CLIP-ViT-B-32-LivingThings-10M-Finetuned

A CLIP ViT-B/32 model, initialized from the DataComp-1B CLIP (ViT-B-32/datacomp_xl_s13b_b90k) and finetuned on the LivingThings-10M subset (animals, plants, fungi) of EntityNet-33M.

Finetuning on organism images turns a general CLIP model into a species expert: iNaturalist 2021 accuracy roughly triples compared to the base model, at the cost of general-domain performance (ImageNet, retrieval).

Paper · Code · Dataset · Project page

Results

Zero-shot top-1 accuracy (%), all models evaluated with the same protocol from the paper, so numbers can differ slightly from those on other model cards. Retrieval is the average image-text recall@1 over COCO, Flickr30k and XM3600. Rare Species tests 400 species that were removed from our training data. Following the paper, it is only reported for models whose training data is known to exclude them ("–" otherwise).

Usage

import torch
import open_clip
from PIL import Image

model, _, preprocess = open_clip.create_model_and_transforms("hf-hub:lmb-freiburg/CLIP-ViT-B-32-LivingThings-10M-Finetuned")
model.eval()
tokenizer = open_clip.get_tokenizer("ViT-B-32", context_length=32)  # trained with 32 text tokens

image = preprocess(Image.open("image.jpg")).unsqueeze(0)
labels = ["red fox", "gray wolf", "coyote"]
text = tokenizer(labels)

with torch.no_grad():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)
    image_features /= image_features.norm(dim=-1, keepdim=True)
    text_features /= text_features.norm(dim=-1, keepdim=True)
    probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

print(labels[probs.argmax().item()])

Species names work in English ("red fox") and Latin ("Vulpes vulpes"); the paper reports the better of the two.

Training

The model has seen ~13B images at a batch size of 90k during pretraining, and ~0.2B images at a batch size of 32k during finetuning. Text labels were sampled 50/50 from the images' web alt texts and from the knowledge-graph text behind the image (the search query, or the entity's aliases or descriptions). See the paper for details.

All EntityNet models

Citation

@inproceedings{ging2026entitynet,
  author    = {Simon Ging and Sebastian Walter and Jelena Bratuli{\'c} and Johannes Dienert and Hannah Bast and Thomas Brox},
  title     = {Using Knowledge Graphs to Harvest Datasets for Efficient {CLIP} Model Training},
  booktitle = {Pattern Recognition, 47th {DAGM} German Conference, {DAGM} {GCPR} 2025, Freiburg, Germany, September 23--26, 2025, Proceedings},
  series    = {Lecture Notes in Computer Science},
  publisher = {Springer Nature Switzerland},
  year      = {2026},
  pages     = {287--302},
  isbn      = {978-3-032-12840-9},
  doi       = {10.1007/978-3-032-12840-9_19}
}