Merge acb17a7b50 into 9d4aac2b8e

* docs: update chinese readme

* style: Improve the styling of the Chinese document jump tips bar in dark mode

* docs: add license translation

.github/workflows/ci.yml

@@ -15,7 +15,7 @@ concurrency:
   cancel-in-progress: true
 
 env:
-  PYTHON_VERSION: 3.9
+  PYTHON_VERSION: 3.11
 
 jobs:
   amd64_build:
@@ -225,3 +225,29 @@ jobs:
           sources: |
             ghcr.io/${{ steps.lowercaseRepo.outputs.lowercase }}:${{ env.SHORT_SHA }}-amd64
             ghcr.io/${{ steps.lowercaseRepo.outputs.lowercase }}:${{ env.SHORT_SHA }}-rpi
+  axera_build:
+    runs-on: ubuntu-22.04
+    name: AXERA Build
+    needs:
+      - amd64_build
+      - arm64_build
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v5
+        with:
+          persist-credentials: false
+      - name: Set up QEMU and Buildx
+        id: setup
+        uses: ./.github/actions/setup
+        with:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      - name: Build and push Axera build
+        uses: docker/bake-action@v6
+        with:
+          source: .
+          push: true
+          targets: axcl
+          files: docker/axcl/axcl.hcl
+          set: |
+            axcl.tags=${{ steps.setup.outputs.image-name }}-axcl
+            *.cache-from=type=gha

README_CN.md

@@ -1,28 +1,31 @@
 <p align="center">
-  <img align="center" alt="logo" src="docs/static/img/frigate.png">
+  <img align="center" alt="logo" src="docs/static/img/branding/frigate.png">
 </p>
 
-# Frigate - 一个具有实时目标检测的本地NVR
+# Frigate NVR™ - 一个具有实时目标检测的本地 NVR
 
-[English](https://github.com/blakeblackshear/frigate) | \[简体中文\] 
+[English](https://github.com/blakeblackshear/frigate) | \[简体中文\]
+
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 
 <a href="https://hosted.weblate.org/engage/frigate-nvr/-/zh_Hans/">
 <img src="https://hosted.weblate.org/widget/frigate-nvr/-/zh_Hans/svg-badge.svg" alt="翻译状态" />
 </a>
 
-一个完整的本地网络视频录像机（NVR），专为[Home Assistant](https://www.home-assistant.io)设计，具备AI物体检测功能。使用OpenCV和TensorFlow在本地为IP摄像头执行实时物体检测。
+一个完整的本地网络视频录像机（NVR），专为[Home Assistant](https://www.home-assistant.io)设计，具备 AI 目标/物体检测功能。使用 OpenCV 和 TensorFlow 在本地为 IP 摄像头执行实时物体检测。
 
-强烈推荐使用GPU或者AI加速器（例如[Google Coral加速器](https://coral.ai/products/) 或者 [Hailo](https://hailo.ai/)）。它们的性能甚至超过目前的顶级CPU，并且可以以极低的耗电实现更优的性能。
-- 通过[自定义组件](https://github.com/blakeblackshear/frigate-hass-integration)与Home Assistant紧密集成
-- 设计上通过仅在必要时和必要地点寻找物体，最大限度地减少资源使用并最大化性能
+强烈推荐使用 GPU 或者 AI 加速器（例如[Google Coral 加速器](https://coral.ai/products/) 或者 [Hailo](https://hailo.ai/)等）。它们的运行效率远远高于现在的顶级 CPU，并且功耗也极低。
+
+- 通过[自定义组件](https://github.com/blakeblackshear/frigate-hass-integration)与 Home Assistant 紧密集成
+- 设计上通过仅在必要时和必要地点寻找目标，最大限度地减少资源使用并最大化性能
 - 大量利用多进程处理，强调实时性而非处理每一帧
-- 使用非常低开销的运动检测来确定运行物体检测的位置
-- 使用TensorFlow进行物体检测，运行在单独的进程中以达到最大FPS
-- 通过MQTT进行通信，便于集成到其他系统中
+- 使用非常低开销的画面变动检测（也叫运动检测）来确定运行目标检测的位置
+- 使用 TensorFlow 进行目标检测，并运行在单独的进程中以达到最大 FPS
+- 通过 MQTT 进行通信，便于集成到其他系统中
 - 根据检测到的物体设置保留时间进行视频录制
-- 24/7全天候录制
-- 通过RTSP重新流传输以减少摄像头的连接数
-- 支持WebRTC和MSE，实现低延迟的实时观看
+- 24/7 全天候录制
+- 通过 RTSP 重新流传输以减少摄像头的连接数
+- 支持 WebRTC 和 MSE，实现低延迟的实时观看
 
