# retinanet-examples

**Repository Path**: mirrors_NVIDIA/retinanet-examples

## Basic Information

- **Project Name**: retinanet-examples
- **Description**: Fast and accurate object detection with end-to-end GPU optimization
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-18
- **Last Updated**: 2026-03-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# NVIDIA Object Detection Toolkit (ODTK)

**Fast** and **accurate** single stage object detection with end-to-end GPU optimization.

## Description

ODTK is a single shot object detector with various backbones and detection heads. This allows performance/accuracy trade-offs.

It is optimized for end-to-end GPU processing using:
* The [PyTorch](https://pytorch.org) deep learning framework with [ONNX](https://onnx.ai) support
* NVIDIA [Apex](https://github.com/NVIDIA/apex) for mixed precision and distributed training
* NVIDIA [DALI](https://github.com/NVIDIA/DALI) for optimized data pre-processing
* NVIDIA [TensorRT](https://developer.nvidia.com/tensorrt) for high-performance inference
* NVIDIA [DeepStream](https://developer.nvidia.com/deepstream-sdk) for optimized real-time video streams support

## Rotated bounding box detections

This repo now supports rotated bounding box detections. See [rotated detections training](TRAINING.md#rotated-detections) and [rotated detections inference](INFERENCE.md#rotated-detections) documents for more information on how to use the `--rotated-bbox` command. 

Bounding box annotations are described by `[x, y, w, h, theta]`. 

## Performance

The detection pipeline allows the user to select a specific backbone depending on the latency-accuracy trade-off preferred.

ODTK **RetinaNet** model accuracy and inference latency & FPS (frames per seconds) for [COCO 2017](http://cocodataset.org/#detection-2017) (train/val) after full training schedule. Inference results include bounding boxes post-processing for a batch size of 1. Inference measured at `--resize 800` using `--with-dali` on a FP16 TensorRT engine.

Backbone |  mAP @[IoU=0.50:0.95] | Training Time on [DGX1v](https://www.nvidia.com/en-us/data-center/dgx-1/) | Inference latency FP16 on [V100](https://www.nvidia.com/en-us/data-center/tesla-v100/) | Inference latency INT8 on [T4](https://www.nvidia.com/en-us/data-center/tesla-t4/) | Inference latency FP16 on [A100](https://www.nvidia.com/en-us/data-center/a100/) | Inference latency INT8 on [A100](https://www.nvidia.com/en-us/data-center/a100/)
--- | :---: | :---: | :---: | :---: | :---: | :---:
[ResNet18FPN](https://github.com/NVIDIA/retinanet-examples/releases/download/19.04/retinanet_rn18fpn.zip) | 0.318 | 5 hrs  | 14 ms;</br>71 FPS | 18 ms;</br>56 FPS | 9 ms;</br>110 FPS | 7 ms;</br>141 FPS
[MobileNetV2FPN](https://github.com/NVIDIA/retinanet-examples/releases/download/v0.2.3/retinanet_mobilenetv2fpn.pth) | 0.333 | | 14 ms;</br>74 FPS | 18 ms;</br>56 FPS | 9 ms;</br>114 FPS | 7 ms;</br>138 FPS
[ResNet34FPN](https://github.com/NVIDIA/retinanet-examples/releases/download/19.04/retinanet_rn34fpn.zip) | 0.343 | 6 hrs  | 16 ms;</br>64 FPS | 20 ms;</br>50 FPS | 10 ms;</br>103 FPS | 7 ms;</br>142 FPS
[ResNet50FPN](https://github.com/NVIDIA/retinanet-examples/releases/download/19.04/retinanet_rn50fpn.zip) | 0.358 | 7 hrs  | 18 ms;</br>56 FPS | 22 ms;</br>45 FPS | 11 ms;</br>93 FPS | 8 ms;</br>129 FPS
[ResNet101FPN](https://github.com/NVIDIA/retinanet-examples/releases/download/19.04/retinanet_rn101fpn.zip) | 0.376 | 10 hrs | 22 ms;</br>46 FPS | 27 ms;</br>37 FPS | 13 ms;</br>78 FPS | 9 ms;</br>117 FPS
[ResNet152FPN](https://github.com/NVIDIA/retinanet-examples/releases/download/19.04/retinanet_rn152fpn.zip) | 0.393 | 12 hrs | 26 ms;</br>38 FPS | 33 ms;</br>31 FPS | 15 ms;</br>66 FPS | 10 ms;</br>103 FPS

## Installation

For best performance, use the latest [PyTorch NGC docker container](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch). Clone this repository, build and run your own image:

```bash
git clone https://github.com/nvidia/retinanet-examples
docker build -t odtk:latest retinanet-examples/
docker run --gpus all --rm --ipc=host -it odtk:latest
```

## Usage

Training, inference, evaluation and model export can be done through the `odtk` utility. 
For more details, including a list of parameters, please refer to the [TRAINING](TRAINING.md) and [INFERENCE](INFERENCE.md) documentation.

