论文标题
使用深度学习的相同图像检索
Identical Image Retrieval using Deep Learning
论文作者
论文摘要
近年来，我们知道与图像的互动增加了。图像相似性涉及从给定参考图像遵守的相似图像。目标是找出搜索的图像是否会导致相似的图片。我们正在使用BigTransfer模型，该模型本身就是最先进的模型。 BigTransfer（位）本质上是重新连接，但在较大的数据集（例如ImageNet和Imagenet-21K）上进行了预先训练，并进行了其他修改。使用微型训练的预训练的卷积神经网络模型，我们提取关键特征并在K-Neartime邻居模型上训练以获得最近的邻居。我们的模型的应用是找到相似的图像，这些图像很难通过低推理时间内的文本查询来实现。我们基于此应用程序分析了模型的基准。
In recent years, we know that the interaction with images has increased. Image similarity involves fetching similar-looking images abiding by a given reference image. The target is to find out whether the image searched as a query can result in similar pictures. We are using the BigTransfer Model, which is a state-of-art model itself. BigTransfer(BiT) is essentially a ResNet but pre-trained on a larger dataset like ImageNet and ImageNet-21k with additional modifications. Using the fine-tuned pre-trained Convolution Neural Network Model, we extract the key features and train on the K-Nearest Neighbor model to obtain the nearest neighbor. The application of our model is to find similar images, which are hard to achieve through text queries within a low inference time. We analyse the benchmark of our model based on this application.