 ## 社区中文翻译文档
 
@@ -32,39 +35,55 @@
 
 如果您想通过捐赠支持开发，请使用 [Github Sponsors](https://github.com/sponsors/blakeblackshear)。
 
+## 协议
+
+本项目采用 **MIT 许可证**授权。
+**代码部分**：本代码库中的源代码、配置文件和文档均遵循 [MIT 许可证](LICENSE)。您可以自由使用、修改和分发这些代码，但必须保留原始版权声明。
+
+**商标部分**：“Frigate”名称、“Frigate NVR”品牌以及 Frigate 的 Logo 为 **Frigate LLC 的商标**，**不在** MIT 许可证覆盖范围内。
+有关品牌资产的规范使用详情，请参阅我们的[《商标政策》](TRADEMARK.md)。
+
 ## 截图
 
 ### 实时监控面板
+
 <div>
 <img width="800" alt="实时监控面板" src="https://github.com/blakeblackshear/frigate/assets/569905/5e713cb9-9db5-41dc-947a-6937c3bc376e">
 </div>
 
 ### 简单的核查工作流程
+
 <div>
 <img width="800" alt="简单的审查工作流程" src="https://github.com/blakeblackshear/frigate/assets/569905/6fed96e8-3b18-40e5-9ddc-31e6f3c9f2ff">
 </div>
 
 ### 多摄像头可按时间轴查看
+
 <div>
 <img width="800" alt="多摄像头可按时间轴查看" src="https://github.com/blakeblackshear/frigate/assets/569905/d6788a15-0eeb-4427-a8d4-80b93cae3d74">
 </div>
 
 ### 内置遮罩和区域编辑器
+
 <div>
 <img width="800" alt="内置遮罩和区域编辑器" src="https://github.com/blakeblackshear/frigate/assets/569905/d7885fc3-bfe6-452f-b7d0-d957cb3e31f5">
 </div>
 
-
 ## 翻译
+
 我们使用 [Weblate](https://hosted.weblate.org/projects/frigate-nvr/) 平台提供翻译支持，欢迎参与进来一起完善。
 
-
 ## 非官方中文讨论社区
-欢迎加入中文讨论QQ群：[1043861059](https://qm.qq.com/q/7vQKsTmSz)
+
+欢迎加入中文讨论 QQ 群：[1043861059](https://qm.qq.com/q/7vQKsTmSz)
 
 Bilibili：https://space.bilibili.com/3546894915602564
 
-
 ## 中文社区赞助商
+
 [![EdgeOne](https://edgeone.ai/media/34fe3a45-492d-4ea4-ae5d-ea1087ca7b4b.png)](https://edgeone.ai/zh?from=github)
 本项目 CDN 加速及安全防护由 Tencent EdgeOne 赞助
+
+---
+
+**Copyright © 2025 Frigate LLC.**

