diff --git a/converters/yolo4/Dockerfile.l4t.tf15 b/converters/yolo4/Dockerfile.l4t.tf15
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+++ b/converters/yolo4/Dockerfile.l4t.tf15
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+FROM nvcr.io/nvidia/l4t-tensorflow:r32.6.1-tf1.15-py3
+
+RUN apt-get update && apt-get install -y git sudo
+RUN git clone https://github.com/jkjung-avt/tensorrt_demos.git /tensorrt_demos
+
+RUN cd /tensorrt_demos/yolo && ./install_pycuda.sh
+RUN apt-get update && apt-get install -y cmake build-essential unzip
+ADD install_protobuf.sh /install_protobuf.sh
+RUN /install_protobuf.sh
+RUN pip3 install onnx==1.4.1
+RUN cd /tensorrt_demos/yolo && ./download_yolo.sh
+ADD run.sh /run.sh
+
+
diff --git a/converters/yolo4/README.md b/converters/yolo4/README.md
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+++ b/converters/yolo4/README.md
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+Following the https://github.com/jkjung-avt/tensorrt_demos#demo-5-yolov4
+
+
+A build.sh file will convert pre-trained yolov3 and yolov4 models through ONNX to TensorRT engines. The implementation with a "yolo_layer" plugin has been updated to speed up inference time of the yolov3/yolov4 models.
+
+Current "yolo_layer" plugin implementation is based on TensorRT's IPluginV2IOExt. It only works for TensorRT 6+. "yolo_layer" developed plugin by referencing similar plugin code by wang-xinyu and dongfangduoshou123. So big thanks to both of them.
+
+
+Output will be copied to the ./model folder
+
+
+
+## Available models
+
+ | TensorRT engine | mAP @
IoU=0.5:0.95 | mAP @
IoU=0.5 | FPS on Nano |
+ |:------------------------|:---------------------:|:------------------:|:-----------:|
+ | yolov3-tiny-288 (FP16) | 0.077 | 0.158 | 35.8 |
+ | yolov3-tiny-416 (FP16) | 0.096 | 0.202 | 25.5 |
+ | yolov3-288 (FP16) | 0.331 | 0.601 | 8.16 |
+ | yolov3-416 (FP16) | 0.373 | 0.664 | 4.93 |
+ | yolov3-608 (FP16) | 0.376 | 0.665 | 2.53 |
+ | yolov3-spp-288 (FP16) | 0.339 | 0.594 | 8.16 |
+ | yolov3-spp-416 (FP16) | 0.391 | 0.664 | 4.82 |
+ | yolov3-spp-608 (FP16) | 0.410 | 0.685 | 2.49 |
+ | yolov4-tiny-288 (FP16) | 0.179 | 0.344 | 36.6 |
+ | yolov4-tiny-416 (FP16) | 0.196 | 0.387 | 25.5 |
+ | yolov4-288 (FP16) | 0.376 | 0.591 | 7.93 |
+ | yolov4-416 (FP16) | 0.459 | 0.700 | 4.62 |
+ | yolov4-608 (FP16) | 0.488 | 0.736 | 2.35 |
+ | yolov4-csp-256 (FP16) | 0.336 | 0.502 | 12.8 |
+ | yolov4-csp-512 (FP16) | 0.436 | 0.630 | 4.26 |
+ | yolov4x-mish-320 (FP16) | 0.400 | 0.581 | 4.79 |
+ | yolov4x-mish-640 (FP16) | 0.470 | 0.668 | 1.46 |
+
+
+Please update frigate/converters/yolo4/assets/run.sh to add necessary models
+
+Note:
+
+This will consume pretty significant amound of memory. You might consider extending swap on Jetson Nano
+
+Usage:
+
+cd ./frigate/converters/yolo4/
+./build.sh
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diff --git a/converters/yolo4/assets/install_protobuf.sh b/converters/yolo4/assets/install_protobuf.sh
new file mode 100755
index 000000000..245cabf4c
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+++ b/converters/yolo4/assets/install_protobuf.sh
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+#!/bin/bash
+
+set -e
+
+folder=${HOME}/src
+mkdir -p $folder
+
+echo "** Install requirements"
+sudo apt-get install -y autoconf libtool
+
+echo "** Download protobuf-3.8.0 sources"
+cd $folder
+if [ ! -f protobuf-python-3.8.0.zip ]; then
+ wget https://github.com/protocolbuffers/protobuf/releases/download/v3.8.0/protobuf-python-3.8.0.zip
+fi
+if [ ! -f protoc-3.8.0-linux-aarch_64.zip ]; then
+ wget https://github.com/protocolbuffers/protobuf/releases/download/v3.8.0/protoc-3.8.0-linux-aarch_64.zip
+fi
+
+echo "** Install protoc"
+unzip protobuf-python-3.8.0.zip
+unzip protoc-3.8.0-linux-aarch_64.zip -d protoc-3.8.0
+sudo cp protoc-3.8.0/bin/protoc /usr/local/bin/protoc
+
+echo "** Build and install protobuf-3.8.0 libraries"
+export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp
+cd protobuf-3.8.0/
+./autogen.sh
+./configure --prefix=/usr/local
+make -j$(nproc)
+make check
+sudo make install
+sudo ldconfig
+
+echo "** Update python3 protobuf module"
+# remove previous installation of python3 protobuf module
+sudo apt-get install -y python3-pip
+sudo pip3 uninstall -y protobuf
+sudo pip3 install Cython
+cd python/
+python3 setup.py build --cpp_implementation
+python3 setup.py test --cpp_implementation
+sudo python3 setup.py install --cpp_implementation
+
+echo "** Build protobuf-3.8.0 successfully"
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diff --git a/converters/yolo4/assets/run.sh b/converters/yolo4/assets/run.sh
new file mode 100755
index 000000000..7c19d224a
--- /dev/null
+++ b/converters/yolo4/assets/run.sh
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+#!/bin/bash
+
+set -xe
+cd /tensorrt_demos/plugins && make
+
+cd /tensorrt_demos/yolo
+for model in yolov4-tiny-288 \
+ yolov4-tiny-416 \
+ yolov4-288 \
+ yolov4-416 ; do
+ python3 yolo_to_onnx.py -m ${model}
+ python3 onnx_to_tensorrt.py -m ${model}
+ cp /tensorrt_demos/yolo/${model}.trt /model/${model}.trt
+done
diff --git a/converters/yolo4/build.sh b/converters/yolo4/build.sh
new file mode 100755
index 000000000..901096f0b
--- /dev/null
+++ b/converters/yolo4/build.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+
+mkdir -p $(pwd)/model
+
+docker build --tag models.yolo4 --file ./Dockerfile.l4t.tf15 ./assets/
+
+sudo docker run --rm -it --name models.yolo4 \
+ --mount type=tmpfs,target=/tmp/cache,tmpfs-size=1000000000 \
+ -v $(pwd)/model:/model:rw \
+ -v /tmp/argus_socket:/tmp/argus_socket \
+ -e NVIDIA_VISIBLE_DEVICES=all \
+ -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video \
+ --runtime=nvidia \
+ --privileged \
+ models.yolo4 /run.sh