### Training

Train a detection model on [COCO 2017](http://cocodataset.org/#download) from pre-trained backbone:
```bash
odtk train retinanet_rn50fpn.pth --backbone ResNet50FPN \
    --images /coco/images/train2017/ --annotations /coco/annotations/instances_train2017.json \
    --val-images /coco/images/val2017/ --val-annotations /coco/annotations/instances_val2017.json
```

### Fine Tuning

Fine-tune a pre-trained model on your dataset. In the example below we use [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html) with [JSON annotations](https://storage.googleapis.com/coco-dataset/external/PASCAL_VOC.zip):
```bash
odtk train model_mydataset.pth --backbone ResNet50FPN \
    --fine-tune retinanet_rn50fpn.pth \
    --classes 20 --iters 10000 --val-iters 1000 --lr 0.0005 \
    --resize 512 --jitter 480 640 --images /voc/JPEGImages/ \
    --annotations /voc/pascal_train2012.json --val-annotations /voc/pascal_val2012.json
```

Note: the shorter side of the input images will be resized to `resize` as long as the longer side doesn't get larger than `max-size`. During training, the images will be randomly randomly resized to a new size within the `jitter` range.

### Inference

Evaluate your detection model on [COCO 2017](http://cocodataset.org/#download):
```bash
odtk infer retinanet_rn50fpn.pth --images /coco/images/val2017/ --annotations /coco/annotations/instances_val2017.json
```

Run inference on [your dataset](#datasets):
```bash
odtk infer retinanet_rn50fpn.pth --images /dataset/val --output detections.json
```

### Optimized Inference with TensorRT

For faster inference, export the detection model to an optimized FP16 TensorRT engine:
```bash
odtk export model.pth engine.plan
```

Evaluate the model with TensorRT backend on [COCO 2017](http://cocodataset.org/#download):
```bash
odtk infer engine.plan --images /coco/images/val2017/ --annotations /coco/annotations/instances_val2017.json
```

### INT8 Inference with TensorRT

For even faster inference, do INT8 calibration to create an optimized INT8 TensorRT engine:
```bash
odtk export model.pth engine.plan --int8 --calibration-images /coco/images/val2017/
```
This will create an INT8CalibrationTable file that can be used to create INT8 TensorRT engines for the same model later on without needing to do calibration.

Or create an optimized INT8 TensorRT engine using a cached calibration table:
```bash
odtk export model.pth engine.plan --int8 --calibration-table /path/to/INT8CalibrationTable
```

## Datasets

RetinaNet supports annotations in the [COCO JSON format](http://cocodataset.org/#format-data).
When converting the annotations from your own dataset into JSON, the following entries are required:
```
{
    "images": [{
        "id" : int,
        "file_name" : str
    }],
    "annotations": [{
        "id" : int,
        "image_id" : int, 
        "category_id" : int,
        "bbox" : [x, y, w, h]   # all floats
        "area": float           # w * h. Required for validation scores
        "iscrowd": 0            # Required for validation scores
    }],
    "categories": [{
        "id" : int
    ]}
}
```

If using the `--rotated-bbox` flag for rotated detections, add an additional float `theta` to the annotations. To get validation scores you also need to fill the `segmentation` section.
```
        "bbox" : [x, y, w, h, theta]    # all floats, where theta is measured in radians anti-clockwise from the x-axis.
        "segmentation" : [[x1, y1, x2, y2, x3, y3, x4, y4]]
                                        # Required for validation scores.
```

## Disclaimer

This is a research project, not an official NVIDIA product.

## Jetpack compatibility

This branch uses TensorRT 7. If you are training and inferring models using PyTorch, or are creating TensorRT engines on Tesla GPUs (eg V100, T4), then you should use this branch.

If you wish to deploy your model to a Jetson device (eg - Jetson AGX Xavier) running Jetpack version 4.3, then you should use the `19.10` branch of this repo.

## References

- [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002).
  Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollár.
  ICCV, 2017.
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://arxiv.org/abs/1706.02677).
  Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, Kaiming He.
  June 2017.
- [Feature Pyramid Networks for Object Detection](https://arxiv.org/abs/1612.03144).
  Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, Serge Belongie.
  CVPR, 2017.
- [Deep Residual Learning for Image Recognition](http://arxiv.org/abs/1512.03385).
  Kaiming He, Xiangyu Zhang, Shaoqing Renm Jian Sun.
  CVPR, 2016.