Utilizing pre-trained models for object detection involves transferring the feature extraction part of models trained on large-scale datasets (such as ResNet, Inception, etc.) to new object detection tasks, improving detection performance through fine-tuning.
Image classification: transferring the feature extraction part of models trained on large-scale image datasets (such as VGG, MobileNet, etc.) to new image classification tasks, achieving high accuracy through fine-tuning.
Object recognition: Transfer the feature extraction part of models trained on large-scale datasets (such as YOLO, SSD, etc.) to new object recognition tasks, and improve recognition accuracy through fine-tuning.
Facial recognition: Transfer the feature extraction part of the model trained on a large-scale facial dataset to a new facial recognition task, achieving high accuracy through fine-tuning.
Image style transfer: transferring the feature extraction part of a model trained on a large-scale dataset to a new image style transfer task, achieving better image style transfer results through fine-tuning.
