Post
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CIFAR-10 your handing image dataset ...
CIFAR-10 is a small, standard computer-vision dataset used to quickly test and compare ideas.
- 60,000 color images, each 32×32 pixels, labeled into 10 classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck.
- Label mapping (important):
- 0 airplane
- 1 automobile
- 2 bird
- 3 cat
- 4 deer
- 5 dog
- 6 frog
- 7 horse
- 8 ship
- 9 truck
- Split: 50,000 train and 10,000 test.
- Why people use it: fast benchmarking for image classifiers (small CNNs, ResNet, ViT), and quick experiments for training pipelines, augmentation, regularization, pruning, distillation, and demos.
- Sizes (downloads): Python version about 163 MB, binary about 162 MB. Hugging Face shows about 144 MB for the dataset files.
- Where to get it: the official CIFAR page (University of Toronto) and the Hugging Face CIFAR-10 dataset page.
uoft-cs/cifar10
If you want something more, check the table below
| Dataset | Resolution | Classes | Best For |
| ImageNet 1K | 224–256×256 | 1000 | Real-world large-scale classification |
| ImageNet-256. | 256×256 | 1000 | Direct high-res training |
| TinyImageNet | 64×64 | 200 | Mid-range benchmark |
| UC Merced Land Use | 256×256 | ~21 | Higher resolution small classification |
| MS COCO | >256×256 | ~80 objects | Detection / segmentation |
CIFAR-10 is a small, standard computer-vision dataset used to quickly test and compare ideas.
- 60,000 color images, each 32×32 pixels, labeled into 10 classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck.
- Label mapping (important):
- 0 airplane
- 1 automobile
- 2 bird
- 3 cat
- 4 deer
- 5 dog
- 6 frog
- 7 horse
- 8 ship
- 9 truck
- Split: 50,000 train and 10,000 test.
- Why people use it: fast benchmarking for image classifiers (small CNNs, ResNet, ViT), and quick experiments for training pipelines, augmentation, regularization, pruning, distillation, and demos.
- Sizes (downloads): Python version about 163 MB, binary about 162 MB. Hugging Face shows about 144 MB for the dataset files.
- Where to get it: the official CIFAR page (University of Toronto) and the Hugging Face CIFAR-10 dataset page.
uoft-cs/cifar10
If you want something more, check the table below
| Dataset | Resolution | Classes | Best For |
| ImageNet 1K | 224–256×256 | 1000 | Real-world large-scale classification |
| ImageNet-256. | 256×256 | 1000 | Direct high-res training |
| TinyImageNet | 64×64 | 200 | Mid-range benchmark |
| UC Merced Land Use | 256×256 | ~21 | Higher resolution small classification |
| MS COCO | >256×256 | ~80 objects | Detection / segmentation |