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Spider Classifier — Training Manifest
Public release of the training manifest used to fine-tune the Spiders of New Hampshire species classifier.
This manifest enumerates every photo used to train, validate, and test the model. Each row links back to the original observation and photo on iNaturalist, preserving full attribution and license metadata.
Source
- Model run:
20260528_104624_licensed_dinov2_l_14_reg4_518 - Generated: 2026-05-29T02:17:00.691986+00:00
Headline numbers
| Total photos | 123,099 |
| Unique observations | 60,859 |
| Species (classes) | 161 |
| Families | 27 |
Splits
| Split | Photos |
|---|---|
| test | 18,393 |
| train | 86,085 |
| val | 18,621 |
Regions
| Region | Photos |
|---|---|
| nh | 123,099 |
License distribution
Every photo retains its original iNaturalist license. The model was trained under a permissive-license policy at the project level.
| License | Photos |
|---|---|
| cc-by-nc | 102,952 |
| cc-by | 13,971 |
| cc0 | 3,570 |
| cc-by-nc-sa | 1,453 |
| cc-by-sa | 1,153 |
Schema
| Column | Description |
|---|---|
label_idx |
Integer class index used by the model |
species |
Genus_epithet (e.g. Phidippus_audax) |
split |
train, val, or test |
observation_id |
iNaturalist observation ID |
photo_id |
iNaturalist photo ID |
final_license_code |
License of the photo (e.g. cc0, cc-by, cc-by-nc) |
license_source |
Whether the license came from the observation CSV or recovery pass |
image_attribution |
Photographer attribution string supplied by iNaturalist |
region |
nh or bordering (geographic filter at scrape time) |
family |
Taxonomic family |
inat_photo_url |
Canonical iNaturalist photo URL |
inat_observation_url |
Canonical iNaturalist observation URL |
Acknowledgments
All photos were sourced from iNaturalist under
their respective Creative Commons or other open licenses. Each row's
image_attribution field credits the individual observer who contributed
the photo. Please follow the terms of each photo's license when reusing.
Reproducibility
This manifest was produced by the project's deterministic pipeline. To
regenerate from the model's bundled run directory, run
pipeline/6_publish_manifest.py against the source dataset_manifest.csv.
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