Update README.md

README.md

@@ -10,14 +10,14 @@
 This repository contains inference code and models for the paper Deep Lighting Environment Map Estimation from Spherical Panoramas ([link](arxiv_link_here)).
 
 
-# Requirements
+## Requirements
 The code is based on PyTorch and has been tested with Python 3.7 and CUDA 10.0.
 We recommend setting up a virtual environment (follow the `virtualenv` documentation) for installing PyTorch and the other necessary Python packages.
 Once your environment is set up and activated, install the necessary packages:
 
 `pip install torch===1.2.0 torchvision===0.4.0 -f https://download.pytorch.org/whl/torch_stable.html`
 
-# Inference
+## Inference
 You can download pre-trained models from [here](https://drive.google.com/file/d/1W7wmZt0AtBKu-jqEnd4aAL4c5dXczlh4/view?usp=sharing), which includes pre-trained LDR-to-HDR autoencoder and Lighting Encoder. Please put the extracted files under `models` and run:
 
 `python inference.py`
@@ -26,3 +26,5 @@ The following flags specify the required parameters.
 - `--input_path`: Specifies the path of the input image.
 - `--out_path`: Specifies the file of the output path. 
 - `--deringing`: Enable/disable low pass deringing filter for the predicted SH coefficients.
+
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