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Update README.md

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@@ -38,6 +38,8 @@ Touch-Ex is divided into two predefined splits:
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  Each dataset sample corresponds to a single tactile frame and includes image data, force measurements, interaction metadata, semantic descriptions, and structured tactile attributes.
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  ## Example Samples
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  | image | object | region | force_level | motion | material | hardness | description |
@@ -93,6 +95,8 @@ Every interaction consists of a sequence of 120 tactile frames collected from a
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  Contact points were sampled within each labelled region to ensure diverse coverage of the object surface, with care taken to include any distinctive or unique features.
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  ### Force Annotation
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  To improve consistency across interactions, FSR measurements were used to categorise contact into three predefined force levels. These force levels were designed to capture meaningful differences in sensor deformation while remaining practical for consistent human application.
@@ -245,17 +249,21 @@ Reference implementations, baseline models, and future experiments will be avail
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  | Statistic | Value |
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  | -------------------------- | -------- |
 
 
 
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  | Training objects | 14 |
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  | Training object regions | 33 |
 
 
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  | Unseen test objects | 9 |
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  | Unseen test object regions | 20 |
 
 
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  | Total objects | 23 |
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  | Total object regions | 53 |
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- | Force levels | 3 |
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- | Motion types | 2 |
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- | Frames per interaction | 120 |
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  | Total interactions | 1,128 |
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- | Total visuo-tactile frames | 135,360 |
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  ---
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@@ -328,4 +336,4 @@ Touch-Ex was collected using the DIGIT vision-based tactile sensor. If you use t
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  year = {2020},
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  doi = {10.1109/LRA.2020.2977257}
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  }
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- ```
 
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  Each dataset sample corresponds to a single tactile frame and includes image data, force measurements, interaction metadata, semantic descriptions, and structured tactile attributes.
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+ The training split was designed so that each object contributes an equal number of tactile interactions regardless of the number of annotated regions, while the unseen test split is intentionally smaller and reserved exclusively for object-level generalisation evaluation.
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+
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  ## Example Samples
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  | image | object | region | force_level | motion | material | hardness | description |
 
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  Contact points were sampled within each labelled region to ensure diverse coverage of the object surface, with care taken to include any distinctive or unique features.
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+ To maintain balanced object representation, the number of interactions collected per region was adjusted according to the number of labelled regions on each object. Consequently, every training object contributes the same number of tactile interactions, preventing objects with more annotated regions from disproportionately influencing model training.
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+
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  ### Force Annotation
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  To improve consistency across interactions, FSR measurements were used to categorise contact into three predefined force levels. These force levels were designed to capture meaningful differences in sensor deformation while remaining practical for consistent human application.
 
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  | Statistic | Value |
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  | -------------------------- | -------- |
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+ | Force levels | 3 |
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+ | Motion types | 2 |
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+ | Frames per interaction | 120 |
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  | Training objects | 14 |
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  | Training object regions | 33 |
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+ | Training object interactions | 1,008 |
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+ | Training object frames | 120,960 |
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  | Unseen test objects | 9 |
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  | Unseen test object regions | 20 |
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+ | Unseen test object interactions | 120 |
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+ | Unseen test object frames | 14,400 |
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  | Total objects | 23 |
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  | Total object regions | 53 |
 
 
 
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  | Total interactions | 1,128 |
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+ | Total frames | 135,360 |
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  ---
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  year = {2020},
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  doi = {10.1109/LRA.2020.2977257}
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  }
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+ ```