ultralytics / yolov5

YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
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Why do i get redundant overlapping labels for a single object ? #9082

Closed Hisan-007 closed 2 years ago

Hisan-007 commented 2 years ago

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Question

Problem:

I have trained yolov5l6 on my dataset of 3000 images with 5 classes for 100 epochs with batch size of 16. While the precision and recall are both 0.9+ on the test set, the model draws two bounding boxes around a single object sometimes and this changes the object count (which is a sensitive and important output).

What I'm looking for ?

I would like to ask why does this occurs and what leads to these situations ? I also want to know what steps can I take to avoid this ? Can this be altered by tuning hyper-parameters ? If so please guide me.

Attachments:

A figure showing redundant labels marked with red arrow issue1

Additional

No response

github-actions[bot] commented 2 years ago

👋 Hello @HissaanAli, 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.

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Requirements

Python>=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

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Hisan-007 commented 2 years ago

@glenn-jocher could you please share the expert advice .

glenn-jocher commented 2 years ago

@HissaanAli 👋 Hello! Thanks for asking about improving YOLOv5 🚀 training results.

Most of the time good results can be obtained with no changes to the models or training settings, provided your dataset is sufficiently large and well labelled. If at first you don't get good results, there are steps you might be able to take to improve, but we always recommend users first train with all default settings before considering any changes. This helps establish a performance baseline and spot areas for improvement.

If you have questions about your training results we recommend you provide the maximum amount of information possible if you expect a helpful response, including results plots (train losses, val losses, P, R, mAP), PR curve, confusion matrix, training mosaics, test results and dataset statistics images such as labels.png. All of these are located in your project/name directory, typically yolov5/runs/train/exp.

We've put together a full guide for users looking to get the best results on their YOLOv5 trainings below.

Dataset

COCO Analysis

Model Selection

Larger models like YOLOv5x and YOLOv5x6 will produce better results in nearly all cases, but have more parameters, require more CUDA memory to train, and are slower to run. For mobile deployments we recommend YOLOv5s/m, for cloud deployments we recommend YOLOv5l/x. See our README table for a full comparison of all models.

YOLOv5 Models

Training Settings

Before modifying anything, first train with default settings to establish a performance baseline. A full list of train.py settings can be found in the train.py argparser.

Further Reading

If you'd like to know more a good place to start is Karpathy's 'Recipe for Training Neural Networks', which has great ideas for training that apply broadly across all ML domains: http://karpathy.github.io/2019/04/25/recipe/

Good luck 🍀 and let us know if you have any other questions!

github-actions[bot] commented 2 years ago

👋 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.

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