Update README.md

README.md

@@ -19,11 +19,11 @@ The SITR dataset consists of three main parts:
 1. **Simulated Tactile Dataset**  
    A large-scale synthetic dataset generated using physics-based rendering (PBR) in Blender. This dataset spans 100 unique simulated sensor configurations with tactile signals, calibration images, and corresponding surface normal maps. It includes 10K unique contact configurations generated using 50 high-resolution 3D meshes of common household objects, resulting in a pre-training dataset of 1M samples.
 
-2. **Real-World Tactile Dataset – Classification**  
+2. **Classification Tactile Dataset**  
    Data collected from 7 real sensors (including variations of GelSight Mini, GelSight Hex, GelSight Wedge, and DIGIT). For the classification task, 20 objects are pressed against each sensor at various poses and depths, accumulating 1K tactile images per object (140K images in total, with 20K per sensor). We decided to only use 16 of the objects for our classification experiments and some of the items were deemed unsuitable (this was decided before experimentation). The dataset is provided as separate train (80%) and test sets (20%). 
 
-3. **Real-World Tactile Dataset – Pose Estimation**  
-   For pose estimation, tactile signals are recorded using a modified Ender-3 Pro 3D printer equipped with 3D-printed indenters. This setup provides accurate ground truth (x, y, z coordinates) for contact points. Data were collected for 6 indenters across 4 sensors, resulting in 1K samples per indentor (24K images in total, 6K per sensor). This dataset is also organized into train and test sets.
+3. **Pose Estimation Tactile Dataset**  
+   For pose estimation, tactile signals are recorded using a modified Ender-3 Pro 3D printer equipped with 3D-printed indenters. This setup provides accurate ground truth (x, y, z coordinates) for contact points. Data were collected for 6 indenters across 4 sensors, resulting in 1K samples per indentor (24K images in total, 6K per sensor). This dataset is also organized into train (80%) and test sets (20%).
 
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@@ -93,7 +93,7 @@ data_root/
 └── ...
 ```
 
-### 2. Real-World Classification Dataset
+### 2. Classification Dataset
 
 Each of the `train_set/` and `test_set/` directories follows this structure:
 
@@ -113,7 +113,7 @@ train_set/  (or test_set/)
 └── ...
 ```
 
-### 3. Real-World Pose Estimation Dataset
+### 3. Pose Estimation Dataset
 
 Similarly, each of the `train_set/` and `test_set/` directories is structured as follows: