retinanetjs

This package provides some convenience methods for using TensorFlow models created using keras-retinanet. Check out the docs. You can also check out the example app.

Getting Started

Convert RetinaNet Model to TensorFlowJS

As an example, we'll convert the ResNet50 weights to TensorFlow.js format. You must have tensorflowjs installed.

First, save a fixed input size training model to a Keras h5 file with both the weights and architecture. You must supply a fixed input shape. In experimenting with different backbones, only a few functioned correctly with undefined input shapes when loaded with TensorFlowJS.

Importantly, we do not convert to a prediction model. Rather, we do the necessary box regression in TensorFlowJS. Including them made some backbones load incorrectly in TensorFlow.js.

import urllib.request

import keras
from keras_retinanet import models

urllib.request.urlretrieve(
    "https://github.com/fizyr/keras-retinanet/releases/download/0.5.1/resnet50_coco_best_v2.1.0.h5",
    "resnet50_coco_best_v2.1.0.h5"
)

model = models.backbone(
    backbone_name='resnet50').retinanet(
    num_classes=80,
    inputs=keras.layers.Input((512, 512, 3)
)
)
model.load_weights('resnet50_coco_best_v2.1.0.h5')
model.save('resnet50_coco_best_v2.1.0_full.h5')

Then, at the command line, execute the following.

tensorflowjs_converter \
    --input_format=keras \
    --output_format=tfjs_layers_model \
     resnet50_coco_best_v2.1.0_full.h5 \
     resnet50_coco_best_v2.1.0

Using Model with retinanetjs

The code below loads the above model and does detection. We assume that you have a reference to an HTMLImage object in imageRef and a list of the COCO class label names in COCO_CLASSES. Note that you must supply the preprocessing mode. Check the preprocess_image method on your backbone to see whether your model uses tf or caffe preprocessing.

import { load } from 'retinanetjs'

const detector = await load(
    'http://www.example.com/path/to/resnet50_coco_best_v2.1.0',
    COCO_CLASSES, "caffe"
)
const detections = detector.detect(imageRef)

Limitations

The following features are not supported at this time:

• Unspecified input shapes (e.g., (None, None, 3))
• Class-specific filtering. For the moment, non-max suppression is performed across all classes.