Update README.md

README.md

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 license: apache-2.0
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+Here we create two datasets (from existing datasets: CLEVRER, VisualGenome) for the Object Counting instruction tuning task.
+
+### CLEVRER
+
+CLEVRER has QA pairs for each 5000 training video 
+```json
+{'video_filename': int, 'scene_index': str (same as filename), 'questions': list [{'question_type': , 'question_subtype': , 'question_text': , 'answer_text': , 'program'(question attributes): }]} 
+```
+We select 'descriptive' type, 'count' subtype questions, they are object counting. 
+CLEVRER contains both positive questions and negative (non-exist) questions, so no need to construct negative samples.
+some questions are 'event' specific, counting moving/stationary objects when a certain event happens. i.e., 'How many objects are stationary when the yellow object enters the scene?'
+downloading videos: http://clevrer.csail.mit.edu/
+
+
+### VisualGenome
+
+We generate some negative questions for non-exist objects in the image. We use the version 1 image sets. Download from: https://homes.cs.washington.edu/~ranjay/visualgenome/api.html
+VisualGenome has 100K+ images. And for the objects in the image, there are attributes, We only focus on the color attributes.
+There are in total 11K+ possible objects. for each image, I add 3 non-exist objects and 1 non-exist attribute for existing objects as negative samples. 
+In the original qa dataset, VG has Object Counting questions, we also include them here, with the 'orig_qa'=='Yes'. For those negative questions we generated, 'orig_qa' =='No'.
+```json
+{'img_id': str, 'orig_qa': Yes/No, 'question_text': 'How many <attribute> <object in plural form> are there? ', 'answer_text': Numbers. or None. }
+```
+For more details, plz refer to the dataset. 
+
+
+