docker/axcl/Dockerfile

@@ -0,0 +1,55 @@
+# syntax=docker/dockerfile:1.6
+
+# https://askubuntu.com/questions/972516/debian-frontend-environment-variable
+ARG DEBIAN_FRONTEND=noninteractive
+
+# Globally set pip break-system-packages option to avoid having to specify it every time
+ARG PIP_BREAK_SYSTEM_PACKAGES=1
+
+
+FROM frigate AS frigate-axcl
+ARG TARGETARCH
+ARG PIP_BREAK_SYSTEM_PACKAGES
+
+# Install axpyengine
+RUN wget https://github.com/AXERA-TECH/pyaxengine/releases/download/0.1.3.rc1/axengine-0.1.3-py3-none-any.whl -O /axengine-0.1.3-py3-none-any.whl
+RUN pip3 install -i https://mirrors.aliyun.com/pypi/simple/ /axengine-0.1.3-py3-none-any.whl \
+    && rm /axengine-0.1.3-py3-none-any.whl
+
+# Install axcl
+RUN if [ "$TARGETARCH" = "amd64" ]; then \
+        echo "Installing x86_64 version of axcl"; \
+        wget https://github.com/ivanshi1108/assets/releases/download/v0.16.2/axcl_host_x86_64_V3.6.5_20250908154509_NO4973.deb -O /axcl.deb; \
+    else \
+        echo "Installing aarch64 version of axcl"; \
+        wget https://github.com/ivanshi1108/assets/releases/download/v0.16.2/axcl_host_aarch64_V3.6.5_20250908154509_NO4973.deb -O /axcl.deb; \
+    fi
+
+RUN mkdir /unpack_axcl && \
+    dpkg-deb -x /axcl.deb /unpack_axcl && \
+    cp -R /unpack_axcl/usr/bin/axcl /usr/bin/ && \
+    cp -R /unpack_axcl/usr/lib/axcl /usr/lib/ && \
+    rm -rf /unpack_axcl /axcl.deb
+
+
+# Install axcl ffmpeg
+RUN mkdir -p /usr/lib/ffmpeg/axcl
+
+RUN if [ "$TARGETARCH" = "amd64" ]; then \
+        wget https://github.com/ivanshi1108/assets/releases/download/v0.16.2/ffmpeg-x64 -O /usr/lib/ffmpeg/axcl/ffmpeg && \
+        wget https://github.com/ivanshi1108/assets/releases/download/v0.16.2/ffprobe-x64 -O /usr/lib/ffmpeg/axcl/ffprobe; \
+    else \
+        wget https://github.com/ivanshi1108/assets/releases/download/v0.16.2/ffmpeg-aarch64 -O /usr/lib/ffmpeg/axcl/ffmpeg && \
+        wget https://github.com/ivanshi1108/assets/releases/download/v0.16.2/ffprobe-aarch64 -O /usr/lib/ffmpeg/axcl/ffprobe; \
+    fi
+
+RUN chmod +x /usr/lib/ffmpeg/axcl/ffmpeg /usr/lib/ffmpeg/axcl/ffprobe
+
+# Set ldconfig path
+RUN echo "/usr/lib/axcl" > /etc/ld.so.conf.d/ax.conf
+
+# Set env
+ENV PATH="$PATH:/usr/bin/axcl"
+ENV LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/lib/axcl"
+
+ENTRYPOINT ["sh", "-c", "ldconfig && exec /init"]

docker/axcl/axcl.hcl

@@ -0,0 +1,13 @@
+target frigate {
+  dockerfile = "docker/main/Dockerfile"
+  platforms = ["linux/amd64", "linux/arm64"]
+  target = "frigate"
+}
+
+target axcl {
+  dockerfile = "docker/axcl/Dockerfile"
+  contexts = {
+    frigate = "target:frigate",
+  }
+  platforms = ["linux/amd64", "linux/arm64"]
+}

docker/axcl/axcl.mk

@@ -0,0 +1,15 @@
+BOARDS += axcl
+
+local-axcl: version
+	docker buildx bake --file=docker/axcl/axcl.hcl axcl \
+		--set axcl.tags=frigate:latest-axcl \
+		--load
+
+build-axcl: version
+	docker buildx bake --file=docker/axcl/axcl.hcl axcl \
+		--set axcl.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-axcl
+
+push-axcl: build-axcl
+	docker buildx bake --file=docker/axcl/axcl.hcl axcl \
+		--set axcl.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-axcl \
+		--push

