Update README.md

README.md

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-# pytorch-maml
-Model-Agnostic Meta-Learning in Pytorch
+# Model-Agnostic Meta-Learning
+
+An implementation of Model-Agnostic Meta-Learning (MAML) in [PyTorch](https://pytorch.org/) with [Torchmeta](https://github.com/tristandeleu/pytorch-meta).
+
+### Getting started
+To avoid any conflict with your existing Python setup, it is suggested to work in a virtual environment with [`virtualenv`](https://docs.python-guide.org/dev/virtualenvs/). To install `virtualenv`:
+```bash
+pip install --upgrade virtualenv
+```
+Create a virtual environment, activate it and install the requirements in [`requirements.txt`](requirements.txt).
+```bash
+virtualenv venv
+source venv/bin/activate
+pip install -r requirements.txt
+```
+
+#### Requirements
+ - Python 3.5 or above
+ - PyTorch 1.2
+ - Torchvision 0.4
+ - Torchmeta 1.1
+
+### Usage
+You can use [`train.py`](train.py) to meta-train your model with MAML. For example, to run MAML on Omniglot 1-shot 5-way with default parameters from the original paper:
+```bash
+python train.py /path/to/data --dataset omniglot --num-ways 5 --num-shots 1 --use-cuda --step-size 0.4 --batch-size 32 --num-workers 8 --num-epochs 600
+```
+
+
+### References
+The code available in this repository is mainly based on the paper
+> Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep
+networks. _International Conference on Machine Learning (ICML)_, 2017 [[ArXiv](https://arxiv.org/abs/1703.03400)]
+
+If you want to cite this paper
+```
+@article{finn17maml,
+  author    = {Chelsea Finn and Pieter Abbeel and Sergey Levine},
+  title     = {Model-{A}gnostic {M}eta-{L}earning for {F}ast {A}daptation of {D}eep {N}etworks},
+  journal   = {International Conference on Machine Learning (ICML)},
+  year      = {2017},
+  url       = {https://arxiv.org/abs/1703.03400}
+}
+```