Added info about preparation and training

Home.md

@@ -6,3 +6,14 @@ General Idea of the Thesis:
 4. a) Feed SSD with 2D data for object. Classify object and calculate classification loss (cross entropy loss).
    b) Feed autoencoder with 2D data for object. Calculate encoding/decoding loss.
 5. In testing phase the encoding/decoding loss tells us if an object is unknown.
+
+Preparation:
+
+1. Clean dataset with dataset_cleaner.py developed in master project
+2. specify train/validate/test split
+3. reorganize dataset inside these splits according to object class rather than movie/frame (metadata only, no duplicate dataset files!)
+4. specify inlier and outlier object classes
+
+Training:
+
+**Independent** training of SSD and Adversarial Autoencoder (AAE). Use ground truth bounding box for AAE.