license: mit
tags:
- image-geolocation
- street-view
- osv-5m
pipeline_tag: image-feature-extraction
datasets:
- osv5m/osv5m
- Jia-py/MP16-Pro
model-index:
- name: Chipoint v2
results:
- task:
type: image-feature-extraction
name: Image Geolocation
dataset:
type: osv5m
name: OSV-5M
metrics:
- name: Acc@1km
type: accuracy
value: 2.66
- name: Acc@25km
type: accuracy
value: 44.83
- name: Acc@200km
type: accuracy
value: 81.1
- name: Acc@750km
type: accuracy
value: 92.11
- name: Acc@2500km
type: accuracy
value: 96.82
- name: GeoScore
type: geoscore
value: 4485
- name: Median error (km)
type: distance-error
value: 32
- name: Mean error (km)
type: distance-error
value: 383
- task:
type: image-feature-extraction
name: Image Geolocation (cross-dataset generalization)
dataset:
type: im2gps3k
name: im2gps3k (street-view rerank)
metrics:
- name: Acc@25km
type: accuracy
value: 11.68
- name: GeoScore
type: geoscore
value: 2018.5
- name: Chipoint v2 (general-photo arm)
results:
- task:
type: image-feature-extraction
name: Image Geolocation
dataset:
type: im2gps3k
name: im2gps3k (photo)
metrics:
- name: Acc@1km
type: accuracy
value: 16.5
- name: Acc@25km
type: accuracy
value: 41.2
- name: Acc@200km
type: accuracy
value: 54.1
- name: Acc@750km
type: accuracy
value: 70.7
- name: Acc@2500km
type: accuracy
value: 84.8
- task:
type: image-feature-extraction
name: Image Geolocation
dataset:
type: yfcc4k
name: YFCC4k (photo)
metrics:
- name: Acc@1km
type: accuracy
value: 19.1
- name: Acc@25km
type: accuracy
value: 34.5
- name: Acc@200km
type: accuracy
value: 44.2
- name: Acc@750km
type: accuracy
value: 60.2
- name: Acc@2500km
type: accuracy
value: 76
chiikabu.cc · discord · twitter / x
Chipoint v2 is a SOTA geolocation model that is open-weight and beats the published frontier closed-weight model (Pinpoint, June 2026), its predecessor (Chipoint v1, July 2026), and all models on the OSV-5M street-view benchmark. This repo hosts both arms:
- street-view arm — the 4.89M-frame OSV-5M gallery index, the
projection heads, and the 23-feature reranker pipeline
(
streetview_k512_joint.pt, K512, per-threshold confidence head) - general-photo arm — the unified 14.5M photo gallery (MP14 10.9M +
MP16-Pro 4.1M) and the 23-feature rerankers
(
photoarm_k768_thr.pt/photoarm_k768_sr.pt/photoarm_k512_thr.pt) router_osv_flickr.ptroutes queries between the two arms for the best accuracy on a provided image.
Street-view arm
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 v1 | 37.63 | 70.56 | 84.94 | 93.36 | 4174 | 716 km |
| Chipoint v2 · street (full pipeline) | 44.8 | 81.1 | 92.1 | 96.8 | 4485 | 383 km |
Chipoint v2's median error is 32 km (v1: 51 km). The full-pipeline line is retrieval (K=64 pool) + the K512 23-feature reranker with a multiscale blend on the full 210,122-image test split. For reference, raw retrieval without reranking scores 34.89 / 61.96 / 74.60 / 85.34, GeoScore 3742, mean 1427 km.
General-photo arm
Running the model
Instructions on how to run Chipoint v2 locally are in the RUN_GUIDE.md file. Quick check:
python run_chipoint.py weights/streetview_k512_joint.pt
python run_chipoint.py weights/photoarm_k768_thr.pt