| license: mit | |
| # Continually Adapt or Not (CAN) Benchmark | |
| The **CAN Benchmark** is a curated ICICLE benchmark designed to evaluate the performance of pre-trained models and support the development of adaptation algorithms in the camera trap domain. By providing a structured, temporally-split dataset, CAN enables research on continual adaptation, domain shifts, and long-term model robustness. | |
| ## Dataset Structure | |
| The dataset consists of two primary components: | |
| 1. **images/**: | |
| Contains all raw images from the camera trap dataset (CDB-D06). | |
| 2. **30/**: | |
| Contains JSON files that divide the dataset into **30-day intervals** to support continual learning evaluation: | |
| - `train.json`: Training data split by 30-day intervals | |
| - `train-all.json`: All training data combined | |
| - `test.json`: Test data split by 30-day intervals | |
| This setup allows researchers to simulate real-world temporal data streams in camera trap applications. | |
| ## How to Use | |
| Clone or download the dataset using: | |
| ```bash | |
| git lfs install | |
| git clone https://huggingface.co/datasets/ICICLE-AI/CAN_Benchmark | |
| # Unzip the provided archive | |
| unzip CAN_Benchmark/CDB_D06.zip -d CAN_Benchmark/data | |
| ``` | |
| You will get the following structure: | |
| ``` | |
| CAN_Benchmark/ | |
| ├── data/ | |
| │ ├── images/ | |
| │ └── 30/ | |
| │ ├── train.json | |
| │ ├── train-all.json | |
| │ └── test.json | |
| ``` |