https://github.com/viig99/mixmatch-freesound
Multi label audio classification using mixmatch & a noisy loss
https://github.com/viig99/mixmatch-freesound
audio-classification freesound mix-match
Last synced: 10 days ago
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Multi label audio classification using mixmatch & a noisy loss
- Host: GitHub
- URL: https://github.com/viig99/mixmatch-freesound
- Owner: viig99
- License: mit
- Created: 2019-06-01T03:47:17.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2019-06-20T10:35:09.000Z (almost 7 years ago)
- Last Synced: 2025-11-20T01:06:11.521Z (7 months ago)
- Topics: audio-classification, freesound, mix-match
- Language: Jupyter Notebook
- Homepage:
- Size: 10.8 MB
- Stars: 4
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# FreeSound 2019 MixMatch + SpecAugment + Noisy
This repository tries to solve the multi-label freesound classification using [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://arxiv.org/abs/1905.02249) and [SpecAugment](https://arxiv.org/abs/1904.08779).
Dataset is using the freesound - 2019 kaggle competion.
## Requirements
- Python 3.6+
- PyTorch 1.1
- **torchvision 0.3.0 (older versions are not compatible with this code)**
- tensorboardX
- progress
- matplotlib
- numpy
- librosa
## Usage
### Train
Train the model by using the freesound 2019 curated and noisy data.
```
./train.sh
```
### Monitoring training progress
```
tensorboard --logdir=./result
```
## Results (Accuracy)
0.856 lwlwrap using 5% test set till now.
## References
```
@article{berthelot2019mixmatch,
title={MixMatch: A Holistic Approach to Semi-Supervised Learning},
author={Berthelot, David and Carlini, Nicholas and Goodfellow, Ian and Papernot, Nicolas and Oliver, Avital and Raffel, Colin},
journal={arXiv preprint arXiv:1905.02249},
year={2019}
}
```