docker/axcl/user_installation.sh

@@ -0,0 +1,83 @@
+#!/bin/bash
+
+# Update package list and install dependencies
+sudo apt-get update
+sudo apt-get install -y build-essential cmake git wget pciutils kmod udev
+
+# Check if gcc-12 is needed
+current_gcc_version=$(gcc --version | head -n1 | awk '{print $NF}')
+gcc_major_version=$(echo $current_gcc_version | cut -d'.' -f1)
+
+if [[ $gcc_major_version -lt 12 ]]; then
+    echo "Current GCC version ($current_gcc_version) is lower than 12, installing gcc-12..."
+    sudo apt-get install -y gcc-12
+    sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 12
+    echo "GCC-12 installed and set as default"
+else
+    echo "Current GCC version ($current_gcc_version) is sufficient, skipping GCC installation"
+fi
+
+# Determine architecture
+arch=$(uname -m)
+download_url=""
+
+if [[ $arch == "x86_64" ]]; then
+    download_url="https://github.com/ivanshi1108/assets/releases/download/v0.16.2/axcl_host_x86_64_V3.6.5_20250908154509_NO4973.deb"
+    deb_file="axcl_host_x86_64_V3.6.5_20250908154509_NO4973.deb"
+elif [[ $arch == "aarch64" ]]; then
+    download_url="https://github.com/ivanshi1108/assets/releases/download/v0.16.2/axcl_host_aarch64_V3.6.5_20250908154509_NO4973.deb"
+    deb_file="axcl_host_aarch64_V3.6.5_20250908154509_NO4973.deb"
+else
+    echo "Unsupported architecture: $arch"
+    exit 1
+fi
+
+# Download AXCL driver
+echo "Downloading AXCL driver for $arch..."
+wget "$download_url" -O "$deb_file"
+
+if [ $? -ne 0 ]; then
+    echo "Failed to download AXCL driver"
+    exit 1
+fi
+
+# Install AXCL driver
+echo "Installing AXCL driver..."
+sudo dpkg -i "$deb_file"
+
+if [ $? -ne 0 ]; then
+    echo "Failed to install AXCL driver, attempting to fix dependencies..."
+    sudo apt-get install -f -y
+    sudo dpkg -i "$deb_file"
+
+    if [ $? -ne 0 ]; then
+        echo "AXCL driver installation failed"
+        exit 1
+    fi
+fi
+
+# Update environment
+echo "Updating environment..."
+source /etc/profile
+
+# Verify installation
+echo "Verifying AXCL installation..."
+if command -v axcl-smi &> /dev/null; then
+    echo "AXCL driver detected, checking AI accelerator status..."
+
+    axcl_output=$(axcl-smi 2>&1)
+    axcl_exit_code=$?
+
+    echo "$axcl_output"
+
+    if [ $axcl_exit_code -eq 0 ]; then
+        echo "AXCL driver installation completed successfully!"
+    else
+        echo "AXCL driver installed but no AI accelerator detected or communication failed."
+        echo "Please check if the AI accelerator is properly connected and powered on."
+        exit 1
+    fi
+else
+    echo "axcl-smi command not found. AXCL driver installation may have failed."
+    exit 1
+fi

docker/rocm/Dockerfile

@@ -15,7 +15,7 @@ ARG AMDGPU
 
 RUN apt update -qq && \
     apt install -y wget gpg && \
-    wget -O rocm.deb https://repo.radeon.com/amdgpu-install/7.1/ubuntu/jammy/amdgpu-install_7.1.70100-1_all.deb && \
+    wget -O rocm.deb https://repo.radeon.com/amdgpu-install/7.1.1/ubuntu/jammy/amdgpu-install_7.1.1.70101-1_all.deb && \
     apt install -y ./rocm.deb && \
     apt update && \
     apt install -qq -y rocm

docker/rocm/rocm.hcl

@@ -2,7 +2,7 @@ variable "AMDGPU" {
   default = "gfx900"
 }
 variable "ROCM" {
-  default = "7.1.0"
+  default = "7.1.1"
 }
 variable "HSA_OVERRIDE_GFX_VERSION" {
   default = ""

docs/docs/configuration/object_detectors.md

@@ -49,6 +49,11 @@ Frigate supports multiple different detectors that work on different types of ha
 
 - [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs.
 
