|
--- |
|
license: apache-2.0 |
|
--- |
|
# Classifier architecture |
|
The classifier uses DenseNet161 as the encoder and some linear layers at classifier base. |
|
|
|
# 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 |
|
|
|
|
|
# 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. |
|
|
|
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 |
|
|
|
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 |
|
|