allentdan/fpn_tensorflow — explained in plain English
Analysis updated 2026-07-18 · repo last pushed 2019-03-26
Download a pre-trained model and run it on your own images to detect and box objects immediately.
Train the network on a custom dataset to detect objects specific to your domain, like security footage or satellite photos.
Use multi-GPU training to speed up training a Feature Pyramid Network on a large dataset.
Benchmark this detector's accuracy against standard datasets like Pascal VOC and COCO as a baseline for your own research.
| allentdan/fpn_tensorflow | akshit-python-programmer/text-detection-using-neural-network | anil-matcha/matchacodes | |
|---|---|---|---|
| Stars | — | 0 | — |
| Language | Jupyter Notebook | Jupyter Notebook | Jupyter Notebook |
| Last pushed | 2019-03-26 | — | 2018-08-16 |
| Maintenance | Dormant | — | Dormant |
| Setup difficulty | hard | easy | easy |
| Complexity | 4/5 | 2/5 | 2/5 |
| Audience | researcher | vibe coder | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires a GPU and TensorFlow setup, multi-GPU training needs multiple graphics cards for speed.
A TensorFlow implementation of Feature Pyramid Networks for object detection, helping computers find and box both tiny and large objects in the same photo.
Mainly Jupyter Notebook. The stack also includes TensorFlow, Python, Jupyter Notebook.
Dormant — no commits in 2+ years (last push 2019-03-26).
Setup difficulty is rated hard, with roughly 1h+ to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
double-check against the repo, no cap.