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.
翻译:近些年来,我们知道与图像的交互作用已经增加。图像相似性涉及获取符合某个参考图像的类似图像。 目标是要找出作为查询搜索的图像是否会产生相似的图片。 我们正在使用“ 大 Transfer 模型”, 这是一种最先进的模型。 大 Transfer( Bit) 本质上是一个 ResNet, 但是在图像网和图像网- 21k 等更大的数据集上预先培训, 并进行进一步的修改。 我们使用经过精细调整的预先训练的 Convolution Neal网络模型, 提取关键特征, 并用 K- Nearest 邻里伯模型培训获取最近的邻居。 我们模型的应用是找到类似的图像, 很难在低速的时间内通过文字查询实现。 我们根据此应用程序分析了模型的基准 。