图像分类,顾名思义,是一个输入图像,输出对该图像内容分类的描述的问题。它是计算机视觉的核心,实际应用广泛。

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准确的图像和视频分类对于广泛的计算机视觉应用非常重要,从识别有害内容,到使视障人士更容易地获得产品,再到帮助人们更容易地在市场等产品上买卖东西。Facebook AI正在开发替代方法来训练我们的人工智能系统,这样我们就可以用更少的标记训练数据来做更多的事情,而且即使在无法获得大量高质量的标记数据集的情况下,也能提供准确的结果。今天,我们分享一个多功能的新模型训练技术的细节,为图像和视频分类系统提供最先进的准确性。

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ImageNet has been arguably the most popular image classification benchmark, but it is also the one with a significant level of label noise. Recent studies have shown that many samples contain multiple classes, despite being assumed to be a single-label benchmark. They have thus proposed to turn ImageNet evaluation into a multi-label task, with exhaustive multi-label annotations per image. However, they have not fixed the training set, presumably because of a formidable annotation cost. We argue that the mismatch between single-label annotations and effectively multi-label images is equally, if not more, problematic in the training setup, where random crops are applied. With the single-label annotations, a random crop of an image may contain an entirely different object from the ground truth, introducing noisy or even incorrect supervision during training. We thus re-label the ImageNet training set with multi-labels. We address the annotation cost barrier by letting a strong image classifier, trained on an extra source of data, generate the multi-labels. We utilize the pixel-wise multi-label predictions before the final pooling layer, in order to exploit the additional location-specific supervision signals. Training on the re-labeled samples results in improved model performances across the board. ResNet-50 attains the top-1 classification accuracy of 78.9% on ImageNet with our localized multi-labels, which can be further boosted to 80.2% with the CutMix regularization. We show that the models trained with localized multi-labels also outperforms the baselines on transfer learning to object detection and instance segmentation tasks, and various robustness benchmarks. The re-labeled ImageNet training set, pre-trained weights, and the source code are available at {https://github.com/naver-ai/relabel_imagenet}.

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