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| license: apache-2.0 |
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| # Classifier architecture |
| The classifier uses DenseNet161 as the encoder and some linear layers at classifier base. |
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| # Model accuracy: |
| Model achieves 91.3% accuracy on the validation set. \ |
| F1-score per class: {'digital': 0.9873773235685747, 'hard': 0.9338602782753218, 'soft': 0.8444277483052108} \ |
| Mean F1-score: 0.9218884500497024 \ |
| Accuracy: 0.913 |
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| # Training dataset metadata: |
| 1. Dataset classes: ['soft', 'digital', 'hard'] |
| 2. Number of classes: 3 |
| 3. Total number of images: 18415 |
| # Number of images per class: |
| - soft : 5482 |
| - digital : 1206 |
| - hard : 11727 |
| # Classes description: |
| 1. The **hard** class denotes a group of scenes to which a coarser background removal method should be applied, intended for objects with an edge without small details. |
| The hard class contains the following categories of objects: |
| object, laptop, charger, pc mouse, pc, rocks, table, bed, box, sneakers, ship, wire, guitar, fork, spoon, plate, keyboard, car, bus, screwdriver, ball, door, flower, clocks, fruit , food, robot. |
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| 2. The **soft** class denotes a group of scenes to which you want to apply a soft background removal method intended for people, hair, clothes, and other similar types of objects. The soft class contains the following categories of objects: |
| animal, people, human, man, woman, t-shirt, hairs, hair, dog, cat, monkey, cow, medusa, clothes |
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| 3. The **digital** class denotes a group of images with digital graphics, such as screenshots, logos, and so on. |
| The digital class contains the following categories of scenes: |
| screenshot |
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