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nexign_itmo's Introduction

Smart validation. Outlet check

Goal is to filter out inappropriate pictures (like a SIM image).

An image is considered to be inappropriate if a human cannot evaluate the parity by looking at it

Dataset details

Used parts of Chicago dataset (306 images)

Classes:

  1. Good
  2. Trash

Model

For this task we used pre-trained (on imageNet) ResNet50, and made fine-tuning on the custom dataset.

POSM detection and segmentation

Dataset details

Combined 2 datasets: 296297_project.tar (870 images) and 296300_project.tar (535 images)

Classes:

  1. Megafon
  2. Yota
  3. Opponents

Augmentation applied:

  • Flip: Horizontal, Vertical
  • 90° Rotate: Clockwise, Counter-Clockwise
  • Crop: 0% Minimum Zoom, 20% Maximum Zoom
  • Blur: Up to 2.5px
  • Noise: Up to 1.2% of pixels

Model

1. YOLOv8 for POSM detection

About:

Validation predicts and labels comparison

image

Metrics and losses:

image

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2. detection + Instance Segmentation

About:

Validation predicts:

val_batch1_labels_51_ce96014e2ca197ca672e

image

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nexign_itmo's People

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