Comparer des méthodes

	Réseau neuronal convolutif affiné	Détection d'objets
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2012–2014	2014–2016
Auteur d'origine	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)
Type	Transfer learning technique (supervised fine-tuning)	Supervised deep learning (region proposal or single-shot)
Source fondatrice	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.
Alias	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
Apparentées	5	3
Résumé	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.