Update README.md

README.md

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 # Material Recognition and Retrieval from images dataset
 
-The goal of the dataset is zero-shot class agnostic material recognition and retrieval from images, for any material under any conditions.
-Meaning the ability to recognize materials and their state from a single example in any domain.
+The goal of the dataset is zero-shot class agnostic material recognition and retrieval from images.
+Meaning the ability to recognize if two materials in different images are the same type or state.
 
 This is fine-grained recognition of the same material and NOT classification of the material to specific set of classes.
-It include recognizing same material, or same states of a material, base on similarity (aka which material or material states are more similar for example in case where material gradually transform like rotting apple).
 
 This dataset is designed to address the general issue of one-shot material retrieval without
 restrictions on material types, settings, and environments.
-The main focus is on distinguishing between states of materials and identifying fine-grained categories such as rotten vs. ripe or coffee vs. cocoa. 
+ 
 
 The dataset is composed of two parts:
-1) large scale synthetic train set focus on materials in general setting as well inside transparent container and materials with partial similarity and gradual transitions.
-2) Real world  natural images benchmarks for one-shot material recogntion focus on materials of of any type in any setting and on materials with partial similarity and gradual transitions.
-3) atural image benchmark to test the ability to identify material states and subclasses using one or
-a few examples (Figure 1).
+1) Large scale synthetic train set focus on materials in general setting as well inside transparent container and materials with partial similarity and gradual transitions.
+2) Real world  natural images benchmarks for one-shot material recogntion focus on materials of of any type in any setting and on materials with partial similarity and gradual transitions. atural image benchmark to test the ability to identify material states and subclasses using one or
+(for example matching/distincting material states like rotten vs ripe fruit, rusty metal vs metal, dusty vs clean surfaces, wet vs dry,or  same different types of fabrics powders and liquids)
 
 # Paper: