Hello everyone, I am working on a fruit classification problem using EfficientNet-B0, and I would appreciate some advice from anyone experienced with domain generalization / cross-dataset evaluation. I have a custom fruit dataset collected under real-world conditions, with different backgrounds, lighting conditions, object sizes, and image acquisition environments. I am training EfficientNet-B0 on my custom dataset and then evaluating the trained model on the Fruit-360 dataset, which has a substantially different visual domain. My main goal is to improve the model's generalization to the unseen Fruit-360 domain, rather than simply maximizing accuracy on the training-domain test set. I would like to ask: What preprocessing or normalization techniques would you recommend before training and cross-dataset evaluation? Should the same preprocessing pipeline be applied to both the custom dataset and Fruit-360? Would techniques such as stronger data augmentation, color normalization, background augmentation, MixStyle, domain randomization, or feature normalization be useful for this problem? Would you recommend any modifications to EfficientNet-B0, such as attention mechanisms, domain-generalization modules, feature alignment, or other architectural changes? Is there a recommended training strategy for improving cross-domain generalization? Are there any MATLAB examples, papers, or implementations that you would recommend for this type of experiment? The experimental setup is: Source domain: My custom real-world fruit dataset Target domain: Fruit-360 Model: EfficientNet-B0 Training: Source/custom dataset only Evaluation: Target/Fruit-360 dataset without using its images for training I would especially appreciate suggestions from anyone who has experience with domain generalization, domain shift, or cross-dataset fruit classification. Thank you in advance for any advice or references.

Domain Generalization for Fruit Classification Using EfficientNet-B0

Batool

2026 9 3

0 个回答

79 次查看（30 天）

显示更早的评论

0 个投票

Hello everyone,

I am working on a fruit classification problem using EfficientNet-B0, and I would appreciate some advice from anyone experienced with domain generalization / cross-dataset evaluation.

I have a custom fruit dataset collected under real-world conditions, with different backgrounds, lighting conditions, object sizes, and image acquisition environments. I am training EfficientNet-B0 on my custom dataset and then evaluating the trained model on the Fruit-360 dataset, which has a substantially different visual domain.

My main goal is to improve the model's generalization to the unseen Fruit-360 domain, rather than simply maximizing accuracy on the training-domain test set.

I would like to ask:

What preprocessing or normalization techniques would you recommend before training and cross-dataset evaluation?
Should the same preprocessing pipeline be applied to both the custom dataset and Fruit-360?
Would techniques such as stronger data augmentation, color normalization, background augmentation, MixStyle, domain randomization, or feature normalization be useful for this problem?
Would you recommend any modifications to EfficientNet-B0, such as attention mechanisms, domain-generalization modules, feature alignment, or other architectural changes?
Is there a recommended training strategy for improving cross-domain generalization?
Are there any MATLAB examples, papers, or implementations that you would recommend for this type of experiment?

The experimental setup is:

Source domain: My custom real-world fruit dataset

Target domain: Fruit-360

Model: EfficientNet-B0

Training: Source/custom dataset only

Evaluation: Target/Fruit-360 dataset without using its images for training

I would especially appreciate suggestions from anyone who has experience with domain generalization, domain shift, or cross-dataset fruit classification.

Thank you in advance for any advice or references.