#object-detection

Implement real-time detection with YOLO. Setup: 1. Choose YOLO version (v8, v9, v10). 2. Pre-trained COCO weights. 3. Inference on images/video. 4. Bounding box detection. 5. Class confidence scores. 6. Non-max suppression. 7. Custom dataset training. 8. Export to ONNX for deployment. Use Ultralytics library and implement tracking for video streams.

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).