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README.md

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-# CFA-ECML-PKDD-2022
+<p align="center">
-Class-Incremental Learning via Knowledge Amalgamation - ECML PKDD 2022
+    <a>
+	    <img src='https://img.shields.io/badge/python-3.6%7C3.7%7C3.8-blueviolet' alt='Python' />
+	</a>
+</p>
+
+# CFA - Class-Incremental Learning via Knowledge Amalgamation
+
+Official repository of [Class-Incremental Learning via Knowledge Amalgamation](https://github.com/Ivsucram/CFA-ECML-PKDD-2022)
+
+## Citing this work
+
+```
+To be updated
+```
+
+## Setting up a CONDA environment
+
+
+Execute line by line
+
+```
+conda create -n CFA python=3.8
+conda activate CFA
+conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
+conda install tqdm matplotlib
+pip install avalanche-lib
+```
+
+## Setting up a PIP environment
+
+Execute line by line
+
+```
+pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113
+pip install tqdm
+pip install matplotlib
+pip install avalanche-lib
+```
+
+## Running
+
+
+For a list of commands:
+```
+python cfa.py --help
+```
+
+For MNIST
+```
+python cfa.py --dataset mnist --memory_budget 1000 --memory_strategy fixed
+python cfa.py --dataset mnist --memory_budget 1000 --memory_strategy grow
+```
+
+For CIFAR10
+```
+python cfa.py --dataset cifar10 --memory_budget 1000 --memory_strategy fixed
+python cfa.py --dataset cifar10 --memory_budget 1000 --memory_strategy grow
+```
+
+For CIFAR100
+
+```
+python cfa.py --dataset cifar100 --memory_budget 1000 --memory_strategy fixed
+python cfa.py --dataset cifar100 --memory_budget 1000 --memory_strategy grow
+```
+
+For Tiny ImageNet
+
+```
+python cfa.py --dataset tiny10 --memory_budget 1000 --memory_strategy fixed
+python cfa.py --dataset tiny10 --memory_budget 1000 --memory_strategy grow
+```
+
+## Tip 1
+
+If you are not intersted in evaluating the BWT and FWT metrics, just the ACC, modify the line 721 from:
+
+```python
+        for n_task in range(2, n_tasks + 1, 1):
+```
+
+to
+```python
+        for n_task in range(n_tasks, n_tasks + 1, 1):
+```
+
+In order to calculate BWT and FWT, we need to run multiple CFA experiments, which can be time-consuming. By making this change, you force the algorithm to just run a full amalgamation of all teachers. This will give you the ACC metric, but BWT and FWT will not be valid.
+
+## Tip 2
+
+CFA accuracy (the student model accuracy) is really dependent on the performance of the teacher models.