---
library_name: litert
tags:
  - vision
  - object-detection
  - litert
  - tflite
  - torchvision
datasets:
  - detection-datasets/coco
---

# Faster R-CNN ResNet-50 FPN for LiteRT

This repository contains an inference-only LiteRT packaging of TorchVision
`fasterrcnn_resnet50_fpn`.

The detector is split into three TFLite files:

| File | Role |
| --- | --- |
| `fasterrcnn_resnet50_fpn_backbone_body_dynamic_hw.tflite` | ResNet backbone body: transformed image tensor to C2-C5 feature maps |
| `fasterrcnn_resnet50_fpn_rpn_head_dynamic_hw.tflite` | RPN head: one FPN level to objectness logits and box deltas |
| `fasterrcnn_resnet50_fpn_roi_box_dynamic_n.tflite` | ROI box head and predictor: pooled ROI features to class logits and box deltas |

TorchVision host code keeps the detector-specific orchestration around those
LiteRT submodels: preprocessing, FPN, anchor/proposal decode, NMS, ROIAlign, and
final postprocessing.

Source model documentation:
https://docs.pytorch.org/vision/main/models/generated/torchvision.models.detection.fasterrcnn_resnet50_fpn.html

## Usage

Install runtime dependencies:

```bash
pip install ai-edge-litert torch torchvision pillow numpy
```

Run the sample from this repository directory:

```bash
python sample_torchvision_fasterrcnn_litert_cpu.py
```

The script defaults to the three `.tflite` files in the same directory as the
sample. You can override them explicitly:

```bash
python sample_torchvision_fasterrcnn_litert_cpu.py \
  --backbone-model fasterrcnn_resnet50_fpn_backbone_body_dynamic_hw.tflite \
  --rpn-head-model fasterrcnn_resnet50_fpn_rpn_head_dynamic_hw.tflite \
  --roi-model fasterrcnn_resnet50_fpn_roi_box_dynamic_n.tflite \
  --image https://github.com/pytorch/hub/raw/master/images/dog.jpg
```

Example output:

```text
input image: original=(1213, 1546) transformed=(1, 3, 800, 1024)
host proposal decode/NMS: 1000 proposals
ROI LiteRT outputs: logits=(1000, 91) box_regression=(1000, 364)
detections above 0.50: 1
  01: dog score=0.9669 box=[137.84, 67.8, 1386.9, 1172.82]
```

## Notes

This is not a single-file object detector. The sample script is part of the
runtime contract and uses TorchVision host modules for the portions that are
not represented by the three LiteRT submodels.

The TFLite files use dynamic image height/width where the current CPU LiteRT
runtime path supports it. The sample runs with `HardwareAccelerator.CPU`.



## BibTeX entry and citation info

```bibtex
@article{DBLP:journals/corr/RenHG015,
  author       = {Shaoqing Ren and
                  Kaiming He and
                  Ross B. Girshick and
                  Jian Sun},
  title        = {Faster {R-CNN:} Towards Real-Time Object Detection with Region Proposal
                  Networks},
  journal      = {CoRR},
  volume       = {abs/1506.01497},
  year         = {2015},
  url          = {http://arxiv.org/abs/1506.01497},
  eprinttype   = {arXiv},
  eprint       = {1506.01497},
  timestamp    = {Mon, 13 Aug 2018 16:46:02 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/RenHG015.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
```