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---
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
``` |