Same yolov5s training, but one over-fitting and one training is very good. about yolov5 HOT 2 OPEN

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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  # 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):

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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

from yolov5.

@mimimind hello there! 😊 It sounds a bit puzzling that you're experiencing different training outcomes with the same setup. A few things to check that might explain the variance:

1. Data Split: Ensure the training-validation split is consistent between runs. Inadvertent changes can cause discrepancies in training results.
2. Environment: Verify that the software environment (PyTorch version, CUDA version, etc.) hasn't changed. Differences here can affect model performance.
3. Random Seeds: YOLOv5 training involves randomness (e.g., data shuffling). Setting a fixed seed can help ensure consistency across training sessions.
4. Updates in YOLOv5 Repo: Even if your setup hasn't changed, updates to the YOLOv5 repository might have occurred. Ensure you're training with the same commit/version of YOLOv5 for both runs.

Here's a tiny bit of code to fix the seed, just in case:

import torch
torch.manual_seed(42)  # Use a consistent seed

If after checking these factors the issue still persists, the detailed logs of both training runs might offer some clues. Comparing them might reveal subtle differences not immediately apparent.

Hope this helps point you in the right direction!

from yolov5.