Update README.md

README.md

@@ -9,28 +9,24 @@ license: mit
 
 Official models for [_AnySat: An Earth Observation Model for Any Resolutions, Scales, and Modalities_](https://arxiv.org/pdf/2404.08351.pdf)
 
-## Abstract
-
-We introduce AnySat: a JEPA-based multimodal Earth Observation model that train simultaneously on diverse datasets with different scales, resolutions (spatial, spectral, temporal), and modality combinations.
-
 
-For more details and results, please check out our [github](https://github.com/gastruc/AnySat) and [project page](https://gastruc.github.io/projects/omnisat.html).  
-
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/662b7fba68ed7bbf40bfb0df/2tc0cFdOF2V0_KgptA-qV.png)
+<p align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/662b7fba68ed7bbf40bfb0df/Jh9eOnMePFiL84TOzhe86.png" alt="image/png" width="600" height="300">
+</p>
 
-## Datasets
+## Abstract
 
-  
-| Dataset name  |             Modalities                   |      Labels         |     Link      
-| ------------- | ---------------------------------------- | ------------------- | ------------------- |
-| PASTIS-HD     | **SPOT 6-7 (1m)** + S1/S2 (30-140 / year)| Crop mapping (0.2m) |    [huggingface](https://huggingface.co/datasets/IGNF/PASTIS-HD) or [zenodo](https://zenodo.org/records/10908628) |
-| TreeSatAI-TS  | Aerial (0.2m) + **S1/S2 (10-70 / year)** |   Forestry (60m)    |   [huggingface](https://huggingface.co/datasets/IGNF/TreeSatAI-Time-Series) |
-| FLAIR         |   aerial (0.2m) + S2 (20-114 / year)     |  Land cover (0.2m)  |  [huggingface](https://huggingface.co/datasets/IGNF/FLAIR) |
+ <div style="display: flex; align-items: center;">
+  <div style="flex: 1;">
+    <p>We introduce AnySat: a JEPA-based multimodal Earth Observation model that train simultaneously on diverse datasets with different scales, resolutions (spatial, spectral, temporal), and modality combinations.
 
 
-<p align="center">
-  <img src="https://github.com/user-attachments/assets/18acbb19-6c90-4c9a-be05-0af24ded2052" width="800" height="400">
-</p>
+For more details and results, please check out our [github](https://github.com/gastruc/AnySat) and [project page](https://gastruc.github.io/projects/omnisat.html).</p>
+  </div>
+  <div style="flex: 1; display: flex; justify-content: center;">
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/662b7fba68ed7bbf40bfb0df/2tc0cFdOF2V0_KgptA-qV.png" alt="image/png" width="400"/>
+  </div>
+</div>
 
 ### Inference 🔥
 
@@ -58,11 +54,12 @@ To get features from an observation of a batch of observations, you need to prov
 
 Time series keys require a "{key}_dates" (for example "s2_dates") tensor of size BxT that value an integer that represent the day of the year.
 Then you have to choose at which scale you want te produce features. Scale argument is in meters and represent the size of the desired patch size. 
-Outputs will be composed of the concatenation of a class token and a flattened feature map where each feature encodes a scale x scale zone 
+Outputs will be composed of the concatenation of a class token and a flattened feature map where each feature encodes a scale x scale zone. 
+Scale should divide the spatial cover of all modalities and be a multiple of 10.
 Then, you can run:
 
 ```python
-features = AnySat(data, scale=scale) #
+features = AnySat(data, scale=scale) #where scale is the size in meters of patches
 ```
 
 And then you can apply those features to the desired downstream task!