLOv5 model as a dictionary, including model weights and possibly other information, rather than directly as a model object. That's why you're seeing an error when trying to use .load_state_dict()
on a dictionary. Here's a quick fix:
# Load the model
model = torch.load('yolov5_path/best.pt', map_location=device)['model']
# Now you can directly use the model for inference without loading state_dict again
model.eval()
This loads your model and sets it to evaluation mode, ready for inference. Remember, the torch.load('yolov5_path/best.pt')
command returns the entire checkpoint, from which you only need the model
for inference or further training tasks. No need to load state.pth
again if you've already got everything in best.pt
. Hope this clears things up!
from yolov5.
Related Issues (20)
- about eval.py HOT 1
- Need advice for training a YOLOv5-obb model HOT 2
- Code doubts about the model in the detection process HOT 2
- predicting from 2D array HOT 2
- Same yolov5s training, but one over-fitting and one training is very good. HOT 2
- Hello, I have some questions about the YOLOv5 code. Could you please help me answer them? HOT 2
- Different results from train.py and val.py HOT 1
- How to change training input image size? HOT 8
- Cannot select specific coda device HOT 2
- Run yolov5 using tensor rt HOT 1
- Is it possible to add ShuffleNetV2 as backbone in the official repo? HOT 2
- Memory Error When Training YOLOv5 Using Git Bash HOT 4
- How to use tensor rt in yolov5 detection HOT 1
- resume_evolve BUG!!! HOT 3
- Classification training model error HOT 2
- How do Yolo target assignments to anchors work? HOT 3
- roc curve HOT 5
- Confusion Matrix wrong output HOT 2
- Zero recall and zero precision even after 100 epochs and pretrained weights HOT 2
- May I ask yolov5 how to port the method of calculating P, R, AP, MAP in val.py to adapt to detect.py, what code need to be packed? HOT 2
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from yolov5.