updated README.md

README.md

@@ -18,34 +18,34 @@ If you use this code in your project, please cite use using:
 }
 ````
 
-### Dependencies ###
+## Dependencies ###
   - Python 3
   - pytorch > 0.2.0, torchvision
   - numpy, skimage (included in conda)
 
-### Getting started ###
+## Getting started ###
   - demo.py demonstrates the results on the ProposalFlow dataset
   - train.py is the main training script
   - eval_pf.py evaluates on the ProposalFlow dataset
 
-### Trained models ###
+## Trained models ###
 
-# Using Streetview-synth dataset
+### Using Streetview-synth dataset
   - [streetviewsynth-aff](http://www.di.ens.fr/willow/research/cnngeometric/trained_models/pytorch/best_streetview_checkpoint_adam_affine_grid_loss.pth.tar)
   - [streetviewsynth-tps](http://www.di.ens.fr/willow/research/cnngeometric/trained_models/pytorch/best_streetview_checkpoint_adam_tps_grid_loss.pth.tar)
 
-### Results on PF
+#### Results on PF
 ```
 PCK affine: 0.472
 PCK tps: 0.513
 PCK affine+tps: 0.572
 ```
 
-# Using Pascal-synth dataset
+### Using Pascal-synth dataset
   - [pascal-aff](http://www.di.ens.fr/willow/research/cnngeometric/trained_models/pytorch/best_pascal_checkpoint_adam_affine_grid_loss.pth.tar)
   - [pascal-tps](http://www.di.ens.fr/willow/research/cnngeometric/trained_models/pytorch/best_pascal_checkpoint_adam_tps_grid_loss.pth.tar)
 
-### Results on PF
+#### Results on PF
 ```
 PCK affine: 0.478
 PCK tps: 0.428