Add image-to-image task category and refine README

README.md

@@ -2,9 +2,10 @@
 license: cc-by-nc-nd-4.0
 size_categories:
 - 100K<n<1M
+task_categories:
+- image-to-image
 ---
 
-
 # PhysicsGen: Can Generative Models Learn from Images to Predict Complex Physical Relations?
 
 ## Paper
@@ -14,6 +15,8 @@ Accepted at IEEE/CVF Conference on Computer Vision and Pattern Recognition ([CVP
 Preprint is available here: [https://arxiv.org/abs/2503.05333](https://arxiv.org/abs/2503.05333)
 
 Website: [https://www.physics-gen.org/](https://www.physics-gen.org/)
+Github: [https://github.com/physicsgen/physicsgen](https://github.com/physicsgen/physicsgen)
+
 
 ## Overview
 
@@ -21,7 +24,7 @@ Website: [https://www.physics-gen.org/](https://www.physics-gen.org/)
 
 ### Variants
 
-- **Urban Sound Propagation:** [sound_baseline, sound_reflection, sound_diffraction, sound_combined]
+- **Urban Sound Propagation:** [`sound_baseline`, `sound_reflection`, `sound_diffraction`, `sound_combined`]
 
   Each sound example includes:
   - Geographic coordinates: `lat`, `long`
@@ -29,14 +32,14 @@ Website: [https://www.physics-gen.org/](https://www.physics-gen.org/)
   - Images: `soundmap`, `osm`, `soundmap_512`
   - Additional metadata: `temperature`, `humidity`, `yaw`, `sample_id`
 
-- **Lens Distortion:** [lens_p1, lens_p2]
+- **Lens Distortion:** [`lens_p1`, `lens_p2`]
 
   Each lens example includes:
   - Calibration parameters: `fx`, `k1`, `k2`, `k3`, `p1`, `p2`, `cx`
   - Label file path: `label_path`
   - Note: The script for applying the distortion to the CelebA Dataset is located [here](https://github.com/physicsgen/physicsgen/blob/main/eval_scripts/hf_apply_lens_distortion.py).
 
-- **Dynamics of rolling and bouncing movements:** [ball_roll, ball_bounce]
+- **Dynamics of rolling and bouncing movements:** [`ball_roll`, `ball_bounce`]
 
   Each ball example includes:
   - Metadata: `ImgName`, `StartHeight`, `GroundIncli`, `InputTime`, `TargetTime`
@@ -60,6 +63,7 @@ input_img = sample["osm"]
 target_img = sample["soundmap_512"]
 
 # plot Input vs Target Image for a single sample
+import matplotlib.pyplot as plt
 fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
 ax1.imshow(input_img)
 ax2.imshow(target_img)
@@ -67,3 +71,17 @@ plt.show()
 ```
 
 ![image info](./example_sample.png)
+
+## Results (Summary - see paper for full details)
+
+PhysicsGen includes baseline results for several models across the three tasks.  See the paper for a complete evaluation.
+
+## License
+This dataset is licensed under a [Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International](https://creativecommons.org/licenses/by-nc-nd/4.0/)
+
+## Funding Acknowledgement
+We express our gratitude for the financial support provided by the German Federal Ministry of Education and Research (BMBF). This project is part of the "Forschung an Fachhochschulen in Kooperation mit Unternehmen (FH-Kooperativ)" program, within the joint project [KI-Bohrer](https://www.ki-bohrer.de/), and is funded under the grant number 13FH525KX1.
+
+<p align="center">
+  <img src="figures/bmbf_logo.png" alt="BMBF-Logo">
+</p>