+**AXERA** <CommunityBadge />
+
+- [AXEngine](#axera): axmodels can run on AXERA AI acceleration.
+
+
 **For Testing**
 
 - [CPU Detector (not recommended for actual use](#cpu-detector-not-recommended): Use a CPU to run tflite model, this is not recommended and in most cases OpenVINO can be used in CPU mode with better results.
@@ -1438,6 +1443,41 @@ model:
   input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
 ```
 
+## AXERA
+
+Hardware accelerated object detection is supported on the following SoCs:
+
+- AX650N
+- AX8850N
+
+This implementation uses the [AXera Pulsar2 Toolchain](https://huggingface.co/AXERA-TECH/Pulsar2).
+
+See the [installation docs](../frigate/installation.md#axera) for information on configuring the AXEngine hardware.
+
+### Configuration
+
+When configuring the AXEngine detector, you have to specify the model name.
+
+#### yolov9
+
+A yolov9 model is provided in the container at /axmodels and is used by this detector type by default.
+
+Use the model configuration shown below when using the axengine detector with the default axmodel:
+
+```yaml
+detectors:
+  axengine:
+    type: axengine
+
+model:
+  path: frigate-yolov9-tiny
+  model_type: yolo-generic
+  width: 320
+  height: 320
+  tensor_format: bgr
+  labelmap_path: /labelmap/coco-80.txt
+```
+
 # Models
 
 Some model types are not included in Frigate by default.

docs/docs/frigate/hardware.md

@@ -104,6 +104,10 @@ Frigate supports multiple different detectors that work on different types of ha
 
 - [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs to provide efficient object detection.
 
+**AXERA** <CommunityBadge />
+
+- [AXEngine](#axera): axera models can run on AXERA NPUs via AXEngine, delivering highly efficient object detection.
+
 :::
 
 ### Hailo-8
@@ -287,6 +291,14 @@ The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms fo
 | ssd mobilenet | ~ 25 ms                         |
 | yolov5m       | ~ 118 ms                        |
 
+### AXERA
+
+- **AXEngine** Default model is **yolov9**
+
+| Name             | AXERA AX650N/AX8850N Inference Time |
+| ---------------- | ----------------------------------- |
+| yolov9-tiny      | ~ 4 ms                              |
+
 ## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)
 
 This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.
@@ -307,4 +319,4 @@ Basically - When you increase the resolution and/or the frame rate of the stream
 
 YES! The Coral does not help with decoding video streams.
 
-Decompressing video streams takes a significant amount of CPU power. Video compression uses key frames (also known as I-frames) to send a full frame in the video stream. The following frames only include the difference from the key frame, and the CPU has to compile each frame by merging the differences with the key frame. [More detailed explanation](https://support.video.ibm.com/hc/en-us/articles/18106203580316-Keyframes-InterFrame-Video-Compression). Higher resolutions and frame rates mean more processing power is needed to decode the video stream, so try and set them on the camera to avoid unnecessary decoding work.
+Decompressing video streams takes a significant amount of CPU power. Video compression uses key frames (also known as I-frames) to send a full frame in the video stream. The following frames only include the difference from the key frame, and the CPU has to compile each frame by merging the differences with the key frame. [More detailed explanation](https://support.video.ibm.com/hc/en-us/articles/18106203580316-Keyframes-InterFrame-Video-Compression). Higher resolutions and frame rates mean more processing power is needed to decode the video stream, so try and set them on the camera to avoid unnecessary decoding work.

docs/docs/frigate/installation.md

@@ -287,6 +287,42 @@ or add these options to your `docker run` command:
 
 Next, you should configure [hardware object detection](/configuration/object_detectors#synaptics) and [hardware video processing](/configuration/hardware_acceleration_video#synaptics).
 
+### AXERA
+
+<details>
+<summary>AXERA accelerators</summary>
+AXERA accelerators are available in an M.2 form factor, compatible with both Raspberry Pi and Orange Pi. This form factor has also been successfully tested on x86 platforms, making it a versatile choice for various computing environments.
+
+#### Installation
+
+Using AXERA accelerators requires the installation of the AXCL driver. We provide a convenient Linux script to complete this installation.
+
+Follow these steps for installation:
+
