SOURCE_PATH ="/dataset/ImageNet-1k" TARGET_SIZE =f"/dataset/ImageNet-{SUBSET_SIZE}_seed{seed}" # Data loading code train_dataset = datasets.ImageNet(SOURCE_PATH, split="train") classes = train_dataset.wnids # Randomly select a subset of classes np.random.seed(seed) subset_classes = n...
We evaluate our method on the ImageNet 2012 classification dataset that consists of 1000 classes. The models are trained on the 1.28 million training images, and evaluated on the 50k validation images. We also obtain a final result on the 100k test images, reported by the test server. 解读:...
ImageNet-1000.zip 是 ImageNet-1k验证集(val.zip)制作的,训练集和数据集都包含1000个分类,图片按8:2分割,这样就不存在不均衡数据。 ImageNet-100.zip 与 ImageNet-1000.zip差不多,不过只取了前100个分类,训练速度要快很多。 展开 文件列表 focalnet_base_srf.pdparams focalnet_small_srf.pdparams focalnet...
dataset = datasets.CIFAR100(args.data_path, train=is_train, transform=transform) nb_classes = 100 elif args.data_set == 'IMNET': root = os.path.join(args.data_path, 'train' if is_train else 'val') dataset = datasets.ImageFolder(root, transform=transform) nb_classes = 1000 elif args...
Training the full imagenet dataset (1k classes) needs a high computational resource, it is usually hard to quickly check your model on your local or personal computer. The mini-imagenet (100 classes) and tiny-imagenet (200 classes) are way more friendly on a local or personal computer, bu...
OpenImage-O is built for the ID dataset ImageNet-1k. It is manually annotated, comes with a naturally diverse distribution, and has a large scale. It is built to overcome several shortcomings of existing OOD benchmarks. OpenImage-O is image-by-image filtered from the test set of OpenImag...
For reference, we report the coarse dataset for a max depth of d=4 and the original ImageNet21K (GT).Information Accuracy (%) # samples # classes Probing k-means TEMI SCANv2 ImageNet21K subset benchmarks WordNet leaf 7.54M 7353 56.92 27.43 28.08 24.56 ImageNet21K leaf 8.37M 8153 ...
Dataset 作者使用了电力传输与配电基础设施图像(ETDII)数据集进行实验,这是一个来自杜克大学的公开数据集。该数据集的来源包括CT ECO、USGS、LINZ和SpaceNet等不同提供者。它由494个图像块组成,来自六个国家,分别是美国、苏丹、新西兰、墨西...
Most of the FasterViT models were trained on ImageNet1K dataset. FasterViT-Large was trained on ImageNet22k dataset Performance Evaluation Data The FasterViT models have been evaluated on the ImageNet1K validation dataset. Methodology and KPI The key performance indicator is accuracy, following ...
在ImageNet-1K数据集上自监督预训练BEIT,其中包含大约1.2M张图像。数据增强策略包括随机调整大小的裁剪,水平翻转,颜色抖动。输入图像尺寸为 224 × 224 ,输入被分割为 14 × 14 的images patches和相同数量的visual token。随机掩码最多75个patch(总图像补丁的40%)。 预训练运行大约500k step(即800 epoch),batch...