#image-processing

Implement computer vision solutions using deep learning for image classification, object detection, and visual analysis. Image preprocessing: 1. Data augmentation: rotation (±15°), horizontal flip, zoom (0.8-1.2x), brightness adjustment. 2. Normalization: pixel values [0,1], ImageNet normalization (mean=[0.485,0.456,0.406), std=[0.229,0.224,0.225]). 3. Resizing strategies: maintain aspect ratio, center cropping, padding to target size. Classification architectures: 1. ResNet: skip connections, deeper networks (50-152 layers), batch normalization. 2. EfficientNet: compound scaling, mobile-optimized, state-of-the-art accuracy/efficiency trade-off. 3. Vision Transformer (ViT): attention-based, patch embedding, competitive with CNNs. Object detection: 1. YOLO (You Only Look Once): real-time detection, single-stage detector, anchor boxes. 2. R-CNN family: two-stage detection, region proposals, high accuracy applications. 3. SSD (Single Shot Detector): multi-scale feature maps, speed/accuracy balance. Semantic segmentation: 1. U-Net: encoder-decoder, skip connections, medical imaging applications. 2. DeepLab: atrous convolution, conditional random fields, accurate boundary detection. Transfer learning: 1. ImageNet pre-training: feature extraction (freeze early layers), fine-tuning (unfreeze gradually). 2. Domain adaptation: medical images, satellite imagery, artistic style transfer. Evaluation metrics: top-1 accuracy (>90% excellent), mAP for detection (>0.5), IoU for segmentation (>0.7), inference time (<50ms for real-time applications).