A collection of datasets comparable to MNIST and dSprites for machine learning research.
| Dataset | Description | Size | Resolution |
|---|---|---|---|
| MNIST | Handwritten digits (0-9) | 70,000 images | 28x28 grayscale |
| Fashion-MNIST | Clothing items (t-shirts, pants, etc.) - drop-in replacement for MNIST | 70,000 images | 28x28 grayscale |
| MedMNIST | Biomedical image collection (12 2D + 6 3D datasets) | ~708K 2D, ~10K 3D | 28x28 / 28x28x28 |
| CIFAR-10 | Natural images in 10 classes | 60,000 images | 32x32 color |
| CIFAR-100 | Natural images in 100 classes | 60,000 images | 32x32 color |
| EMNIST | Extended MNIST with handwritten letters and digits | 814,255 images | 28x28 grayscale |
| KMNIST | Kuzushiji-MNIST - Japanese hiragana characters | 70,000 images | 28x28 grayscale |
| Dataset | Description | Factors | Size |
|---|---|---|---|
| dSprites | 2D shapes with 6 ground truth latent factors (color, shape, scale, rotation, x, y) | 6 factors | 737,280 images |
| Shapes3D | 3D shapes with factors: floor color, wall color, object color, size, shape, azimuth | 6 factors | 480,000 images (64x64 RGB) |
| dSprites-Scream | dSprites with textured backgrounds for added visual complexity | 6 factors | Variable |
| Infinite dSprites (idSprites) | Procedurally generates unlimited 2D shapes for continual learning | Configurable | Unlimited |
| 3D Chairs | 3D rendered chairs with varying pose and style | Pose, style | ~86,000 images |
| CelebA | Face images with 40 binary attributes | 40 attributes | 202,599 images |
| dMelodies | Audio disentanglement benchmark with musical melodies | 9 factors | 1,524,096 samples |