# Current Release

*Avalanche* is a framework in constant development. Thanks to the support of the [ContinualAI](https://www.continualai.org) community and its active members we plan to **extend its features** and **improve its usability** based on the demands of our research community!\
\
A the moment, *Avalanche* is in **Beta (v0.3.1).** We support a large number of *Benchmarks*, *Strategies* and *Metrics*, that makes it, we believe, **the best tool out there for your continual learning research!** 💪

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You can find the **full list of available features** on the [API documentation](https://avalanche-api.continualai.org).
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Do you think we are missing some important features? Please [let us know](/questions-and-issues/request-a-feature.md)! We deeply value [your feedback](/questions-and-issues/give-feedback.md)!
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## Benchmarks and Datasets

You find a complete list of the features on the [benchmarks API documentation](https://avalanche-api.continualai.org/en/latest/benchmarks.html).

### 🖼️ Datasets

Avalanche supports all the most popular computer vision datasets used in *Continual Learning*. Some of them are available in [*Torchvision*](https://pytorch.org/docs/stable/torchvision/index.html), while other have been integrated by us. Most datasets can be automatically downloaded by Avalanche.

* **Toy datasets**: MNIST, Fashion MNIST, KMNIST, EMNIST, QMNIST.
* **CIFAR:** CIFAR10, CIFAR100.
* **ImageNet**: TinyImagenet, MiniImagenet, Imagenet.
* **Others**: EndlessCLDataset, CUB200, OpenLORIS, Stream-51, INATURALIST2018, Omniglot, CLEARImage, ...

### 📚 Benchmarks

All the major continual learning benchmarks are available and ready to use. Benchmarks split the datasets and create the train and test streams:

* **MNIST**: SplitMNIST, RotatedMNIST, PermutedMNIST, SplitFashionMNIST.
* **CIFAR10**: SplitCIFAR10, SplitCIFAR100, SplitCIFAR110.
* **CORe50**: all the CORe50 scenarios are supported.
* **Others**: SplitCUB200, CLStream51, CLEAR.

## 📈 Continual Learning Strategies

Avalanche provides *Continual Learning* algorithms (*strategies*). We are continuously expanding the library with new algorithms and making sure they can reproduce seminal papers results in the sibling project [CL-Baselines](https://github.com/ContinualAI/continual-learning-baselines).

* **Baselines**: Naive, JointTraining, Cumulative.
* **Rehearsal**: Replay with reservoir sampling and balanced buffers, GSS greedy, CoPE, Generative Replay.
* **Regularization**: EWC, LwF, GEM, AGEM, CWR\*, Synaptic Intelligence, MAS.
* **Architectural**: Progressive Neural Networks, multi-head, incremental classifier.
* **Others**: GDumb, iCaRL, AR1, Streaming LDA, LFL.

## Models

Avalanche uses and extends pytorch `nn.Module` to define continual learning models:

* support for `nn.Module`s and `torchvision` models.
* Dynamic output heads for class-incremental scenarios and multi heads for task-incremental scenarios.
* support for architectural strategies and dynamically expanding models such as progressive neural networks.

## 📊 Metrics and Evaluation

Avalanche provides continuous evaluation of CL strategies with a large set of **Metrics**. They are collected and logged automatically by the strategy during the training and evaluation loops.

* **Standard Performance Metrics**: accuracy, loss, confusion (averaged over streams or experiences).
* **CL-Metrics**: backward/forward transfer, forgetting.
* **Computational Resources**: CPU and RAM usage, MAC, execution times.

and [many more](https://avalanche-api.continualai.org/en/latest/evaluation.html#).


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://avalanche.continualai.org/getting-started/alpha-version.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.