Update README.md

README.md

@@ -53,40 +53,40 @@ The **IndustrialLateralLoads** dataset is designed for object detection and inst
 When loaded using the `datasets` library, each sample contains the following fields:
 
 - **`image`**: A `PIL.Image` object containing the original RGB image.
-- **`mask`**: A `PIL.Image` object containing the segmentation mask. The background is black `(0, 0, 0)`.
+- **`mask`**: A `PIL.Image` object containing the binary segmentation mask.
 - **`class_id`**: The classification label of the load (integer mapped to class name).
 - **`bbox`**: The bounding box coordinates in the format `[x_min, y_min, width, height]`.
-- **`mask_rgb_color`**: A list `[R, G, B]` indicating the specific color used for the class in the segmentation mask.
+- **`mask_rgb_color`**: An `[R, G, B]` list specifying the RGB triplet associated with the class in the binary mask.
 
 ### Raw data organization (repository files):
 If you prefer to download the raw files directly, the repository includes the following folders:
 
 - **`images`**: Contains the source images.
-- **`masks`**: Contains the PNG segmentation masks.
+- **`masks`**: Contains the binary PNG segmentation masks.
 - **`bboxes`**: Contains `.txt` files with bounding box annotations.
 
 **Remark 1.** Each row in a `.txt` file (inside the `bboxes` folder) follows this format:
 
 `<class_name> <instance_id> <x_min> <y_min> <width> <height>`,
 
-where the `<instance_id>` field is legacy and not primarily used in the currently published dataset.
+where the `<instance_id>` field is legacy and not relevant in the currently published dataset.
 
 **Remark 2.** The segmentation masks are provided as **binary RGB images**. They contain only **two** distinct pixel values:
 - **Background:** Black `(0, 0, 0)`.
 - **Foreground:** The specific **RGB Color** associated with the class ID listed below.
 | Class ID | Class Name              | Foreground RGB Color |
 | :------- | :---------------------- | :------------------- |
-| **0** | **verde_plastico** | (51, 221, 255)  |
-| **1** | **azul** | (255, 204, 51)  |
-| **2** | **negro_plastico** | (52, 209, 183)  |
-| **3** | **negro_carton** | (255, 96, 55)   |
-| **4** | **roja_plastico** | (36, 179, 83)   |
-| **5** | **carton** | (255, 125, 187) |
-| **6** | **verde_carton** | (221, 255, 51)  |
-| **7** | **roja_eldulze** | (170, 240, 209) |
-| **8** | **verde_plastico_oscuro**| (255, 10, 124) |
-| **9** | **verde_cogollo** | (184, 61, 245)  |
-| **10** | **ilfres** | (116, 113, 206) |
+| **0** | **verde_plastico** | `(51, 221, 255)`  |
+| **1** | **azul** | `(255, 204, 51)`  |
+| **2** | **negro_plastico** | `(52, 209, 183)`  |
+| **3** | **negro_carton** | `(255, 96, 55)`   |
+| **4** | **roja_plastico** | `(36, 179, 83)`   |
+| **5** | **carton** | `(255, 125, 187)` |
+| **6** | **verde_carton** | `(221, 255, 51)`  |
+| **7** | **roja_eldulze** | `(170, 240, 209)` |
+| **8** | **verde_plastico_oscuro**| `(255, 10, 124)` |
+| **9** | **verde_cogollo** | `(184, 61, 245)`  |
+| **10** | **ilfres** | `(116, 113, 206)` |
 ### Scientific validation & ongoing research:
 This dataset serves as a benchmark for validating computer vision approaches in industrial logistics. It remains an active resource for ongoing research, with current efforts shifting towards **instance segmentation** methodologies in industrial environments.