So, me Shubhamai and I have come up with these 3 things -
COCO Dataset & using Detectron2, MMDetection
YES! I have converted this dataset into COCO Dataset and which we train Mask-RCNN using Detectron2.
There we go boys - Colab Link
More things will be added so like this post RIGHT NOW 
Web Application & Visualisation
https://seismic-facies-identification.herokuapp.com/
But this time, I found that a great preprocessing pipeline can help to model to find accurate features and increasing overall accuracy. But it kinda isn’t that easy as it looks —
So I made a Web Application based on that which allows you to play/experiment with many of the image preprocessing functions/methods, changing parameters or writing custom image preprocessing functions to experiment.
And it also contains all the visualizations from the colab notebook .
I hope that it will help you in making the perfect preprocessing pipelines 
.
End-to-End Baseline & Tensorflow
https://colab.research.google.com/drive/1t1hF_Vs4xIyLGMw_B9l1G6qzLBxLB5eG?usp=sharing
I have made a complete colab notebook from Data Exploration to Submitting Predictions. Here are some of the glimpse of the image visualization section!
And this 3D Plot!
Tables of Content -
- Setting our Workspace
- Data Exploration
- Image Preprocessing Techniqes
- Creating our Dataset
- Creating our Model
- Training the Model
- Evaluating the model
- Testing on test Data
- Generate More Data + Some tips & tricks
The main libraries covered in this notebook is —
- Tensorflow 2.0 & Keras
- Plotly
- cv2
and much more…
The model that i am using is UNet, pretty much standard in image segmentation. More is in the colab notebook!
I hope the colab notebook will help you get started in this competition or learning something new 
. If the notebook did help you, make sure to like the post. lol.
https://colab.research.google.com/drive/1t1hF_Vs4xIyLGMw_B9l1G6qzLBxLB5eG?usp=sharing
Please like the topic if this helps in any way possible 
. I really appreciate that 