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birder-project/
rope_deit3_m14_dino-v2-dist-bio

Name: rope_deit3_m14_dino-v2-dist-bio
Availability: InStock

birder

image-feature-extraction

birder-project/vit_reg4_so150m_p14_ls_dino-v2-bio

finetune

apache-2.0

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Model Card for rope_deit3_m14_dino-v2-dist-bio

rope_deit3_m14_dino-v2-dist-bio is a compact Bio-DINO image encoder distilled from the larger Bio-DINO SoViT-150M/14 model. It keeps the same natural-photography biodiversity scope as the teacher model, but uses a much smaller DeiT3-M/14-style student with 38.3M backbone parameters and 512-dimensional embeddings.

For the training-data description, intended scope, limitations, broader evaluation design and background on the Bio-DINO release, see the teacher model card. This card focuses only on what is specific to the distilled model.

Model Details

Model Type: Image encoder and detection backbone
Model Stats:
- Params (M): 38.3
- Input image size: 252 x 252
Dataset: Trained on a diverse dataset of approximately 31M images, including:
- TreeOfLife-10M-EOL-NaturalImages
- iNaturalist 2021
- BIOSCAN-5M (pretrain split)
- TreeOfLife-200M (subset)
- IP102 v1.1
- iWildCam 2022 (subset)
- The Birder dataset (private dataset)
Papers:
- DeiT III: Revenge of the ViT: https://arxiv.org/abs/2204.07118
- Rotary Position Embedding for Vision Transformer: https://arxiv.org/abs/2403.13298
- DINOv2: Learning Robust Visual Features without Supervision: https://arxiv.org/abs/2304.07193

Selected Results

The iNaturalist 2021 result uses a fuller linear-probing setup than the frozen-embedding evaluations above: the backbone is frozen, but the classification head is trained for multiple epochs with light image augmentation.

Model	Resolution	Params (M)	Accuracy	Top-3 accuracy	Macro F1
Bio-DINO 224px	224	142.6	0.8572	0.9420	0.8564
Bio-DINO 252px	252	142.6	0.8709	0.9510	0.8702
Bio-DINO 336px	336	142.6	0.8807	0.9567	0.8800
Bio-DINO M/14 (dist)	252	38.3	0.8352	0.9274	0.8336
Bio-DINO S/14 (dist)	252	25.5	0.8010	0.9055	0.8000
BioCLIP v1 ViT-B/16	224	93.5	0.7890	0.9005	0.7874
BioCLIP v2 ViT-L/14	224	313.4	0.9169	0.9745	0.9164
BioTrove-CLIP-O ViT-B/16	224	93.5	0.8351	0.9290	0.8334
DINOv2 ViT-B/14	224	93.4	0.7492	0.8645	0.7457
DINOv3 RoPE ViT-B/16	256	93.4	0.7826	0.8889	0.7795
DINOv3 RoPE ViT-L/16	256	313.4	0.8333	0.9235	0.8309
PE-Core RoPE ViT-B/16	224	100.6	0.6831	0.8299	0.6788
ConvNeXt v1 L ImageNet-22K	224	211.6	0.7317	0.8647	0.7289

For the iNaturalist 2021 linear-probe table, parameter counts include the frozen image encoder plus the trained 10k-class linear classification head.

Model Usage

Image Embeddings

python

import birder
from birder.inference.classification import infer_image

# Option 1: manual setup (more control over preprocessing)
net, model_info = birder.load_pretrained_model("rope_deit3_m14_dino-v2-dist-bio", inference=True)

# Get the image size the model was trained on
size = birder.get_size_from_signature(model_info.signature)

# Create an inference transform
transform = birder.classification_transform(size, model_info.rgb_stats)

# Option 2: helper (quick start with default preprocessing)
net, model_info, transform = birder.load_pretrained_model_and_transform("rope_deit3_m14_dino-v2-dist-bio", inference=True)

image = "path/to/image.jpeg"  # or a PIL image
out, embedding = infer_image(net, image, transform, return_embedding=True)
# embedding is a NumPy array with shape of (1, 512)

Detection Feature Map