|
--- |
|
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](https://arxiv.org/pdf/2403.10380) and the |
|
[BirdSet Repository](https://github.com/DBD-research-group/BirdSet/tree/main) 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, 343])`) |
|
|
|
- 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 |
|
|
|
```python |
|
import torch |
|
from transformers import AutoModelForImageClassification |
|
from datasets import load_dataset |
|
|
|
dataset = load_dataset("DBD-research-group/BirdSet", "HSN") |
|
``` |
|
|
|
## Model Source |
|
- **Repository:** [BirdSet Repository](https://github.com/DBD-research-group/BirdSet/tree/main) |
|
- **Paper [optional]:** [BirdSet Paper](https://arxiv.org/pdf/2403.10380) |
|
|
|
## Citation |
