Is 458 pictures enough to train the apple banana recognition model?

Analysis of sample size of deep learning model training: case study on apple and banana identification

This article discusses the sample size required to train a deep learning model that distinguishes apples from bananas. The user used the ResNet50 model to collect 195 banana pictures and 263 apple pictures (458 in total), but the model recognition effect was extremely poor, and all pictures were identified as bananas. This raises the question of whether the sample size is insufficient.

458 images may not be enough for training a deep learning model with huge parameters like ResNet50. Although ResNet50 has strong pre-training capabilities, its advantages require a lot of data to fully utilize. Even with data augmentation, 458 images may not be enough for the model to learn the nuances between apples and bananas, resulting in overfitting the model, performing well on the training set but extremely poor on the test set.

A viable alternative is to extract image features using a pre-trained VGG16 model and then train using a three-layer multi-layer perceptron (MLP). This method reduces model complexity and reduces the need for the number of training samples. VGG16 has learned a wealth of image features, thus simplifying the classification task and reducing the sample size requirement, and hundreds of images may be enough. This shows that choosing the right model architecture is crucial for training small datasets, and lightweight models are more suitable.

However, sample quality remains critical regardless of the model architecture. Poor picture quality, uneven light, inconsistent angles, etc. will affect the learning effect of the model. Therefore, high-quality and diverse training data remains the key to training successful models.

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