SlowGuess/NeurIPS_2023_OCR huggingface dataset:
ImageNet
Figure 10: Image samples from ImageNet and its out-of-distribution variants. From left to right: we show images of the puffer category in the ImageNet, ImageNet-C, ImageNet-A, ImageNet-R, ImageNetV2, and Stylized ImageNet dataset. ImageNet-C applies different corruptions to images. ImageNet-A consists of real-world image examples that are misclassified by existing classifiers. ImageNet-R renders images in artistic styles, e.g. cartoon, sketch, graphic, etc. ImageNetV2 attempts to collect images from the same distribution as ImageNet, but still suffers from minor distribution shift. ImageNet-S transforms ImageNet images into different art styles, while preserving the global contents of original images. Though these images all correspond to the same category, they are visually dissimilar and can easily confuse ImageNet-trained classifiers.
ImageNet-C
ImageNet-A
ImageNet-R
ImageNetV2
ImageNet-S
We consider ImageNet and its out-of-distribution variants for test-time adaptation. 1. ImageNet [9] is a large-scale generic object classification dataset, featuring 1000 object categories. We test on the validation set that consists of 50 images for each category. 2. ImageNet-C [19] applies various visual corruptions to the same validation set as ImageNet. We consider the shift due to gaussian noise (level-5) in our single-sample experiments. We consider 5 types of shift (gaussian noise, fog, pixelate, contrast, and snow) in our online adaptation experiments. 3. ImageNet-A[21] consists of real-world image examples that are misclassified by existing classifiers. The dataset converts 200 categories in ImageNet and 7450 images for testing. 4. ImageNet-R [20] is a synthetic dataset that renders 200 ImageNet classes in art, cartoons, deviantart, graffiti, embroidery, graphics, origami, paintings, patterns, plastic objects, plush objects, sculptures, sketches, tattoos, toys, and video game styles. The dataset consists of  $30\mathrm{K}$  test images. 5. ImageNetV2 collects images from the same distribution as ImageNet, composed of 1000 categories, each with 10 test images. 6. ImageNet-S transforms ImageNet images into different artistic styles, while preserving the global contents of original images. For ImageNet and its C, R, V2, and S variants except A, we sample 3 images per category, as these datasets contain a lot more classes than the other datasets. For ImageNet-A we evaluate on it's whole test-set.