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Fast and accurate object detection with end-to-end GPU optimization
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RetinaNet Examples

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


RetinaNet is a single shot object detector with multiple backbones offering various performance/accuracy trade-offs.

It is optimized for end-to-end GPU processing using:

  • The PyTorch deep learning framework
  • NVIDIA Apex for mixed precision and distributed training
  • NVIDIA DALI for optimized data pre-processing
  • NVIDIA TensorRT for high-performance inference


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


For best performance, we encourage using the latest PyTorch NGC docker container:

nvidia-docker run --rm --ipc=host -it

From the container, simply install retinanet using pip:

pip install --no-cache-dir git+

Or you can clone this repository, build and run your own image:

git clone
docker build -t retinanet:latest retinanet/
nvidia-docker run --rm --ipc=host -it retinanet:latest


Training, inference, evaluation and model export can be done through the retinanet utility.

For more details refer to the INFERENCE and TRAINING documentation.


Train a detection model on COCO 2017 from pre-trained backbone:

retinanet 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 with JSON annotations:

retinanet train model_mydataset.pth \
    --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.


Evaluate your detection model on COCO 2017:

retinanet infer retinanet_rn50fpn.pth --images /coco/images/val2017/ --annotations /coco/annotations/instances_val2017.json

Run inference on your dataset:

retinanet 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:

retinanet export model.pth engine.plan

Note: for older versions of TensorRT (prior to TensorRT 5.1 / 19.03 containers) the ONNX opset version should be specified (using --opset 8 for instance).

Evaluate the model with TensorRT backend on COCO 2017:

retinanet 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:

retinanet 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:

retinanet export model.pth engine.plan --int8 --calibration-table /path/to/INT8CalibrationTable


Training numbers for COCO 2017 (train/val) after full training schedule with default parameters.

Inference numbers include bounding boxes post-processing for batch = 1.

Backbone Resize mAP @[IoU=0.50:0.95] Training Time [DGX1v] Inference Latency FP16 [V100] Inference Latency FP16 [T4] Inference Latency INT8 [T4]
ResNet18FPN 800 0.318 5 hrs 12 ms/im 17 ms/im 12 ms/im
ResNet34FPN 800 0.343 6 hrs 14 ms/im 20 ms/im 14 ms/im
ResNet50FPN 800 0.358 7 hrs 16 ms/im 26 ms/im 16 ms/im
ResNet101FPN 800 0.376 10 hrs 20 ms/im 34 ms/im 20 ms/im
ResNet152FPN 800 0.393 12 hrs 25 ms/im 42 ms/im 24 ms/im


RetinaNet supports annotations in the COCO JSON format. 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]
    "categories": [{
        "id" : int


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