We lifted 3 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| imageomics/bioclip | canonical | 0 of 1 |
| copy not recorded | — | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| convert_to_custom_text_state_dict | Ran | this paper's copy was not recorded; identical code first harvested from deep-real/dcp pointer only · get_code("f19962ebb134b3d7") |
| get_cast_dtype | Ran | this paper's copy was not recorded; identical code first harvested from zsxkib/PuLID pointer only · get_code("dcd422d66b0581d8") |
| build_model_from_openai_state_dict | Not yet run | imageomics/bioclip/src/open_clip/model.py pointer only (licence: NOASSERTION) · get_code("57f1602ed20a9c21") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Images of the natural world, collected by a variety of cameras, from drones to individual phones, are increasingly abundant sources of biological information. There is an explosion of computational methods and tools, particularly computer vision, for extracting biologically relevant information from images for science and conservation. Yet most of these are bespoke approaches designed for a specific task and are not easily adaptable or extendable to new questions, contexts, and datasets. A vision model for general organismal biology questions on images is of timely need. To approach this, we curate and release TreeOfLife-10M, the largest and most diverse ML-ready dataset of biology images. We then develop BioCLIP, a foundation model for the tree of life, leveraging the unique properties of biology captured by TreeOfLife-10M, namely the abundance and variety of images of plants, animals, and fungi, together with the availability of rich structured biological knowledge. We rigorously benchmark our approach on diverse fine-grained biology classification tasks and find that BioCLIP consistently and substantially outperforms existing baselines (by 16% to 17% absolute). Intrinsic evaluation reveals that BioCLIP has learned a hierarchical representation conforming to the tree of life, shedding light on its strong generalizability. https://imageomics.github.io/bioclip has models, data and code.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2311.18803")
get_code_for_paper("2311.18803")
have("2311.18803")
Connect an agent — have() is free.