16 Deep learning for classification, detection, segmentation and anomaly
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16.1 Image classification
16.1.1 Whole-image classification pipelines
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16.1.2 Multi-label classification
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16.1.3 Fine-grained classification
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16.1.4 Evaluation with imbalanced industrial datasets
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16.2 Object detection
16.2.1 Two-stage detectors: R-CNN, Fast R-CNN, Faster R-CNN
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16.2.2 One-stage detectors: YOLO family, SSD, RetinaNet
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16.2.3 Anchor-free detectors: FCOS, CenterNet
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16.2.4 Transformer-based detection: DETR
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16.2.5 Evaluation: IoU, mAP, per-class analysis
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16.3 Semantic and instance segmentation
16.3.1 Fully convolutional networks
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16.3.2 U-Net and encoder–decoder architectures
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16.3.3 DeepLab and dilated convolutions
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16.3.4 Mask R-CNN for instance segmentation
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16.3.5 Panoptic segmentation
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16.4 Anomaly and defect detection
16.4.1 Reconstruction-based methods: autoencoders and GANs
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16.4.2 Embedding-based methods: PatchCore, PaDiM, SPADE
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16.4.3 One-class classifiers and Deep SVDD
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16.4.4 Benchmarks: MVTec AD and industrial extensions
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16.4.5 Choosing between anomaly detection and supervised approaches
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