+1. Copy or download [this script](https://github.com/ivanshi1108/assets/releases/download/v0.16.2/user_installation.sh).
+2. Ensure it has execution permissions with `sudo chmod +x user_installation.sh`
+3. Run the script with `./user_installation.sh`
+
+#### Setup
+
+To set up Frigate, follow the default installation instructions, for example: `ghcr.io/blakeblackshear/frigate:stable`
+
+Next, grant Docker permissions to access your hardware by adding the following lines to your `docker-compose.yml` file:
+
+```yaml
+devices:
+  - /dev/axcl_host
+  - /dev/ax_mmb_dev
+  - /dev/msg_userdev
+```
+
+If you are using `docker run`, add this option to your command `--device /dev/axcl_host --device /dev/ax_mmb_dev --device /dev/msg_userdev`
+
+#### Configuration
+
+Finally, configure [hardware object detection](/configuration/object_detectors#axera) to complete the setup.
+</details>
+
 ## Docker
 
 Running through Docker with Docker Compose is the recommended install method.

docs/src/components/LanguageAlert/styles.module.css

@@ -1,13 +1,18 @@
 .alert {
-    padding: 12px;
-    background: #fff8e6;
-    border-bottom: 1px solid #ffd166;
-    text-align: center;
-    font-size: 15px;
-  }
-  
-  .alert a {
-    color: #1890ff;
-    font-weight: 500;
-    margin-left: 6px;
-  }
+  padding: 12px;
+  background: #fff8e6;
+  border-bottom: 1px solid #ffd166;
+  text-align: center;
+  font-size: 15px;
+}
+
+[data-theme="dark"] .alert {
+  background: #3b2f0b;
+  border-bottom: 1px solid #665c22;
+}
+
+.alert a {
+  color: #1890ff;
+  font-weight: 500;
+  margin-left: 6px;
+}

frigate/detectors/plugins/axengine.py

@@ -0,0 +1,86 @@
+import logging
+import os.path
+import re
+import urllib.request
+from typing import Literal
+
+import axengine as axe
+
+from frigate.const import MODEL_CACHE_DIR
+from frigate.detectors.detection_api import DetectionApi
+from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
+from frigate.util.model import post_process_yolo
+
+logger = logging.getLogger(__name__)
+
+DETECTOR_KEY = "axengine"
+
+supported_models = {
+    ModelTypeEnum.yologeneric: "frigate-yolov9-.*$",
+}
+
+model_cache_dir = os.path.join(MODEL_CACHE_DIR, "axengine_cache/")
+
+
+class AxengineDetectorConfig(BaseDetectorConfig):
+    type: Literal[DETECTOR_KEY]
+
+
+class Axengine(DetectionApi):
+    type_key = DETECTOR_KEY
+
+    def __init__(self, config: AxengineDetectorConfig):
+        logger.info("__init__ axengine")
+        super().__init__(config)
+        self.height = config.model.height
+        self.width = config.model.width
+        model_path = config.model.path or "frigate-yolov9-tiny"
+        model_props = self.parse_model_input(model_path)
+        self.session = axe.InferenceSession(model_props["path"])
+
+    def __del__(self):
+        pass
+
+    def parse_model_input(self, model_path):
+        model_props = {}
+        model_props["preset"] = True
+
+        model_matched = False
+
+        for model_type, pattern in supported_models.items():
+            if re.match(pattern, model_path):
+                model_matched = True
+                model_props["model_type"] = model_type
+
+        if model_matched:
+            model_props["filename"] = model_path + ".axmodel"
+            model_props["path"] = model_cache_dir + model_props["filename"]
+
+            if not os.path.isfile(model_props["path"]):
+                self.download_model(model_props["filename"])
+        else:
+            supported_models_str = ", ".join(model[1:-1] for model in supported_models)
+            raise Exception(
+                f"Model {model_path} is unsupported. Provide your own model or choose one of the following: {supported_models_str}"
+            )
+        return model_props
+
+    def download_model(self, filename):
+        if not os.path.isdir(model_cache_dir):
+            os.mkdir(model_cache_dir)
+
+        GITHUB_ENDPOINT = os.environ.get("GITHUB_ENDPOINT", "https://github.com")
+        urllib.request.urlretrieve(
+            f"{GITHUB_ENDPOINT}/ivanshi1108/assets/releases/download/v0.16.2/{filename}",
+            model_cache_dir + filename,
+        )
+
+    def detect_raw(self, tensor_input):
+        results = None
+        results = self.session.run(None, {"images": tensor_input})
+        if self.detector_config.model.model_type == ModelTypeEnum.yologeneric:
+            return post_process_yolo(results, self.width, self.height)
+        else:
+            raise ValueError(
+                f'Model type "{self.detector_config.model.model_type}" is currently not supported.'
+            )