You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
About: Used Merge model developed by Tanti et al. in 2017 as a refereance for the base model for image captioning
Dataset: Acquired dataset from kaggle : Flicker8k
Here i used Tensorflow tokenizers to get a word dictionary for encoding and de-coding for sequence processing.
Used Tensorflow library for preprocessing the text data.
For giving the image input to the model, I used various different architecture's for transfer learning.
Got the best loss by using VGG16. Rather then using cool architecture focused to solve the business problem with the best solution possible.
The model used for input of the processed data as:
About
Created Model for extracting features from images using LSTM, CNN and Transfer l earning. To give the required information of the image in the form of caption