I'm using NVIDIA DIGITS 3.0 to do training for detecting direction of a ball throw.
Dataset contains 400+ binary images each for left and right throw with the following specs in DIGITS:
- Image Type: Grayscale, JPG
- Image Size: 256 x 256
- Resize Transformation: Fill
Classification Model specs:
- Solver Type: NAG
- Networks: GoogLeNet
- The rest are default values
I separated out 8 images (4 for left, 4 for right) from my 400+ training dataset to do testing on my model. The results returned from my classification model always classifies all 8 are left throws at 90%+ accuracy even though 4 of which are clearly right throws.
2 of the test images are shown below. My subject always stands at the same position to throw the ball, which is more or less constant. I've tried to change various parameters in the classification model like no. of epochs, subtract image mean, etc. to no avail.
In a previous dataset, my model classifies all throws as right throw, similarly only one direction is detected. I'm unsure whether the problem lies in my dataset images or the training model. I would greatly appreciate any advice.