Update README.md

README.md

@@ -31,7 +31,7 @@ model-index:
 
 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the [Lokier & Al Junaibi (2016)](https://onlinelibrary.wiley.com/doi/10.1111/sed.12293) data S1. 
 
-The model captures the expertise of 177 volunteers from 33 countries with 3270 years of academic & industry experience in classifying 14 carbonates thin section samples by using the classical [Dunham (1962)](https://en.wikipedia.org/wiki/Dunham_classification) carbonate classification. 
+The model captures the expertise of 177 volunteers from 33 countries with 3,270 years of academic & industry experience in classifying 14 carbonate thin section samples by using the classical [Dunham (1962)](https://en.wikipedia.org/wiki/Dunham_classification) carbonate classification. 
 
 In the original paper, the authors intended to objectively analyze whether these volunteers have the same standards in applying Dunham classification.
 
@@ -39,6 +39,7 @@ In the original paper, the authors intended to objectively analyze whether these
 
 - Input: Carbonate thin section image, can be either parallel-polarized (PPL) or cross-polarized (XPL)
 - Output: Dunham classification (Mudstone/Wackestone/Packstone/Grainstone/Boundstone/Crystalline) and the probability value
+- Limitation: The original dataset is missing Boundstone sample, hence it cannot classify a Boundstone.
 
 Sample image source: [Grainstone - Wikipedia](https://en.wikipedia.org/wiki/Grainstone)
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64ff0bce56243ce8cb6df456/r4aBwewYuL-WLfTdqqFL-.png)
@@ -47,9 +48,9 @@ Sample image source: [Grainstone - Wikipedia](https://en.wikipedia.org/wiki/Grai
 
 Source: [Lokier & Al Junaibi (2016), Data S1](https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2Fsed.12293&file=sed12293-sup-0001-SupInfo.zip)
 
-The data consists of 14 samples. Each samples has 3 magnifications (x2, x4, and x10) and taken in PPL and XPL. Hence, there are 14 * 3 * 2 = 84 samples in the training dataset.
+The data consists of 14 samples. Each samples has 3 magnifications (x2, x4, and x10) and taken in PPL and XPL. Hence, there are 14 samples * 3 magnifications * 2 polarizations = 84 images in the training dataset.
 
-Classification for each sample is taken from the most popular respondent's response in Table 7. Note that the original dataset is missing Boundstone sample, hence it cannot classify a Boundstone.
+Classification for each sample is taken from the most popular respondent's response in Table 7.
 - Sample 1: Packstone
 - Sample 2: Grainstone
 - Sample 3: Wackestone