ultralytics / ultralytics

Ultralytics YOLO11 🚀
https://docs.ultralytics.com
GNU Affero General Public License v3.0
36.58k stars 7.05k forks source link

ablation experiments #18958

Open Ellohiye opened 2 weeks ago

Ellohiye commented 2 weeks ago

Search before asking

Question

Hello author, I would like to ask you a question about ablation experiments. When designing an ablation experiment, if module A and module B are added to the model, do we need to ensure that the original model, original model + A, and original model + B have the same experimental parameters? If the parameters must be consistent, wouldn't the innovation work be a simple superposition?

Additional

No response

UltralyticsAssistant commented 2 weeks ago

👋 Hello @Ellohiye, thank you for your interest in Ultralytics 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.

If this is a ❓ General Question, we kindly ask you to provide more context or examples if possible to ensure we understand your question fully and provide a more accurate response. If your query relates to custom configurations or experimental setups, details like the model structure and specific use cases can be incredibly helpful. Additionally, for ablation studies, referencing related Model Training Tips may be beneficial for setting up consistent parameters and analyzing outcomes effectively.

Join the Ultralytics community where it suits you best. For real-time chat, head to Discord 🎧. Prefer in-depth discussions? Check out Discourse. Or dive into threads on our Subreddit to exchange knowledge with the community.

Upgrade

Ensure you are using the latest ultralytics package version, as updates may include relevant features or fixes:

pip install -U ultralytics

Environments

YOLO may be run in any of the following up-to-date, verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

Status

Ultralytics CI

If this badge is green, all Ultralytics CI tests are currently passing. CI tests validate correct operation of all YOLO Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

This is an automated response to help point you in the right direction. An Ultralytics engineer will respond to your question as soon as possible! 😊

Y-T-G commented 2 weeks ago

If they aren't consistent, how do you say that the improvement is due to your module?

Ellohiye commented 2 weeks ago

So if it is a self-made data set, does the innovation only lie in the design of the modules and the simple superposition of multiple modules?

Ellohiye commented 2 weeks ago

So why is there so much parameter adjustment work?

glenn-jocher commented 2 weeks ago

Parameter adjustment is essential because each dataset and task has unique characteristics, and optimal hyperparameters (learning rate, augmentation, etc.) directly impact how effectively your model architecture—including any new modules—can learn from the data. While module design is a core innovation, parameters fine-tune how these components interact and adapt to your specific data. For guidance on balancing architectural changes with parameter optimization, see our hyperparameter tuning guide.

Ellohiye commented 2 weeks ago

If I add a module A to the YOLOv8 architecture and design an ablation experiment for baseline and baseline+A, and keep the hyperparameters of the two experiments consistent, how do I adjust the hyperparameters?

glenn-jocher commented 2 weeks ago

For ablation studies comparing baseline vs. baseline+A, maintain identical hyperparameters to isolate the module's impact. If architectural changes (e.g., parameter count) significantly affect convergence, consider minor learning rate adjustments while documenting this as part of your module's behavior. For general hyperparameter optimization, see our Hyperparameter Tuning Guide.

Ellohiye commented 2 weeks ago

If I adjust the parameters, won’t it be inconsistent with the baseline parameters? How should I compare?

glenn-jocher commented 2 weeks ago

For valid ablation comparisons between baseline and modified models, maintain identical hyperparameters where possible. If architectural changes (e.g., added parameters) necessitate adjustments (like learning rate), document this clearly and ensure fair comparisons by using controlled experiments. For detailed guidance, see our Hyperparameter Tuning Guide.

Ellohiye commented 2 weeks ago

Thank you for your patient reply, but I am still not sure how to ensure the fairness of the experiment. For example, should the baseline and baseline+A be compared with the same hyperparameters? After adjusting multiple parameters of baseline+A, should the effect be compared with the effect after adjusting the same parameters of baseline?

glenn-jocher commented 1 week ago

For valid ablation studies comparing baseline vs. baseline+A, maintain identical hyperparameters where possible to isolate module impact. If architectural changes (e.g., added parameters) necessitate adjustments (like learning rate), apply the same tuned parameters to both models for fair comparison. See our Hyperparameter Tuning Guide for best practices.