---
license: cc-by-nc-4.0
tags:
- image-geolocation
- osv-5m
pipeline_tag: image-feature-extraction
datasets:
- osv5m/osv5m
base_model:
- timm/ViT-SO400M-16-SigLIP2-256
- timm/ViT-gopt-16-SigLIP2-256
- facebook/dinov3-vitl16-pretrain-lvd1689m
model-index:
- name: Chipoint
  results:
  - task:
      type: image-feature-extraction
      name: Image Geolocation
    dataset:
      type: osv5m
      name: OSV-5M
    metrics:
    - name: Acc@25km
      type: accuracy
      value: 37.63
    - name: Acc@200km
      type: accuracy
      value: 70.56
    - name: Acc@750km
      type: accuracy
      value: 84.94
    - name: Acc@2500km
      type: accuracy
      value: 93.36
    - name: GeoScore
      type: geoscore
      value: 4174
    - name: Median error (km)
      type: distance-error
      value: 51
    - name: Mean error (km)
      type: distance-error
      value: 716
    - name: Country accuracy
      type: accuracy
      value: 85.55
    - name: Region accuracy
      type: accuracy
      value: 61.68
  - task:
      type: image-feature-extraction
      name: Image Geolocation (cross-dataset generalization)
    dataset:
      type: geoguessr
      name: GeoGuessr (Google Street View)
    metrics:
    - name: Country accuracy
      type: accuracy
      value: 63.14
---

Chipoint is a SOTA geolocation model that is open-weight and currently beats the published frontier closed-weight model (Pinpoint, June 2026) and beats all models on the OSV-5M bench.




![chipoint_mean_error](https://cdn-uploads.huggingface.co/production/uploads/6965be9bffba099211b1908f/E3NSpI63Hj_oVo_v-pDHf.png)
![chipoint_accuracy_thresholds](https://cdn-uploads.huggingface.co/production/uploads/6965be9bffba099211b1908f/mmO0-NkxWDhUFZvsYooWl.png)
![chipoint_geoscore](https://cdn-uploads.huggingface.co/production/uploads/6965be9bffba099211b1908f/xe2gmIqdFCtoMPPou1e4g.png)
![chipoint_kpi_banner](https://cdn-uploads.huggingface.co/production/uploads/6965be9bffba099211b1908f/231ds1ovY118HIPZxd1W_.png)

# Chipoint — OSV-5M benchmark

OSV-5M test set (210,122 images). Acc@Xkm is the share of predictions within X km. GeoScore is `5000·exp(-d/1492.7)`, higher is better.

| Model | @25km | @200km | @750km | @2500km | GeoScore | Mean err |
|---|---|---|---|---|---|---|
| OSV-5M Baseline | – | – | – | – | 3361 | 1814 km |
| GeoCLIP | 21.5 | 52.1 | 72.1 | – | – | – |
| GRE | 9.7 | 35.6 | 72.5 | 91.1 | – | 1192 km |
| LocDiff | 11.0 | 46.3 | 77.0 | 88.2 | – | – |
| RFM (S2S) | – | – | – | – | 3767 | 1069 km |
| HierLoc | – | – | – | – | 3963 | 861 km |
| Pinpoint (retrieval only) | 32.1 | 65.6 | 82.8 | 92.8 | 4035 | 784 km |
| Pinpoint (full) | 35.6 | 67.5 | 83.7 | 93.2 | 4114 | 743 km |
| **Chipoint** | **37.63** | **70.56** | **84.94** | **93.36** | **4174** | **716 km** |

Chipoint's median error is **51 km**. Baseline numbers are as reported in the OSV-5M (CVPR 2024), HierLoc (arXiv 2601.23064), and Pinpoint (arXiv 2606.04133) papers. Blank cells are metrics a given method does not report on OSV-5M. OSV-5M Baseline, RFM, and HierLoc report administrative accuracy plus GeoScore/mean rather than distance thresholds, so they compare only on the GeoScore and mean-error columns.

**Country / region (OSV-5M):** Chipoint reaches country ≈ **85.6%**, region / admin-1 ≈ **61.7%** (from reverse-geocoding the predictions, approximate vs exact admin boundaries). For reference, at admin level Pinpoint reports Country 84.8% / Region 59.1% and HierLoc 82.9% / 55.0%. Our figures use nearest-place geocoding rather than exact polygons, so treat that comparison as indicative.

# IMPORTANT
Chipoint could not compare itself on other benchmarks other than OSV-5M due to several factors. Benchmarks such as im2gps, im2gps3k, YFCC4k, and GWS15k are either private or unretrivable. 

# Running the model
Instructions on how to run Chipoint locally are located in the RUN_GUIDE.md file.

# when the hell is chipoint 2 coming out??
its in progress! we are cooking at chiikabu labs, and if youd like to accelerate chipoint 2's progress, feel free to contact me on discord! (username is "fourlatte")