Terrifie/NACTI_Species_Recognition_model

NACTI Species Recognition Models

This repository contains the pretrained PyTorch model checkpoints associated with our paper:

Benchmarking NACTI Species Recognition in Long-Tailed Regimes Zehua Liu and Tilo Burghardt https://arxiv.org/abs/2607.18033

The repository provides the model weights for the different training configurations evaluated in the paper.

Model Repository

Hugging Face repository: https://huggingface.co/Terrifie/NACTI_Species_Recognition_model

The original training and inference code is available in the project repository:

Species-Classification: https://github.com/ZehuaLiuY/Species-Classification

For details about the model architecture, dataset, training procedure, and evaluation protocol, please refer to the original project and associated paper.

Available Checkpoints

The repository currently provides the following model configurations:

Directory	Description
CE_Adam	Cross-Entropy loss + Adam optimizer
CE_AdamW	Cross-Entropy loss + AdamW optimizer
CE_AdamW_sc	Cross-Entropy loss + AdamW optimizer + scheduler
FL_AdamW	Focal Loss + AdamW optimizer
FL_AdamW_sc	Focal Loss + AdamW optimizer + scheduler
LDAM	Label-Distribution-Aware Margin loss
LDAM_sc	Label-Distribution-Aware Margin loss + scheduler
WCE_AdamW	Weighted Cross-Entropy loss + AdamW optimizer
WCE_AdamW_sc	Weighted Cross-Entropy loss + AdamW optimizer + scheduler

Naming Convention

Model directories follow the naming convention:

<loss>_<optimizer>[_<scheduler>]

The scheduler component is optional.

For example:

CE_Adam

indicates a model trained with Cross-Entropy loss and the Adam optimizer without a learning-rate scheduler.

CE_AdamW_sc

indicates a model trained with Cross-Entropy loss and the AdamW optimizer with a learning-rate scheduler.

The suffix sc indicates that a scheduler was used during training.

Baseline Model

The CE_Adam configuration is the baseline model.

It uses:

• Loss: Cross-Entropy (CE)
• Optimizer: Adam
• Scheduler: None

The other checkpoints represent alternative training configurations that can be compared against this baseline.

Checkpoint Files

Depending on the training configuration, each directory may contain one or more PyTorch checkpoint files, such as:

best_model.pth
final_model.pth
best_model_ddp.pth
final_model_ddp.pth

best_model

The checkpoint corresponding to the best-performing model observed during training according to the training/evaluation procedure.

final_model

The checkpoint saved at the end of training.

*_ddp.pth

These checkpoints were produced using Distributed Data Parallel (DDP) training.

The exact loading procedure may therefore depend on how the model was trained and implemented in the original project.

Reproducibility

This repository provides pretrained model weights corresponding to the training configurations used in the NACTI Species Recognition project.

For complete reproducibility, including model architecture, dataset preparation, training code, and evaluation procedures, please refer to the original project repository and associated publication.

Related Resources

• Original GitHub repository: https://github.com/ZehuaLiuY/Species-Classification

• Hugging Face model repository: https://huggingface.co/Terrifie/NACTI_Species_Recognition_model

License

The model weights in this repository are released under the MIT License.

Please also refer to the license and terms associated with the original project and its training data before redistributing or using the models in downstream applications.

Citation

If you use these pretrained models in your research, please cite the original NACTI Species Recognition work:

@conference{visapp26,
author={Zehua Liu and Tilo Burghardt},
title={Long-Tailed Species Recognition in the NACTI Wildlife Dataset},
booktitle={Proceedings of the 21st International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP},
year={2026},
pages={357-365},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014342200004084},
isbn={978-989-758-804-4},
}

Please replace the citation above with the official bibliographic information from the original publication.