Suggestions to Make Your Project Stand Out!
ierolsen opened this issue · 0 comments
1. AUGMENT THE TRAINING DATA
Augmenting the training and/or validation set might help improve model performance.
2. TURN YOUR ALGORITHM INTO A WEB APP
Turn your code into a web app using Flask!
3. OVERLAY DOG EARS ON DETECTED HUMAN HEADS
Overlay a Snapchat-like filter with dog ears on detected human heads. You can determine where to place the ears through the use of the OpenCV face detector, which returns a bounding box for the face. If you would also like to overlay a dog nose filter, some nice tutorials for facial keypoints detection exist here.
4. ADD FUNCTIONALITY FOR DOG MUTTS
Currently, if a dog appears 51% German Shephard and 49% poodle, only the German Shephard breed is returned. The algorithm is currently guaranteed to fail for every mixed breed dog. Of course, if a dog is predicted as 99.5% Labrador, it is still worthwhile to round this to 100% and return a single breed; so, you will have to find a nice balance.
5. EXPERIMENT WITH MULTIPLE DOG/HUMAN DETECTORS
Perform a systematic evaluation of various methods for detecting humans and dogs in images. Provide improved methodology for the face_detector and dog_detector functions.