Feedforward Few-shot Species Range Estimation
Authors: Christian Lange, Max Hamilton, Elijah Cole, Alexander Shepard, Samuel Heinrich, Angela Zhu, Subhransu Maji, Grant Van Horn, Oisin Mac Aodha
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on two challenging benchmarks, where we obtain state-of-the-art range estimation performance, in a fraction of the compute time, compared to recent alternative approaches. |
| Researcher Affiliation | Collaboration | 1University of Edinburgh 2UMass Amherst 3Gen Bio AI 4i Naturalist 5Cornell. Correspondence to: Christian Lange <EMAIL>. |
| Pseudocode | No | The paper describes methods and procedures in narrative text, often accompanied by mathematical formulations (e.g., LAN-full loss in Section 3.1, LAN-full-b in Section 3.2), but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code for FS-SINR is available at: https://github.com/Chris-lange/fs-sinr |
| Open Datasets | Yes | Data. We train FS-SINR on the presence-only dataset from Cole et al. (2023), which comprises 35.5 million citizen-science records each annotated with latitude, longitude, and species label for 47,375 diverse species including plants, fungi, and animals from the i Naturalist platform (i Naturalist, 2025). |
| Dataset Splits | Yes | Importantly, we hold out any species from the union of these two datasets from the training set so that species from the evaluation set are not observed during training. As a result, by default, FS-SINR is trained on data from 44,422 species. |
| Hardware Specification | Yes | Training takes approximately ten hours on a single NVIDIA A6000 GPU, requiring approximately six gigabytes of RAM. |
| Software Dependencies | No | The paper mentions software components like PyTorch (Paszke et al., 2019), Adam optimizer (Kingma & Ba, 2015), Grit LM (Muennighoff et al., 2025), EVA-02 ViT (Fang et al., 2024), and scikit-learn (Pedregosa et al., 2011). However, it only provides citations/publication years for these, not specific version numbers for replication. |
| Experiment Setup | Yes | For all training we use the Adam optimizer (Kingma & Ba, 2015) with a learning rate of 0.0005, and an exponential learning rate scheduler with a learning rate decay of 0.98 per epoch, and we use a batch size of 2048. |