Experimenting with computer vision techniques to generate annotated image datasets from gameplay recordings automatically.

Last update: Apr 24, 2022

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Overview

Self-learning dataset generation:

# class 0 for T or 1 for CT
python vid.py --vid ./videos/t_model_0.mp4 --cls 0 --ml True

Train on generated cs:go dataset with:

python yolov5/train.py --img 640 --batch 10 --epochs 500 --data dataset.yaml --weights yolov5/yolov5s.pt

Inference:

python yolov5/detect.py --img 640 --conf 0.2  --weights ./data/weights/best.pt --source ./data/images

Inference on live gameplay (WIP):

python detect.py

Owner

Martin Valchev

University of Southampton Graduate w/ First-Class Honours in BEng Software Engineering

GitHub Repository

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Experimenting with computer vision techniques to generate annotated image datasets from gameplay recordings automatically.

Related tags

Overview

Self-learning dataset generation:

Train on generated cs:go dataset with:

Inference:

Inference on live gameplay (WIP):

Owner

Martin Valchev

Official public repository of paper "Intention Adaptive Graph Neural Network for Category-Aware Session-Based Recommendation"

Code for "Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification", ECCV 2020 Spotlight

Pretrained models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet.

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Predicting lncRNA–protein interactions based on graph autoencoders and collaborative training

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SNIPS: Solving Noisy Inverse Problems Stochastically

Deep Networks with Recurrent Layer Aggregation

Building a real-time environment using webcam frame division in OpenCV and classify cropped images using a fine-tuned vision transformers on hybryd datasets samples for facial emotion recognition.

The Turing Change Point Detection Benchmark: An Extensive Benchmark Evaluation of Change Point Detection Algorithms on real-world data

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