cv-detection
Object Detection Best Practice
Architecture families:
- One-stage: YOLO (v5/v8), SSD, RetinaNet, FCOS
- Two-stage: Faster R-CNN, Cascade R-CNN
- Transformer: DETR, DINO, RT-DETR
Training recipe:
- Use pre-trained backbone (ImageNet)
- Multi-scale training and testing
- IoU threshold: 0.5 for mAP50, 0.5:0.95 for mAP
- Use FPN for multi-scale feature extraction
- Focal loss for class imbalance in one-stage detectors
Standard benchmarks:
- COCO val2017: ~37 mAP (Faster R-CNN R50), ~51 mAP (DINO Swin-L)
- Pascal VOC: ~80 mAP50 (Faster R-CNN)
More from aiming-lab/autoresearchclaw
scientific-writing
Academic manuscript writing with IMRAD structure, citation formatting, and reporting guidelines. Use when drafting or revising research papers.
10hypothesis-formulation
Structured scientific hypothesis generation from observations. Use when formulating testable hypotheses, competing explanations, or experimental predictions.
9scientific-visualization
Publication-ready scientific figure design with matplotlib and seaborn. Use when creating journal submission figures with proper formatting, accessibility, and statistical annotations.
9literature-search
Systematic literature review methodology including search strategy, screening, and synthesis. Use when conducting literature reviews or writing background sections.
9statistical-reporting
Statistical test selection, assumption checking, and APA-formatted reporting. Use when analyzing experimental results or writing results sections.
9a-evolve
>
8