BIOSCAN-5M

Figure 1: A BIOSCAN-5M dataset sample with multimodal data types.

Overview

This repository contains the code and data related to the BIOSCAN-5M project. BIOSCAN-5M is a comprehensive multi-modal dataset comprised of over 5 million specimens, 98% of which are insects. Every record has both image and DNA data.

If you make use of the BIOSCAN-5M dataset and/or this code repository, please cite the following paper:

@misc{gharaee2024bioscan5m,
    title={{BIOSCAN-5M}: A Multimodal Dataset for Insect Biodiversity},
    author={Zahra Gharaee and Scott C. Lowe and ZeMing Gong and Pablo Millan Arias
        and Nicholas Pellegrino and Austin T. Wang and Joakim Bruslund Haurum
        and Iuliia Zarubiieva and Lila Kari and Dirk Steinke and Graham W. Taylor
        and Paul Fieguth and Angel X. Chang
    },
    year={2024},
    eprint={2406.12723},
    archivePrefix={arXiv},
    primaryClass={cs.LG},
    doi={10.48550/arxiv.2406.12723},
}

Dataset

We present BIOSCAN-5M dataset to the machine learning community. We hope this dataset will facilitate the development of tools to automate aspects of the monitoring of global insect biodiversity.

Each record of the BIOSCAN-5M dataset contains six primary attributes:

• RGB image
  • Metadata field: processid
• DNA barcode sequence
  • Metadata field: dna_barcode
• Barcode Index Number (BIN)
  • Metadata field: dna_bin
• Biological taxonomic classification
  • Metadata fields: phylum, class, order, family, subfamily, genus, species
• Geographical information
  • Metadata fields: country, province_state, latitude, longitude
• Specimen size
  • Metadata fields: image_measurement_value, area_fraction, scale_factor

Dataset Access

All dataset image packages and metadata files are accessible for download through the GoogleDrive folder. Additionally, the dataset is available on Zenodo, Kaggle, and HuggingFace.

Copyright and License

The images and metadata included in the BIOSCAN-5M dataset available through this repository are subject to copyright and licensing restrictions shown in the following:

• Copyright Holder: CBG Photography Group
• Copyright Institution: Centre for Biodiversity Genomics (email:[email protected])
• Photographer: CBG Robotic Imager
• Copyright License: Creative Commons Attribution 3.0 Unported (CC BY 3.0)
• Copyright Contact: [email protected]
• Copyright Year: 2021

Getting Started with BIOSCAN-5M

To set up the BIOSCAN-5M project, use the provided bioscan5m.yaml file to install all required packages by running:

conda env create -f bioscan5m.yaml

Please note that to work with all modules connected to this repository, you may need to install additional dependencies specific to each module (if any). Be sure to follow the instructions provided within each module's folder for further setup details.

Metadata

The dataset metadata file BIOSCAN_5M_Insect_Dataset_metadata contains biological information, geographic information as well as size information of the organisms. We provide this metadata in both CSV and JSONLD file types.

RGB Image

The BIOSCAN-5M dataset comprises resized and cropped images. We have provided various packages of the BIOSCAN-5M dataset, each tailored for specific purposes.

Figure 2: Examples of the original images of the BIOSCAN-5M dataset.

Cropped images

We trained a model on examples from this dataset in order to create a tool introduced in BIOSCAN-1M, which can automatically generate bounding boxes around the insect. We used this to crop each image down to only the region of interest.

Image packages

• BIOSCAN_5M_original_full: The raw images of the dataset.
• BIOSCAN_5M_cropped: Images after cropping with our cropping tool.
• BIOSCAN_5M_original_256: Original images resized to 256 on their shorter side.
• BIOSCAN_5M_cropped_256: Cropped images resized to 256 on their shorter side.

