Datasets:
File size: 2,923 Bytes
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license: cdla-permissive-1.0
task_categories:
- image-classification
tags:
- camera-trap
- wildlife
- serengeti
- snapshot-safari
- megadetector
pretty_name: Snapshot Safari SER Balanced — Classroom Subset v1.0
size_categories:
- 1K<n<10K
---
# Snapshot Safari SER (Serengeti) — Balanced Classroom Subset v1.0
## Summary
A curated, balanced subset of the Snapshot Safari 2024 Expansion SER (Serengeti
National Park) camera trap dataset, prepared for use in the CAS Deep Learning —
Computer Vision course exercises.
| Archive | Images | Description |
|---------------------|-------:|-----------------------------------|
| `ser_balanced.tar.gz` | 1,850 | Balanced, ≤200/class, full frames |
## Source
- **Dataset:** Snapshot Safari 2024 Expansion — SER (Serengeti) subset
- **URL:** https://lila.science/datasets/snapshot-safari-2024-expansion/
- **License:** Community Data License Agreement — Permissive variant 1.0
- **Attribution:** Snapshot Safari / University of Minnesota Lion Center
## MegaDetector
Pre-computed MegaDetector v1000-redwood RDE-filtered results from LILA Science:
`snapshot-safari-2024-expansion-SER-subset-v1000.0.0-redwood_detections.threshold.filtered.json.zip`
Used to filter frames (conf ≥ 0.8) and select the best frame per sequence.
## Species
buffalo, elephant, empty, gazellegrants, gazellethomsons, hartebeest, impala,
warthog, wildebeestblue, zebraplains
## Statistics
| Class | Train | Val | Test | Total |
|-----------------|------:|----:|-----:|------:|
| buffalo | 140 | 30 | 30 | 200 |
| elephant | 140 | 30 | 30 | 200 |
| empty | 35 | 8 | 7 | 50 |
| gazellegrants | 140 | 30 | 30 | 200 |
| gazellethomsons | 140 | 30 | 30 | 200 |
| hartebeest | 140 | 30 | 30 | 200 |
| impala | 140 | 30 | 30 | 200 |
| warthog | 140 | 30 | 30 | 200 |
| wildebeestblue | 140 | 30 | 30 | 200 |
| zebraplains | 140 | 30 | 30 | 200 |
| **Total** | 1295 | 278 | 277 | 1850 |
**Note:** ~56% of images are IR/night (near-infrared, nearly greyscale).
## Curation Details
- **Deduplication:** one image per sequence (highest MD animal confidence frame)
- **Animal filter:** MD animal confidence ≥ 0.8
- **Empty filter:** max MD animal confidence < 0.2
- **Split strategy:** stratified 70/15/15 by sequence ID — no sequence spans splits
- **Image resolution:** resized to max 1024 px on longer side, JPEG quality 92
- **Format:** ImageFolder layout — `<split>/<label>/<filename>.jpg`
## Usage
```python
from huggingface_hub import hf_hub_download
import tarfile
archive = hf_hub_download(
"marco-willi/ser_balanced",
"ser_balanced.tar.gz",
repo_type="dataset",
)
with tarfile.open(archive) as tar:
tar.extractall(DATA_PATH)
# → DATA_PATH/ser/ser_balanced/{train,val,test}/<label>/*.jpg
```
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