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| from dataclasses import dataclass | |
| from enum import Enum | |
| class Model_Backbone(Enum): | |
| Original = "Original" | |
| Other = "Other" | |
| def from_str(model_backbone: str): | |
| if model_backbone == Model_Backbone.Original.value: | |
| return Model_Backbone.Original | |
| return Model_Backbone.Other | |
| def format_for_leaderboard(cls, model_backbone: str): | |
| return (cls.from_str(model_backbone), model_backbone) | |
| class Training_Dataset(Enum): | |
| XCL = "BirdSet (XCL)" | |
| XCM = "BirdSet (XCM)" | |
| Dedicated = "BirdSet (Dedicated)" | |
| Other = "other" | |
| def from_str(training_dataset: str): | |
| if training_dataset in [Training_Dataset.Dedicated.value, Training_Dataset.Dedicated.name, "BirdSet - Dedicated", "dt", "DT"]: | |
| return Training_Dataset.Dedicated | |
| if training_dataset in [Training_Dataset.XCM.value, Training_Dataset.XCM.name, "BirdSet - XCM", "mt", "MT"]: | |
| return Training_Dataset.XCM | |
| if training_dataset in [Training_Dataset.XCL.value, Training_Dataset.XCL.name, "BirdSet - XCL", "lt", "LT"]: | |
| return Training_Dataset.XCL | |
| return Training_Dataset.Other | |
| def format_for_leaderboard(cls, training_dataset: str): | |
| return (cls.from_str(training_dataset), training_dataset) | |
| class Testing_Type(Enum): | |
| AVG = "avg" | |
| PER = "per" | |
| NES = "nes" | |
| UHH = "uhh" | |
| HSN = "hsn" | |
| NBP = "nbp" | |
| SSW = "ssw" | |
| SNE = "sne" | |
| class Task: | |
| metric: str | |
| col_name: str | |
| # Select your tasks here | |
| # --------------------------------------------------- | |
| class Tasks(Enum): | |
| # metric_key in the json file, name to display in the leaderboard | |
| cmap = Task("cmap", "cmAP") | |
| auroc = Task("auroc", "AUROC") | |
| t1acc = Task("t1-acc", "T1-Acc") | |
| NUM_FEWSHOT = 0 | |
| # --------------------------------------------------- | |
| # Your leaderboard name | |
| TITLE = """<h1 align="center" id="space-title">BirdSet Leaderboard</h1>""" | |
| # What does your leaderboard evaluate? | |
| INTRODUCTION_TEXT = """ | |
| This leaderboard accompanies the [BirdSet Dataset Collection](https://huggingface.co/datasets/DBD-research-group/BirdSet). You can find out more about BirdSet in the \"About\" Tab. | |
| """ | |
| # Which evaluations are you running? how can people reproduce what you have? | |
| ABOUT_TEXT = f""" | |
| ## What is BirdSet | |
| Deep learning models have emerged as a powerful tool in avian bioacoustics to assess environmental health. | |
| To maximize the potential of cost-effective and minimal-invasive passive acoustic monitoring (PAM), models must analyze bird vocalizations across a wide range of species and environmental conditions. | |
| However, data fragmentation challenges a evaluation of generalization performance. | |
| Therefore, we introduce the BirdSet dataset, comprising approximately 520,000 global bird recordings for training and over 400 hours PAM recordings for testing in a multi-label classification setting. | |
| You can find the datasets on [Huggingface](https://huggingface.co/datasets/DBD-research-group/BirdSet) and the code on [Github](https://github.com/DBD-research-group/BirdSet). | |
| """ | |
| EVALUATION_QUEUE_TEXT = """ | |
| ## How to Submit a Model | |
| First you need to evaluate your model on the BirdSet dataset. | |
| Then you can enter your evaluation information and submit a request. | |
| We will then check your request and approve it if everything is alright. | |
| Please make sure that you model is publicly available so that we can check you results. | |
| If you want to submit an average over all datasets then choose \"AVG\" as \"Tested on\". | |
| """ | |
| CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results" | |
| CITATION_BUTTON_TEXT = r""" | |
| @misc{rauch2024birdset, | |
| title={BirdSet: A Dataset and Benchmark for Classification in Avian Bioacoustics}, | |
| author={Lukas Rauch and Raphael Schwinger and Moritz Wirth and René Heinrich and Denis Huseljic and Jonas Lange and Stefan Kahl and Bernhard Sick and Sven Tomforde and Christoph Scholz}, | |
| year={2024}, | |
| eprint={2403.10380}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.SD}, | |
| url={https://arxiv.org/abs/2403.10380}, | |
| } | |
| """ | |