What is Image Classification

How It Works

Image classification models take an image as input and output a probability distribution over predefined classes. The image passes through a feature extraction backbone (CNN or Vision Transformer) that produces a representation vector, which is then mapped to class probabilities via a classification head. The class with the highest probability is selected as the prediction.

Technical Details

Modern classifiers use Vision Transformers (ViT, DeiT) or efficient ConvNets (EfficientNet, ConvNeXt) pretrained on ImageNet-21K or larger datasets. Transfer learning through fine-tuning the classifier head or the full model on domain data is standard practice. Multi-label classification uses sigmoid outputs instead of softmax for images belonging to multiple categories. Top-1 and top-5 accuracy are standard evaluation metrics.

Best Practices

• Start with a pretrained model and fine-tune on your domain data rather than training from scratch
• Use progressive resizing during training to improve both speed and accuracy
• Implement data augmentation strategies like MixUp and CutMix for better generalization
• Use multi-label classification when images naturally belong to multiple categories

Common Pitfalls

• Training on imbalanced datasets without applying class weighting or resampling
• Using too many fine-grained classes when coarser categories would serve the application better
• Not validating on data that reflects the actual production distribution
• Ignoring prediction confidence, leading to overconfident misclassifications

Advanced Tips

• Use CLIP-based zero-shot classification to handle classes not present during training
• Implement hierarchical classification for taxonomies with parent-child category relationships
• Apply knowledge distillation to compress large classifiers for edge deployment
• Use classification confidence scores as metadata filters in multimodal search pipelines