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Interpreting training results and showing loss stats/graph #1468
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Hello @naveedri, 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://www.ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com. RequirementsPython 3.8 or later with all requirements.txt dependencies installed, including $ pip install -r requirements.txt EnvironmentsYOLOv5 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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GIoU term is deprecated: #1120 Follow custom training tutorial: |
I have already followed the custom training tutorial, but I still don't know what each graph in the results.png says. Is there a loss graph? How should I measure the results? |
@naveedri oh, for the metrics I would recommend the original YOLOv3 paper to brush up on the basics: and wikipedia for metrics: |
Thank you so much!
Here are the results I got on this (again, single image, 200 epochs |
You require a valid dataset for valid results. |
This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions. |
I'm new to yolov5 and I'm trying to build a single class object detector(detecting mouth in an image).
![results](https://user-images.githubusercontent.com/74835824/99886088-67733800-2c42-11eb-9bff-9e909ab59842.png)
Here are the results I got:
I'm pretty sure that my dataset is problematic and thus the training wasn't successful, but putting this aside, I have two questions:
PS - I have also tried running train.py on a dataset that includes a single image of my mouth labeled - the same image is used for training, validation, and testing. I expected the model to be able to learn to label this image correctly as it is the only data it gets, but when it comes to testing or validation - the model isn't able to detect anything above 0.05 confident rate after 200 epochs - and even then it draws random boxes and doesn't detect the mouth correctly. Is it the expected result, or does it indicates that something is wrong in my setup?
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