Approach 1

Overall

For each lion's class, extract samples, build a dataset and train a convnet. Then search and count each lion's class findings on target images using a sliding window.

Steps

1. Explore and validate dataset (dotted samples vs csv counting)
2. Export lion samples to dataset
   • For each image on training folder:
     1. Find dots by class (through dot color)
     2. Export 124x124 images centered on dots to a dataset, labeled with lion's class (1-5) in one hot encoding
        • Ideas: ignore lions that are too near each other to minimize data noise?
3. Train dataset to classify lion classes
4. Test: Find and count lion classes on train images and compare to csv class count
   1. Generate object candidates (lets avoid a full sliding window for search)
   2. For each object candidate, predict for a lion class. If good, count class match. Avoid recount overlappings.
   3. Show error count by class and generate RMSE
5. Generate submission file