Dog Breed Classifier
Files Submitted
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Submission Files |
The submission includes all required files. |
Step 1: Detect Humans
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Question 1: Assess the Human Face Detector |
The submission returns the percentage of the first 100 images in the dog and human face datasets with a detected human face. |
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Question 2: Assess the Human Face Detector |
The submission opines whether Haar cascades for face detection are an appropriate technique for human detection. |
Step 2: Detect Dogs
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Question 3: Assess the Dog Detector |
The submission returns the percentage of the first 100 images in the dog and human face datasets with a detected dog. |
Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
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Model Architecture |
The submission specifies a CNN architecture. |
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Train the Model |
The submission specifies the number of epochs used to train the algorithm. |
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Test the Model |
The trained model attains at least 1% accuracy on the test set. |
Step 5: Create a CNN to Classify Dog Breeds
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Obtain Bottleneck Features |
The submission downloads the bottleneck features corresponding to one of the Keras pre-trained models (VGG-19, ResNet-50, Inception, or Xception). |
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Model Architecture |
The submission specifies a model architecture. |
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Question 5: Model Architecture |
The submission details why the chosen architecture succeeded in the classification task and why earlier attempts were not as successful. |
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Compile the Model |
The submission compiles the architecture by specifying the loss function and optimizer. |
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Train the Model |
The submission uses model checkpointing to train the model and saves the model weights with the best validation loss. |
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Load the Model with the Best Validation Loss |
The submission loads the model weights that attained the least validation loss. |
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Test the Model |
Accuracy on the test set is 60% or greater. |
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Predict Dog Breed with the Model |
The submission includes a function that takes a file path to an image as input and returns the dog breed that is predicted by the CNN. |
Step 6: Write Your Algorithm
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Write your Algorithm |
The submission uses the CNN from Step 5 to detect dog breed. The submission has different output for each detected image type (dog, human, other) and provides either predicted actual (or resembling) dog breed. |
Step 7: Test Your Algorithm
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Test Your Algorithm on Sample Images! |
The submission tests at least 6 images, including at least two human and two dog images. |
Tips to make your project standout:
(Presented in no particular order …)
(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 or web.py!
(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.