Update README.md

README.md

@@ -52,10 +52,10 @@ To preprocess the data there were three options we considered:
 - The last option was to use <a href="https://ai.googleblog.com/2020/12/mediapipe-holistic-simultaneous-face.html">MediaPipe Holistic</a> to provide pose face and hand landmarks to the ControlNet. This method was promising in theory, however, the HaGRID dataset was not suitable for this method as the Holistic model performs poorly with partial body and obscurely cropped images.
 
 We anecdotally determined that when trained at lower steps the encoded hand model performed better than the standard MediaPipe model due to implied handedness. We theorize that with a larger dataset of more full-body hand and pose classifications, Holistic landmarks will provide the best images in the future however for the moment the hand-encoded model performs best.
-This repo contain the weight of encoded hand ControlNet model
+This repo contain the weight of **Encoded hand ControlNet model**
 ## Dataset 
 
-[Dataset for Hand Encoding Mode](https://huggingface.co/datasets/MakiPan/hagrid250k-blip2)
+130k image [Dataset for Hand Encoding Mode](https://huggingface.co/datasets/MakiPan/hagrid250k-blip2)
 
 
 ## Examples