Code of the lileonardo team for the 2021 Emotion and Theme Recognition in Music task of MediaEval 2021

Overview

Emotion and Theme Recognition in Music

The repository contains code for the submission of the lileonardo team to the 2021 Emotion and Theme Recognition in Music task of MediaEval 2021 (results).

Requirements

  • python >= 3.7
  • pip install -r requirements.txt in a virtual environment
  • Download data from the MTG-Jamendo Dataset in data/jamendo. Audio files go to data/jamendo/mp3 and melspecs to data/jamendo/melspecs.
  • Process 128 bands mel spectrograms and store them in data/jamendo/melspecs2 by running:
    python preprocess.py experiments/preprocessing/melspecs2.json

Usage

Run python main.py experiments/DIR where DIR contains the parameters.

Parameters are overridable by command line arguments:

python main.py --help
usage: main.py [-h] [--data_dir DATA] [--num_workers NUM] [--restart_training] [--restore_name NAME]
               [--num_epochs EPOCHS] [--learning_rate LR] [--weight_decay WD] [--dropout DROPOUT]
               [--batch_size BS] [--manual_seed SEED] [--model MODEL] [--loss LOSS]
               [--calculate_stats]
               DIRECTORY

Train according to parameters in DIRECTORY

positional arguments:
  DIRECTORY            path of the directory containing parameters

optional arguments:
  -h, --help           show this help message and exit
  --data_dir DATA      path of the directory containing data (default: data)
  --num_workers NUM    number of workers for dataloader (default: 4)
  --restart_training   overwrite previous training (default is to resume previous training)
  --restore_name NAME  name of checkpoint to restore (default: last)
  --num_epochs EPOCHS  override number of epochs in parameters
  --learning_rate LR   override learning rate
  --weight_decay WD    override weight decay
  --dropout DROPOUT    override dropout
  --batch_size BS      override batch size
  --manual_seed SEED   override manual seed
  --model MODEL        override model
  --loss LOSS          override loss
  --calculate_stats    recalculate mean and std of data (default is to calculate only when they
                       don't exist in parameters)

Ensemble predictions

The predictions are averaged by running:

python average.py --outputs experiments/convs-m96*/predictions/test-last-swa-outputs.npy --targets experiments/convs-m96*/predictions/test-last-swa-targets.npy --preds_path predictions/convs.npy
python average.py --outputs experiments/filters-m128*/predictions/test-last-swa-outputs.npy --targets experiments/filters-m128*/predictions/test-last-swa-targets.npy --preds_path predictions/filters.npy
python average.py --outputs predictions/convs.npy predictions/filters.npy --targets predictions/targets.npy
Owner
Vincent Bour
Vincent Bour
TargetAllDomainObjects - A python wrapper to run a command on against all users/computers/DCs of a Windows Domain

TargetAllDomainObjects A python wrapper to run a command on against all users/co

Podalirius 19 Dec 13, 2022
Car Price Predictor App used to predict the price of the car based on certain input parameters created using python's scikit-learn, fastapi, numpy and joblib packages.

Pricefy Car Price Predictor App used to predict the price of the car based on certain input parameters created using python's scikit-learn, fastapi, n

Siva Prakash 1 May 10, 2022
All materials of Cassandra Event, Udyam'22

Cassandra 2022 Workspace Workshop Materials Workshop-1 Workshop-2 Workshop-3 Workshop-4 Assignments Assignment-1 Assignment-2 Assignment-3 Resources P

36 Dec 31, 2022
4D Human Body Capture from Egocentric Video via 3D Scene Grounding

4D Human Body Capture from Egocentric Video via 3D Scene Grounding [Project] [Paper] Installation: Our method requires the same dependencies as SMPLif

Miao Liu 37 Nov 08, 2022
pytorch implementation of the ICCV'21 paper "MVTN: Multi-View Transformation Network for 3D Shape Recognition"

MVTN: Multi-View Transformation Network for 3D Shape Recognition (ICCV 2021) By Abdullah Hamdi, Silvio Giancola, Bernard Ghanem Paper | Video | Tutori

Abdullah Hamdi 64 Jan 03, 2023
Generate fine-tuning samples & Fine-tuning the model & Generate samples by transferring Note On

UPMT Generate fine-tuning samples & Fine-tuning the model & Generate samples by transferring Note On See main.py as an example: from model import PopM

7 Sep 01, 2022
Introduction to Statistics and Basics of Mathematics for Data Science - The Hacker's Way

HackerMath for Machine Learning “Study hard what interests you the most in the most undisciplined, irreverent and original manner possible.” ― Richard

Amit Kapoor 1.4k Dec 22, 2022
This repository contains the code for the ICCV 2019 paper "Occupancy Flow - 4D Reconstruction by Learning Particle Dynamics"

Occupancy Flow This repository contains the code for the project Occupancy Flow - 4D Reconstruction by Learning Particle Dynamics. You can find detail

189 Dec 29, 2022
Memory-Augmented Model Predictive Control

Memory-Augmented Model Predictive Control This repository hosts the source code for the journal article "Composing MPC with LQR and Neural Networks fo

Fangyu Wu 1 Jun 19, 2022
A simple but complete full-attention transformer with a set of promising experimental features from various papers

x-transformers A concise but fully-featured transformer, complete with a set of promising experimental features from various papers. Install $ pip ins

Phil Wang 2.3k Jan 03, 2023
From this paper "SESNet: A Semantically Enhanced Siamese Network for Remote Sensing Change Detection"

SESNet for remote sensing image change detection It is the implementation of the paper: "SESNet: A Semantically Enhanced Siamese Network for Remote Se

1 May 24, 2022
This repo. is an implementation of ACFFNet, which is accepted for in Image and Vision Computing.

Attention-Guided-Contextual-Feature-Fusion-Network-for-Salient-Object-Detection This repo. is an implementation of ACFFNet, which is accepted for in I

5 Nov 21, 2022
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks

StackGAN Pytorch implementation Inception score evaluation StackGAN-v2-pytorch Tensorflow implementation for reproducing main results in the paper Sta

Han Zhang 1.8k Dec 21, 2022
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging

Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging This repository contains an implementation

Computational Photography Lab @ SFU 1.1k Jan 02, 2023
Some methods for comparing network representations in deep learning and neuroscience.

Generalized Shape Metrics on Neural Representations In neuroscience and in deep learning, quantifying the (dis)similarity of neural representations ac

Alex Williams 45 Dec 27, 2022
HashNeRF-pytorch - Pure PyTorch Implementation of NVIDIA paper on Instant Training of Neural Graphics primitives

HashNeRF-pytorch Instant-NGP recently introduced a Multi-resolution Hash Encodin

Yash Sanjay Bhalgat 616 Jan 06, 2023
Scikit-event-correlation - Event Correlation and Forecasting over High Dimensional Streaming Sensor Data algorithms

scikit-event-correlation Event Correlation and Changing Detection Algorithm Theo

Intellia ICT 5 Oct 30, 2022
official code for dynamic convolution decomposition

Revisiting Dynamic Convolution via Matrix Decomposition (ICLR 2021) A pytorch implementation of DCD. If you use this code in your research please cons

Yunsheng Li 110 Nov 23, 2022
[CVPR 2021] Pytorch implementation of Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs

Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs In this work, we propose a framework HijackGAN, which enables non-linear latent space travers

Hui-Po Wang 46 Sep 05, 2022
Python implementation of Wu et al (2018)'s registration fusion

reg-fusion Projection of a central sulcus probability map using the RF-ANTs approach (right hemisphere shown). This is a Python implementation of Wu e

Dan Gale 26 Nov 12, 2021