timm provides PyTorch image encoders and backbones along with training, evaluation, inference, and export scripts.
Open source
24 alternativeslearn2learn
The learn2learn library supports meta learning experiments and research with PyTorch.
About learn2learn
learn2learn is an open-source PyTorch library for meta-learning research. It is intended to speed up two parts of the research cycle: fast prototyping of new ideas and reproducible evaluation of those ideas, through low-level utilities, a unified interface, implementations of existing algorithms and standardized benchmarks.
Key features
- Taskset and transforms build few-shot tasks from any PyTorch dataset
- High-level wrappers for MAML, ProtoNets, ANIL, Meta-SGD, Reptile and related algorithms
- LearnableOptimizer and ModuleTransform for differentiable optimization and meta-descent
- Vision datasets and benchmarks including Omniglot, mini-/tiered-ImageNet, FC100, CIFAR-FS
- Gym utilities and AsyncVectorEnv for parallel meta-reinforcement-learning environments
- Retains compatibility with torchvision, torchaudio, torchtext, cherry and other PyTorch libraries
Best for: Meta-learning researchers who work in PyTorch and need ready-made algorithms, benchmarks and task-generation utilities for experiments.
Good for
- Open source and installable with pip install learn2learn
- Combines low-level utilities with implementations of existing algorithms and standardized benchmarks
- Documentation, tutorials, examples and a Slack channel are linked from the project
Watch out for
- Scope is meta-learning research; the source does not describe production deployment tooling
- No pricing or commercial support information is stated in the source
learn2learn at a glance
- Category
- AI & Machine Learning › Models & ML Research
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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learn2learn FAQ
How is learn2learn installed?
The source lists the installation command as: pip install learn2learn.
Does it work alongside other PyTorch-based libraries?
The project states it retains compatibility with torchvision, torchaudio, torchtext, cherry, and any other PyTorch-based library.
Are standardized few-shot datasets included?
The source says datasets such as Omniglot, mini-/tiered-ImageNet, FC100 and CIFAR-FS are readily available in learn2learn.vision.datasets.
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