readme: ---
  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
  