Objectness loss, class loss and bounding box losses in YOLOv5

[Shanky71]

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Question

Hey @glenn-jocher I hope you are doing well. I have a small doubt about the objectness loss.

• I have divided my data in 80% training, 15% testing and 5% validation. As shown in the below image, what can be the possible reason of the sudden spike in the initial part and also, why there is higher objectness loss during validation than training.

• Also out of the three losses (class, objectness, box) which loss is more important. If I consider only class loss doesn't it also account for objectness and box loss?

@pderrenger @UltralyticsAssistant Can you please clarify it?

Additional

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[UltralyticsAssistant]

👋 Hello @Shanky71, thank you for reaching out with your question about YOLOv5 🚀! This is an automated response to help guide you, and an Ultralytics engineer will also assist soon.

For questions related to loss functions and their behavior during training and validation, providing a bit more context can be extremely helpful. If you have logs, specific configurations, or additional screenshots from your training, please share them to aid in diagnosing the issue.

If this is a 🐛 Bug Report, please ensure you provide a minimum reproducible example so we can help you debug it accurately.

If you have questions about training specific to your dataset, please include as much information as possible, such as examples of your dataset images and any error logs.

Here are a few things to check:

• Have you ensured that your dataset and labels are formatted correctly?
• Are you following the tips for the best training results? Sometimes tweaks in hyperparameters or batch sizes can affect loss curves significantly.
• Is your validation dataset distinct and representative of your training data?

Remember, the importance of each type of loss can depend on your specific use case. Generally, all three losses—class, objectness, and box—play crucial roles in effective model training.

Feel free to provide more details, and we'll do our best to assist you further! 😊

[pderrenger]

@Shanky71 the initial spike in objectness loss during validation could be due to the model adjusting to the dataset's characteristics early in training. It’s common to see higher losses in validation compared to training if there’s overfitting or variance in the validation set. Regarding importance, all three losses (class, objectness, box) are crucial as they each contribute to different aspects of learning: classifying objects, predicting their presence, and accurately locating them. Focusing solely on class loss would neglect the spatial precision and presence confidence provided by the other losses. For further insights, you may refer to the YOLOv5 Loss Function documentation for a detailed explanation.

[Shanky71]

Hey @pderrenger and @jkocherhans @timstokman @glenn-jocher ,I have a question related to trade-off between image resolution and performance . As observed from the below content that, inference time is decreasing with increase in resolution, which is counterintuitive. Can u help me with this. Is there any documentation regarding the same? Thanks in advance in yolov5 object detection: Speed:0.3 ms preprocess, 9.1 ms inference, 1.8 NMS per image at shape (1, 3, 640, 640) Speed:0.4 ms preprocess, 8.3 ms inference, 1.1 NMS per image at shape (1, 3, 1024, 1024) Speed:0.5 ms preprocess, 8.0 ms inference, 1.4 NMS per image at shape (1, 3, 2048, 2048)

[pderrenger]

@Shanky71 That's an interesting observation! This counterintuitive behavior can occur due to several factors: 1) Better GPU utilization with larger tensors allowing more efficient parallelization, 2) Certain hardware accelerators having optimized performance at specific dimensions, or 3) Processing overhead becoming proportionally smaller as image size increases.

While inference time might decrease, note that memory usage increases significantly with higher resolutions. There's usually a sweet spot that balances detection accuracy and resource usage. This behavior is hardware-dependent - your specific GPU architecture likely handles the larger tensors efficiently in this case. Our GitHub wiki contains information on performance benchmarks across different image sizes that may provide additional context.

[Shanky71]

@pderrenger Thanks for the help :)

[pderrenger]

You're welcome @Shanky71! Feel free to reach out if you have any other questions about YOLOv5 or need further clarification on these topics.

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