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ailia-models-tflite

Quantized tflite models for ailia TFLite Runtime

About ailia TFLite Runtime

ailia TFLite Runtime is a TensorFlow Lite compatible inference engine. Written in C99, it supports inference in Non-OS and RTOS. It also supports high-speed inference using Intel MKL on a PC. In the Android environment, we provide a Unity Package, which also supports NPU inference using NNAPI.

Install

NEW - ailia TFLite Runtime can now be installed with "pip3 install ailia_tflite" !

Run the following command. The Python version is compatible with Windows, macOS, and Linux. It is also planned to support Arm Linux in the future.

pip3 install ailia_tflite

Models

Background removal

Model Reference Exported From Supported Ailia Version
u2net U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection TensorFlow 1.1.0

Depth estimation

Model Reference Exported From Supported Ailia Version
Midas Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer Pytorch 1.1.7

Face detection

Model Reference Exported From Supported Ailia Version
BlazeFace PINTO_model_zoo TensorFlow 1.0.0

Face recognition

Model Reference Exported From Supported Ailia Version
Face Mesh PINTO_model_zoo TensorFlow 1.0.0
face_classification Real-time face detection and emotion/gender classification TensorFlow 1.1.1

Hand recognition

Model Reference Exported From Supported Ailia Version
Blaze Hand PINTO_model_zoo TensorFlow 1.0.0

Image classification

Model Reference Exported From Supported Ailia Version
MobileNet MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Keras 1.0.0
MobileNetV2 MobileNetV2: Inverted Residuals and Linear Bottlenecks Keras 1.0.0
ResNet50 tf.keras.applications.resnet50.ResNet50 Keras 1.0.0
EfficientnetLite efficientnet-lite-keras Keras 1.0.0
SqueezeNet keras_squeezenet2 Keras 1.0.0
vgg16 VGG16 - Torchvision Pytorch 1.1.7 for int8, 1.1.9 for float
googlenet GOOGLENET Pytorch 1.1.10

Image segmentation

Model Reference Exported From Supported Ailia Version
DeepLabv3+ PINTO_model_zoo TensorFlow 1.0.0
HRNet-Semantic-Segmentation HRNet-Semantic-Segmentation TensorFlow 1.1.0

Object detection

Model Reference Exported From Supported Ailia Version
YOLOv3 tiny tensorflow-yolov4-tflite TensorFlow 1.0.0
YOLOX YOLOX Pytorch 1.1.1
EfficientDetLite PINTO_model_zoo TensorFlow 1.1.3

Pose estimation

Model Reference Exported From Supported Ailia Version
pose_resnet Simple Baselines for Human Pose Estimation and Tracking Pytorch 1.1.7 for int8, 1.1.9 for float

Super resolution

Model Reference Exported From Supported Ailia Version
ESPCN Image Super-Resolution using an Efficient Sub-Pixel CNN TensorFlow 1.1.0
srresnet Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network Pytorch 1.1.10

Options

You can benchmark with the -b option. You can use the official TensorFlow Lite with the --tflite option.

Launchar

You can use cui launchar.

python3 launchar.py

i.MX8 Support Status

See NXP

About

Quantized version of model library

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