DBD-research-group/ConvNeXT-Base-BirdSet-XCL

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

Citation