This is the Udacity final project for Nanodegree - Artificial Intelligence Programming with Python
Part 1 of the project accomplishes training of a custom deep neural network classifier model on 102 category flower dataset. The incredible training computation speed is achieved by the use of CUDA GPU.
Using Torchvision's pretrained model densenet201 and my custom classifier, an accuracy of 85.166% was achieved at the time of submission.
Other models that can be used with this Image Classifier are vgg16 with an accuracy of 79.714%
and densenet121 with accuracy of 82.145%. Each of these models were run on a classifier with three hidden layers of size [120, 100, 80],
with a default dropout rate of 0.2, default learning rate of 0.003 for 10 epochs.
Further training of the model with pretrained network densenet201 and my custom classifier, for increased number of epochs showed promise. While using default learning rate and dropout, training for 15 epochs, improved validation accuracy. But, testing accuracy dropped to 84%. Retaining default learning rate and dropout values, and further training for 20 epochs resulted in the best possible accuracy of 89.252%.
Code files : Image Classifier Project.ipynb
Part 2 of the project creates a command line python application to train and predict a sample image's most probable categories.
Code files : train.py, predict.py and network_utils.py
Output files : predict_output.txt
Using the scripts created in Part 2 of the project, we can develop advanced applications that take in user input, in the form of an image and predict the most probable flower category for the image. This is a demonstration of how well our Image classifier performs in predicting fresh, unseen images of flowers. Using a checkpoint of higher accuracy will generate better results for the application user.
Below is a demonstration of prediction output for images that are not part of the "flowers" dataset. Two of the checkpoints used are RunA: densenet201 with 84% accuracy and RunB: densenet201 with 89% accuracy. RunA fails to predict images as Rose with higher probability. Clearly, RunB offers better prediction, with prediction as high as 0.99
- Windows10 local run of predict.py fails during load_checkpoint.
Fix: map_location is set to device and passed through torch.load
- Windows10 local run of predict.py fails to compare device as string.
Fix: Convert device type to str before comparing with "cpu"
- Windows10 local run of predict.py fails to print verdict for non-dataset image.
Fix: Print actual flower name and verdict only for sample image from dataset "flowers"
