CIFAR-100

노트

위키데이터

• ID : Q45038012

말뭉치

1. The 100 classes in the CIFAR-100 are roughly grouped into 20 superclasses.
2. layer { name: "cifar100" type: "Data" top: "data" top: "label" include { phase: TRAIN } transform_param { mean_file: "../mean.binaryproto" } data_param { source: "..
3. The 100 classes in the CIFAR-100 are grouped into 20 superclasses.
4. There are the following classes in the CIFAR-100 dataset: S. No Superclass Classes 1.
5. The 100 classes in the CIFAR-100 are grouped into 20 super-classes.
6. CIFAR-100 data set is just like the CIFAR-10, except it has 100 classes containing 600 images each.
7. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters.
8. Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data).
9. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet).
10. Our RoR-3-WRN58-4+SD models achieve new state-of-the-art results on CIFAR-10, CIFAR-100 and SVHN, with test errors 3.77%, 19.73% and 1.59%, respectively.
11. The CIFAR-100 dataset contains 50,000 training and 10,000 test images of 20 object classes, along with 100 object subclasses.
12. Then, we’ll actually build one – by using the CIFAR-10 and CIFAR-100 datasets.
13. They come in two ways: the CIFAR-10 datasets, with ten classes, and the CIFAR-100 dataset, with one hundred classes.
14. Now, let’s load some CIFAR-100 data.
15. Instead of cifar10 , you’ll import cifar100 .

메타데이터

위키데이터

• ID : Q45038012

Spacy 패턴 목록

• [{'LEMMA': 'CIFAR-100'}]
• [{'LEMMA': 'CIFAR100'}]