The goal of this project was Image Recognition/Classification. Experiments were done on the 15 scene dataset.
The process is split into three parts -
The baseline results (picking randomly) results in ~7% accuracy.
Category name | Accuracy | Sample training images | Sample true positives | False positives with true label | False negatives with wrong predicted label | ||||
---|---|---|---|---|---|---|---|---|---|
Kitchen | 0.550 | LivingRoom |
Store |
InsideCity |
LivingRoom |
||||
Store | 0.500 | Street |
Highway |
Mountain |
LivingRoom |
||||
Bedroom | 0.350 | LivingRoom |
Kitchen |
Office |
Office |
||||
LivingRoom | 0.310 | Store |
Bedroom |
Suburb |
Bedroom |
||||
Office | 0.880 | Store |
LivingRoom |
Store |
LivingRoom |
||||
Industrial | 0.450 | Kitchen |
TallBuilding |
Store |
TallBuilding |
||||
Suburb | 0.910 | InsideCity |
InsideCity |
TallBuilding |
Mountain |
||||
InsideCity | 0.450 | Store |
Store |
Industrial |
Street |
||||
TallBuilding | 0.760 | Bedroom |
InsideCity |
Bedroom |
Coast |
||||
Street | 0.760 | InsideCity |
InsideCity |
Suburb |
Mountain |
||||
Highway | 0.750 | OpenCountry |
Industrial |
Industrial |
OpenCountry |
||||
OpenCountry | 0.430 | Suburb |
Industrial |
Mountain |
Coast |
||||
Coast | 0.800 | OpenCountry |
Highway |
OpenCountry |
OpenCountry |
||||
Mountain | 0.770 | Store |
Forest |
OpenCountry |
Industrial |
||||
Forest | 0.890 | Store |
Mountain |
Suburb |
Mountain |
||||
Category name | Accuracy | Sample training images | Sample true positives | False positives with true label | False negatives with wrong predicted label |
Vocabulary Size | Accuracy (%) |
---|---|
10 | 40.9 |
50 | 57.3 |
100 | 60.4 |
200 | 63.7 |
400 | 66.7 |
1000 | 67.4 |
10000 | 62.2 |
Lambda | Accuracy (%) |
---|---|
0.1 | 42.1 |
0.01 | 49.1 |
0.005 | 54.9 |
0.001 | 60.7 |
0.0005 | 62.5 |
0.0001 | 63.7 |
0.00001 | 59.2 |