Update README.md

README.md

@@ -34,7 +34,7 @@ The dataset is commonly used for **benchmarking disentanglement learning**, and
 
 These variants allow systematic testing under **different nuisance variations** (color, noise, background texture, abstract factors).
 
-![Dataset Visualization](https://huggingface.co/datasets/randall-lab/dsprites-color/blob/main/animation0.gif)
+![Dataset Visualization](https://huggingface.co/datasets/randall-lab/dsprites-color/resolve/main/animation0.gif)
 
 ## Dataset Source
 - **Homepage**: [https://github.com/google-research/disentanglement_lib/](https://github.com/google-research/disentanglement_lib/tree/master)
@@ -51,9 +51,7 @@ These variants allow systematic testing under **different nuisance variations**
 |posX|0,...,31|[0, 1] normalized position (32 values)|
 |posY|0,...,31|[0, 1] normalized position (32 values)|
 
-**Note:** In this Color variant, the `color` and `colorValue` fields remain 0 to match the original dSprites format. The **actual applied color** is provided in `colorRGB`, a list of `[R, G, B]` values ∈ [0.5,1.0].
-
-Each image corresponds to a unique combination of these 6 factors. The images are stored in a **row-major order** (fastest-changing factor is `posY`, slowest-changing factor is `color`).
+**Note:** In this Color variant, the `color` and `colorValue` fields remain 0 to match the original dSprites format. The **actual applied color** is provided in `colorRGB`, a list of `[R, G, B]` values ∈ [0.5,1.0]. Each image corresponds to a unique combination of these 6 factors. The images are stored in a **row-major order** (fastest-changing factor is `posY`, slowest-changing factor is `color`).
 
 ### Why no train/test split?
 The Color dSprites dataset does not provide an official train/test split. It is designed for **representation learning research**, where the goal is to learn disentangled and interpretable latent factors. Since the dataset is a complete Cartesian product of all factor combinations, models typically require access to the full dataset to explore factor-wise variations.