cosmetic changes

README.md

@@ -10,13 +10,17 @@
 
 ### Epipolar Losses
 
-We release implementations of the epipolar regression and the epipolar classification losses described in the paper in `epipolar_losses/`. In principle, these should be able to serve as drop-in replacements for regression and classification losses that use ground-truth correspondences, but depending on the model that is fine-tuned some adjustments to the input / output of the functions may need to be made.
+We release implementations of the epipolar regression and the epipolar classification losses described in the paper in `epipolar_losses/`. 
+
+In principle, these should be able to serve as drop-in replacements for regression and classification losses that use ground-truth correspondences, but depending on the model that is fine-tuned some adjustments to the input / output of the functions may need to be made.
 
 ### Scripts
 
 #### Bootstrapping: Estimating Fundamental Matrices
 
-In `scripts/estimate_fundamental_matrices.py` we also provide the skeleton of a script to use a pre-trained pixel correspondence estimator to estimate fundamental matrices which can then be used to fine-tune the model using the bootstrapping approach described in the paper. Since the exact form of this script depends quite heavily on the pre-trained model and dataset used, the end-user will need to implement the `setup_dataset` and `setup_model` functions, and likely will need to adjust the format to save the fundamental matrices in.
+In `scripts/estimate_fundamental_matrices.py` we also provide the skeleton of a script to use a pre-trained pixel correspondence estimator to estimate fundamental matrices which can then be used to fine-tune the model using the bootstrapping approach described in the paper. 
+
+Since the exact form of this script depends quite heavily on the pre-trained model and dataset used, the end-user will need to implement the `setup_dataset` and `setup_model` functions, and likely will need to adjust the format to save the fundamental matrices in.
 
 #### EuRoC-MAV Data Preparation
 
@@ -30,7 +34,9 @@ Then, run `sh scripts/eurocmav-preparation-scripts/prepare_eurocmav.sh` to prepa
 #### EuRoC-MAV Test Script
 
 The script in `scripts/test_eurocmav.py` evaluates a pixel correspondence estimator on the EuRoC-MAV dataset. The `setup_model` function needs to be implemented before usage.
+
 This script contains code to compute the metrics we report in the paper.
+
 It should also be mostly straightforward to adapt this script to compute the same metrics on a different dataset.
 
 ## Fine-Tuned Models