Update README.md

README.md

@@ -1,35 +1,35 @@
----
-license: agpl-3.0
-pipeline_tag: image-segmentation
-tags:
-- medical
-- biology
----
-
-## VascX models
-
-This repository contains the instructions for using the VascX models from the paper [VascX Models: Model Ensembles for Retinal Vascular Analysis from Color Fundus Images](https://arxiv.org/abs/2409.16016).
-
-The model weights are in [huggingface](https://huggingface.co/Eyened/vascx).
-
-<img src="imgs/CHASEDB1_12R_rgb.png" width="240" height="240"><img src="imgs/CHASEDB1_12R.png" width="240" height="240">
-
-<img src="imgs/DRIVE_22_rgb.png" width="240" height="240"><img src="imgs/DRIVE_22.png" width="240" height="240">
-
-<img src="imgs/HRF_04_g_rgb.png" width="240" height="240"><img src="imgs/HRF_04_g.png" width="240" height="240">
-
-### Installation
-
-To install the entire fundus analysis pipeline including fundus preprocessing, model inference code and vascular biomarker extraction:
-
-1. Create a conda or virtualenv virtual environment, or otherwise ensure a clean environment.
-
-2. Install the [rtnls_inference package](https://github.com/Eyened/retinalysis-inference).
-
-### Usage
-
-To speed up re-execution of vascx we recommend to run the preprocessing and segmentation steps separately:
-
-1. Preprocessing. See [this notebook](./notebooks/0_preprocess.ipynb). This step is CPU-heavy and benefits from parallelization (see notebook).
-
-2. Inference. See [this notebook](./notebooks/1_segment_preprocessed.ipynb). All models can be ran in a single GPU with >10GB VRAM.
+---
+license: agpl-3.0
+pipeline_tag: image-segmentation
+tags:
+- medical
+- biology
+---
+
+## VascX models
+
+This repository contains the instructions for using the VascX models from the paper [VascX Models: Model Ensembles for Retinal Vascular Analysis from Color Fundus Images](https://arxiv.org/abs/2409.16016).
+
+The model weights are in [huggingface](https://huggingface.co/Eyened/vascx).
+
+<img src="imgs/CHASEDB1_12R_rgb.png" width="240" height="240" style="display:inline"><img src="imgs/CHASEDB1_12R.png" width="240" height="240" style="display:inline">
+
+<img src="imgs/DRIVE_22_rgb.png" width="240" height="240" style="display:inline"><img src="imgs/DRIVE_22.png" width="240" height="240" style="display:inline">
+
+<img src="imgs/HRF_04_g_rgb.png" width="240" height="240" style="display:inline"><img src="imgs/HRF_04_g.png" width="240" height="240" style="display:inline">
+
+### Installation
+
+To install the entire fundus analysis pipeline including fundus preprocessing, model inference code and vascular biomarker extraction:
+
+1. Create a conda or virtualenv virtual environment, or otherwise ensure a clean environment.
+
+2. Install the [rtnls_inference package](https://github.com/Eyened/retinalysis-inference).
+
+### Usage
+
+To speed up re-execution of vascx we recommend to run the preprocessing and segmentation steps separately:
+
+1. Preprocessing. See [this notebook](./notebooks/0_preprocess.ipynb). This step is CPU-heavy and benefits from parallelization (see notebook).
+
+2. Inference. See [this notebook](./notebooks/1_segment_preprocessed.ipynb). All models can be ran in a single GPU with >10GB VRAM.