BIOSCAN_5M_original_full	BIOSCAN_5M_cropped
BIOSCAN_5M_original_full.01.zip BIOSCAN_5M_original_full.02.zip BIOSCAN_5M_original_full.03.zip BIOSCAN_5M_original_full.04.zip BIOSCAN_5M_original_full.05.zip	BIOSCAN_5M_cropped.01.zip BIOSCAN_5M_cropped.02.zip
BIOSCAN_5M_original_256	BIOSCAN_5M_cropped_256
BIOSCAN_5M_original_256.zip BIOSCAN_5M_original_256_pretrain.zip BIOSCAN_5M_original_256_train.zip BIOSCAN_5M_original_256_eval.zip	BIOSCAN_5M_cropped_256.zip BIOSCAN_5M_cropped_256_pretrain.zip BIOSCAN_5M_cropped_256_train.zip BIOSCAN_5M_cropped_256_eval.zip

Geographical Information

The BIOSCAN-5M dataset provides Geographic information associated with the collection sites of the organisms. The following geographic data is presented in the country, province_state, latitude, and longitude fields of the metadata file(s):

• Latitude and Longitude coordinates
• Country
• Province or State

Figure 3: Locations obtained from latitude and longitude coordinates associated with the sites of collection.

Figure 4: Countries associated with the sites of collection.

Size Information

The BIOSCAN-5M dataset provides information about size of the organisms. The size data is presented in the image_measurement_value, area_fraction, and scale_factor fields of the metadata file(s):

• Image measurement value: Total number of pixels occupied by the organism

Furthermore, utilizing our cropping tool, we calculated the following information about size of the organisms:

• Area fraction: Fraction of the original image, the cropped image comprises.
• Scale factor: Ratio of the cropped image to the cropped and resized image.

Figure 5: Examples of original images (top) and their corresponding masks (bottom) depicting pixels occupied by the organism.

Benchmark Experiments

Data Partitions

We partitioned the BIOSCAN-5M dataset into splits for both closed-world and open-world machine learning problems. To use the partitions we propose, see the split field of the metadata file(s).

• The closed-world classification task uses samples labelled with a scientific name for their species (train, val, and test partitions).

  • This task requires the model to correctly classify new images and DNA barcodes of across a known set of species labels that were seen during training.

• The open-world classification task uses samples whose species name is a placeholder name, and whose genus name is a scientific name (key_unseen, val_unseen, and test_unseen partitions).

  • This task requires the model to correctly group together new species that were not seen during training.
  • In the retreival paradigm, this task can be performed using test_unseen records as queries against keys from the key_unseen records.
  • Alternatively, this data can be evaluated at the genus-level by classification via the species in the train partition.

• Samples labelled with placeholder species names, and whose genus name is not a scientific name are placed in the other_heldout partition.

  • This data can be used to train an unseen species novelty detector.

• Samples without species labels are placed in the pretrain partition, which comprises 90% of the data.

  • This data can be used for self-supervised or semi-supervised training.

Figure 8: Treemap diagram showing number of samples per partition. For the pretrain partition (blues), we provide a further breakdown indicating the most fine-grained taxonomic rank that is labelled for the samples. For the remainder of the partitions (all of which are labelled to species level) we show the number of samples in the partition. Samples for seen species are shown in shades of green, and unseen in shades of red.

DNA-based taxonomic classification task

Two stages of the proposed semi-supervised learning set-up based on BarcodeBERT.

1. Pretraining: DNA sequences are tokenized using non-overlapping k-mers and 50% of the tokens are masked for the MLM task. Tokens are encoded and fed into a transformer model. The output embeddings are used for token-level classification.
2. Fine-tuning: All DNA sequences in a dataset are tokenized using non-overlapping $k$-mer tokenization and all tokenized sequences, without masking, are passed through the pretrained transformer model. Global mean-pooling is applied over the token-level embeddings and the output is used for taxonomic classification.

Figure 9: BarcodeBERT model architecture.

Zero-shot transfer learning task

We follow the experimental setup recommended by zero-shot clustering, expanded to operate on multiple modalities.

1. Take pretrained encoders.
2. Extract feature vectors from the stimuli by passing them through the pretrained encoder.
3. Reduce the embeddings with UMAP.
4. Cluster the reduced embeddings with Agglomerative Clustering.
5. Evaluate against the ground-truth annotations with Adjusted Mutual Information.

Figure 10: BIOSCAN-ZSC model architecture.

Multimodal retrieval learning task

Our experiments using the BIOSCAN-CLIP are conducted in two steps.

1. Training: Multiple modalities, including RGB images, textual taxonomy, and DNA sequences, are encoded separately, and trained using a contrastive loss function.
2. Inference: Image vs DNA embedding is used as a query, and compared to the embeddings obtained from a database of image, DNA and text (keys). The cosine similarity is used to find the closest key embedding, and the corresponding taxonomic label is used to classify the query.

Figure 11: BIOSCAN-CLIP model architecture.