Inspireface

The first Android version of pack

In the current version, we support Rockchip RV1106 and RK356x devices (RV1103 support is possible but unverified). We have implemented RGA hardware acceleration for image processing on devices with RKNPU2 support, significantly improving processing speed. Additionally, we provide an optimized Android SDK with JNI integration and a [simple demo](https://github.com/HyperInspire/InspireFace/tree/master/android/InspireFaceExample). Corresponding [resource files](https://github.com/HyperInspire/InspireFace/releases/tag/v1.x) are available for these platforms.

- Supports the python library automatic upgrade model.
- Fixed some bugs.
- Update t3 series model: Formalizes the structure of the description file.
- Updated with the latest face landmark model.

In the current version, we support Rockchip RV1106 and RK356x devices (RV1103 support is possible but unverified). We have implemented RGA hardware acceleration for image processing on devices with RKNPU2 support, significantly improving processing speed. Additionally, we provide an optimized Android SDK with JNI integration and a [simple demo](https://github.com/HyperInspire/InspireFace/tree/master/android/InspireFaceExample). Corresponding [resource files](https://github.com/HyperInspire/InspireFace/releases/tag/v1.x) are available for these platforms.

- Open the parameter interface of some trackers.
- Add a similarity conversion tool.
- Update t3 series model: Formalizes the structure of the description file.

In the current version, we support Rockchip RV1106 and RK356x devices (RV1103 support is possible but unverified). We have implemented RGA hardware acceleration for image processing on devices with RKNPU2 support, significantly improving processing speed. Additionally, we provide an optimized Android SDK with JNI integration and a [simple demo](https://github.com/HyperInspire/InspireFace/tree/master/android/InspireFaceExample). Corresponding [resource files](https://github.com/HyperInspire/InspireFace/releases/tag/v1.x) are available for these platforms.

- Fixed some bugs running on RV1106/1103 and RK356x devices.
- Fixed a bug where the database persistence save path was invalid.

In the current version, we support Rockchip RV1106 and RK356x devices (RV1103 support is possible but unverified). We have implemented RGA hardware acceleration for image processing on devices with RKNPU2 support, significantly improving processing speed. Additionally, we provide an optimized Android SDK with JNI integration and a [simple demo](https://github.com/HyperInspire/InspireFace/tree/master/android/InspireFaceExample). Corresponding [resource files](https://github.com/HyperInspire/InspireFace/releases/tag/v1.x) are available for these platforms.

- Add support for NPU acceleration inference on RK356X platforms
- Make keypoint detection optional by default, auto-enabling only in tracking mode
- Fix face bounding box displacement issue in detection mode

In the current version, we have adapted and tested for Rockchip's RV1106 device and provided corresponding [resource files ](https://github.com/HyperInspire/InspireFace/releases/tag/v1.x)(it may support RV1103, but we haven't verified it with actual devices). We believe we will soon adapt to other Rockchip device models, such as RV356x and RV3588. Meanwhile, we are implementing RGA image hardware acceleration processing adaptation for devices supporting RKNPU2, which has improved the image processing speed on RK devices. We have improved the Android SDK based on Java Native Interface (JNI), optimized its size, and provided a [simple demo](https://github.com/HyperInspire/InspireFace/tree/master/android/InspireFaceExample).

- Add NPU inference support for Rockchip rv1106;
- Add new models to the Model Zoo;
- Fixed some bugs that were causing crashes;
- Added RGA acceleration support for some image processing interfaces on Rockchip devices with RKNPU2;
- Added a simple Android example demo;
- Added library support for Linux and macOS on x86 and arm64 platforms in PyPI, enabling rapid deployment;
- Release of precompiled libraries for macOS.