My image dataset now has 9 field sports - and I am into the low 90’s with resnet50 and a little fine tuning (as per lesson 1). The confusions makes sense - not sure why some are pretty symmetric (Aussie rules and rugby) and others not (cricket v baseball). To improve the accuracy I’m thinking more data and some image curation. Any other ideas? Differentiating what people are doing is certainly harder than pure identity.
[(‘cricket’, ‘baseball’, 8),
(‘aussie’, ‘rugby’, 7),
(‘rugby’, ‘aussie’, 5),
(‘rugby’, ‘soccer’, 5),
(‘soccer’, ‘rugby’, 5),
(‘athletics’, ‘fieldhockey’, 4),
(‘aussie’, ‘soccer’, 4),
(‘cricket’, ‘aussie’, 3),
(‘lacrosse’, ‘fieldhockey’, 3),
(‘soccer’, ‘cricket’, 3),
(‘athletics’, ‘aussie’, 2),
(‘aussie’, ‘baseball’, 2),
(‘fieldhockey’, ‘soccer’, 2),
(‘football’, ‘cricket’, 2),
(‘football’, ‘lacrosse’, 2),
(‘football’, ‘rugby’, 2),
(‘football’, ‘soccer’, 2),
(‘lacrosse’, ‘athletics’, 2),
(‘lacrosse’, ‘football’, 2),
(‘rugby’, ‘fieldhockey’, 2)]