I’m Using a CNN for Image Classification, but It Misclassifies Similar Objects—What Can I Do to Improve Accuracy?

[RubyDemon3131]

I’m training a convolutional neural network (CNN) for image classification, but it often misclassifies objects that have similar visual features (e.g., cats vs. foxes, trucks vs. buses). The model reaches ~85% accuracy, but confusion between specific classes remains high. I’ve tried increasing the dataset size slightly and using standard augmentations. Should I consider techniques like contrastive learning or fine-tuning a pretrained model like ResNet50? What approaches can help the model better distinguish between visually similar classes?

[legendy4141]

To improve class separation for visually similar objects, consider using a pretrained model like ResNet50 or EfficientNet and fine-tuning it on your dataset, as these models have strong feature extraction capabilities. Additionally, implementing contrastive learning (e.g., SimCLR or Triplet Loss) can help the model learn more discriminative representations by emphasizing differences between similar classes. Increasing intra-class variability through targeted augmentations (e.g., background changes, perspective shifts) also helps. Analyzing confusion matrices and applying class-specific data balancing or focal loss can further reduce misclassifications in critical categories.