experiencor/keras-yolo2
Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows). observed · 2026-08-28
Health v2 · maintenance only
23/100
- Activity 0
- Release rhythm 8
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3451
- days_rel: n/a
- days_push: 1258
- n_releases_24m: 0
Adoption not part of the score
1733 stars · 771 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A Keras/TensorFlow implementation of the YOLOv2 real-time object detection model with support for training on custom datasets. It offers multiple backend architectures including Full YOLO, Tiny YOLO, MobileNet, SqueezeNet, InceptionV3, and ResNet50.
Use cases
- train a yolo object detector on my own images
- detect raccoons or animals in photos
- real-time object detection with keras
- train yolov2 with a mobilenet backend
- object detection from VOC-format annotations
- build a custom detector for hands or cells
When to avoid
- you need the latest YOLO versions (YOLOv4/v5/v8) or state-of-the-art accuracy
- you require multi-GPU or multiscale training, which are unimplemented
- you need active maintenance or modern TensorFlow 2.x compatibility
- you want a production-ready detector with mAP evaluation built in
Facets
library · maturity maintenance
machine-learning deep-learning computer-vision image-processing deep-learning computer-vision machine-learning image-processing python cross-platform yolo object-detection keras tensorflow realtime-detection custom-training mobilenet voc-annotations
1 source
- readme: https://github.com/experiencor/keras-yolo2 · fetched 2026-08-28 · 66262605778c
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| experiencor/keras-yolo2 | main | 23 |
For agents
markdown · JSON · MCP: product_card(name="experiencor/keras-yolo2")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem