Update README.md

README.md

@@ -54,13 +54,14 @@ To address these challenges, 'opensr-test' provides a fair approach for SR bench
 
 ## **Datasets**
 
-The `opensr-test` package provides five datasets for benchmarking SR models. These datasets are carefully crafted to minimize spatial and spectral misalignment. Each dataset consist of a dictionary with the following keys:
+The `opensr-test` package provides five datasets for benchmarking SR models. These datasets are carefully crafted to minimize spatial and spectral misalignment. Each dataset consists of a dictionary with the following keys:
 
 - **`L2A`**: Sentinel-2 L2A bands (12 bands).
 - **`L1C`**: Sentinel-2 L1C bands (12 bands).
 - **`HR`**: High-resolution image (RGBNIR) without harmonization.
 - **`HRharm`**: Harmonized high-resolution image (RGBNIR). The HRharm image is **harmonized with respect to the Sentinel-2 L2A bands**.
 - **`metadata`**: A pandas DataFrame with the images' metadata.
+- **`gradient_threshold`**: Threshold estimated by human interpretation. All the pixels or patches with a gradient higher than this threshold are considered relevant and, therefore, used to estimate the percentage of hallucination, omission, and correctness by the SR model.
 
 We provide 12 bands for Sentinel-2 L2A (see table below) and 13 for Sentinel-2 L1C (see table below).