metadata
library_name: transformers
tags: []
ConvNext (trained on XCL from BirdSet)
ConvNext trained on the XCL dataset from BirdSet, covering 9736 bird species from Xeno-Canto. Please refer to the BirdSet Paper and the BirdSet Repository for further information.
Model Details
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them.
How to use
The BirdSet data needs a custom processor that is available in the BirdSet repository. The model does not have a processor available.
The model accepts a mono image (spectrogram) as input (e.g., torch.Size([16, 1, 128, 1024])
)
- The model is trained on 5-second clips of bird vocalizations.
- num_channels: 1
- pretrained checkpoint: facebook/convnext-base-224-22k
- sampling_rate: 32_000
- normalize spectrogram: mean: -4.268, std: 4.569 (from esc-50)
- spectrogram: n_fft: 1024, hop_length: 320, power: 2.0
- melscale: n_mels: 128, n_stft: 513
- dbscale: top_db: 80
import torch
from transformers import AutoModelForImageClassification
from datasets import load_dataset
dataset = load_dataset("DBD-research-group/BirdSet", "HSN")
Model Source
- Repository: BirdSet Repository
- Paper [optional]: BirdSet Paper