قارن الطرق

	الشبكة العصبونية الالتفافية المُحسَّنة بدقة	اكتشاف الكائنات
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2012–2014	2014–2016
صاحب الطريقة	Yosinski, J. et al. (theoretical basis); practice widespread from Krizhevsky et al. 2012 onward	Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO)
النوع	Transfer learning technique (supervised fine-tuning)	Supervised deep learning (region proposal or single-shot)
المصدر التأسيسي	Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems, 27.	Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 580–587.
الأسماء البديلة	Fine-tuned CNN, CNN fine-tuning, CNN transfer learning with fine-tuning, adapted convolutional network	visual object detection, image object localization, region-based object detection, bounding-box detection
ذات صلة	5	3
الملخص	Fine-tuning a CNN means starting from a network already trained on a large dataset — typically ImageNet — and continuing training on a smaller target dataset so the model adapts its learned visual features to a new task. This approach dramatically reduces the data and compute required to reach strong performance compared with training from scratch.	Object detection is a computer vision task in which a deep neural network simultaneously locates and classifies every instance of one or more object categories within an image, producing a bounding box and a class label for each detected object. Modern detectors — from the R-CNN family to YOLO and DETR — achieve near-human accuracy at real-time speeds on standard benchmarks.