We are provided with a image with specific dimensions, where the model should recommend similar images of user defined N categories.
Link: https://drive.google.com/file/d/1o3T91VBTdcVqKRi5zEilY-FtQeu05I-A/view?usp=sharing
- The dataset provided consists of photos of various animals such as lion, tiger, cheetah,..etc.
- This dataset consists of 4738 images
This file consists of all the features extracted from the dataset for the respective images. To save some time I have provided the link to the processed data or else we can run the code from the above file to generate the csv file.
Link: https://drive.google.com/file/d/1XCSxWIdVP-vJQ7QOH88x_7AcoZjJv1NV/view?usp=sharing
- sklearn
- numpy
- matplotlib
- tensorflow==2.2.0
- tqdm
The logic and the pipeline has been explained thoroughly in the Ipython notebook and inorder to just view it, I also provided a pdf and html format of the notebook for reference.
[!]Note: Somehow github doesn't display the Ipython notebooks markdown images of the architecture online when we view the ipynb file but can be seen when downloaded. Although the pipeline images can be seen in the pdf and the html fomat of the program.