Update README.md

README.md

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 In this release, we provide an open source implementation of the FlyNet supervised learning experiments in [**A Hybrid Compact Neural Architecture for Visual Place Recognition**](https://doi.org/10.1109/LRA.2020.2967324), DOI [10.1109/LRA.2020.2967324](https://doi.org/10.1109/LRA.2020.2967324), accepted for publication in the IEEE Robotics and Automation Letters (RA-L) journal. Preprint version available at https://arxiv.org/abs/1910.06840.
 
-Project page: https://mchancan.github.io/projects/FlyNet
-
-## Abstract
-
-State-of-the-art algorithms for visual place recognition, and related visual navigation systems, can be broadly split into two categories: computer-science-oriented models including deep learning or image retrieval-based techniques with minimal biological plausibility, and neuroscience-oriented dynamical networks that model temporal properties underlying spatial navigation in the brain. In this letter, we propose a new compact and high-performing place recognition model that bridges this divide for the first time. Our approach comprises two key neural models of these categories: (1) FlyNet, a compact, sparse two-layer neural network inspired by brain architectures of fruit flies, Drosophila melanogaster, and (2) a one-dimensional continuous attractor neural network (CANN). The resulting FlyNet+CANN network incorporates the compact pattern recognition capabilities of our FlyNet model with the powerful temporal filtering capabilities of an equally compact CANN, replicating entirely in a hybrid neural implementation the functionality that yields high performance in algorithmic localization approaches like SeqSLAM. We evaluate our model, and compare it to three state-of-the-art methods, on two benchmark real-world datasets with small viewpoint variations and extreme environmental changes - achieving 87% AUC results under day to night transitions compared to 60% for Multi-Process Fusion, 46% for LoST-X and 1% for SeqSLAM, while being 6.5, 310, and 1.5 times faster, respectively.
+Project page: https://mchancan.github.io/projects/
 
 ## Dataset
 
-The dataset needed to run this code can be downloaded from
+The dataset used to run this code can be downloaded from
 [here](https://drive.google.com/open?id=1xrHKrHYgSqrMk9-XeC1qIe8UYDmOsgfd), which is a small subset of the Nordland dataset. However, this code can easily be adapted to run across other much larger datasets.
 
 ## Dependencies
@@ -19,7 +15,7 @@ This code was tested on [PyTorch](https://pytorch.org/) v1.0 and Python 3.6.
 
 ## Use FlyNet
 
-We provide a demo of FlyNet on the Nordland dataset. After downloading the [dataset](https://drive.google.com/open?id=1xrHKrHYgSqrMk9-XeC1qIe8UYDmOsgfd), extract it into the `dataset/` folder and run:
+We provide a demo of FlyNet on the Nordland dataset. After downloading the [dataset](https://drive.google.com/open?id=1xrHKrHYgSqrMk9-XeC1qIe8UYDmOsgfd), extract it into the `dataset/` directory and run:
 
 	python main.py