This script converts the OpenVINO IR model to Tensorflow's saved_model, tflite, h5, tfjs, tftrt(TensorRT), CoreML, EdgeTPU, ONNX and pb. PyTorch (NCHW) -> ONNX (NCHW) -> OpenVINO (NCHW) -> openvino2tensorflow -> Tensorflow/Keras (NHWC) -> TFLite (NHWC). And the conversion from .pb to saved_model and from saved_model to .pb and from .pb to .tflite and saved_model to .tflite and saved_model to onnx. Support for building environments with Docker. It is possible to directly access the host PC GUI and the camera to verify the operation. NVIDIA GPU (dGPU) support.
Work in progress now.
I'm continuing to add more layers of support and bug fixes on a daily basis. If you have a model that you are having trouble converting, please share the .bin
and .xml
with the issue. I will try to convert as much as possible.
- TensorFlow v2.5.0+
pip3 install --upgrade tensorflow
orpip3 install --upgrade tf-nightly
- OpenVINO 2021.3.394+
- Python 3.6+
- tensorflowjs
pip3 install --upgrade tensorflowjs
- tensorrt
- coremltools
pip3 install --upgrade coremltools
- onnx
pip3 install --upgrade onnx
- tf2onnx
pip3 install --upgrade tf2onnx
- tensorflow-datasets
pip3 install --upgrade tensorflow-datasets
- edgetpu_compiler
- Docker
-
PyTorch (NCHW) -> ONNX (NCHW) -> OpenVINO (NCHW) ->
- ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> TFLite (NHWC) - ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> TFJS (NHWC) - ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> TF-TRT (NHWC) - ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> EdgeTPU (NHWC) - ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> CoreML (NHWC) - ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> ONNX (NHWC) - ->
openvino2tensorflow
-> Myriad Inference Engine Blob (NCHW)
- ->
-
Caffe (NCHW) -> OpenVINO (NCHW) ->
- ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> TFLite (NHWC) - ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> TFJS (NHWC) - ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> TF-TRT (NHWC) - ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> EdgeTPU (NHWC) - ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> CoreML (NHWC) - ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> ONNX (NHWC) - ->
openvino2tensorflow
-> Myriad Inference Engine Blob (NCHW)
- ->
-
MXNet (NCHW) -> OpenVINO (NCHW) ->
- ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> TFLite (NHWC) - ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> TFJS (NHWC) - ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> TF-TRT (NHWC) - ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> EdgeTPU (NHWC) - ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> CoreML (NHWC) - ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> ONNX (NHWC) - ->
openvino2tensorflow
-> Myriad Inference Engine Blob (NCHW)
- ->
-
Keras (NHWC) -> OpenVINO (NCHW・Optimized) ->
- ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> TFLite (NHWC) - ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> TFJS (NHWC) - ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> TF-TRT (NHWC) - ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> EdgeTPU (NHWC) - ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> CoreML (NHWC) - ->
openvino2tensorflow
-> Tensorflow/Keras (NHWC) -> ONNX (NHWC) - ->
openvino2tensorflow
-> Myriad Inference Engine Blob (NCHW)
- ->
-
saved_model ->
saved_model_to_pb
-> pb -
saved_model ->
- ->
saved_model_to_tflite
-> TFLite - ->
saved_model_to_tflite
-> TFJS - ->
saved_model_to_tflite
-> TF-TRT - ->
saved_model_to_tflite
-> EdgeTPU - ->
saved_model_to_tflite
-> CoreML - ->
saved_model_to_tflite
-> ONNX
- ->
-
pb ->
pb_to_tflite
-> TFLite -
pb ->
pb_to_saved_model
-> saved_model
- Currently, there are problems with the Reshape operation of 5D Tensor.
No. | OpenVINO Layer | TF Layer | Remarks |
---|---|---|---|
1 | Parameter | Input | |
2 | Const | Constant, Bias | |
3 | Convolution | Conv2D | |
4 | Add | Add | |
5 | ReLU | ReLU | |
6 | PReLU | PReLU | Maximum(0.0,x)+alpha*Minimum(0.0,x) |
7 | MaxPool | MaxPool2D | |
8 | AvgPool | AveragePooling2D | |
9 | GroupConvolution | DepthwiseConv2D, Conv2D/Split/Concat | |
10 | ConvolutionBackpropData | Conv2DTranspose | |
11 | Concat | Concat | |
12 | Multiply | Multiply | |
13 | Tan | Tan | |
14 | Tanh | Tanh | |
15 | Elu | Elu | |
16 | Sigmoid | Sigmoid | |
17 | HardSigmoid | hard_sigmoid | |
18 | SoftPlus | SoftPlus | |
19 | Swish | Swish | You can replace swish and hard-swish with each other by using the "--replace_swish_and_hardswish" option |
20 | Interpolate | ResizeNearestNeighbor, ResizeBilinear | |
21 | ShapeOf | Shape | |
22 | Convert | Cast | |
23 | StridedSlice | Strided_Slice | |
24 | Pad | Pad, MirrorPad | |
25 | Clamp | ReLU6, Clip | |
26 | TopK | ArgMax, top_k | |
27 | Transpose | Transpose | |
28 | Squeeze | Squeeze | |
29 | Unsqueeze | Identity, expand_dims | WIP |
30 | ReduceMean | reduce_mean | |
31 | ReduceMax | reduce_max | |
32 | ReduceMin | reduce_min | |
33 | ReduceSum | reduce_sum | |
34 | ReduceProd | reduce_prod | |
35 | Subtract | Subtract | |
36 | MatMul | MatMul | |
37 | Reshape | Reshape | |
38 | Range | Range | WIP |
39 | Exp | Exp | |
40 | Abs | Abs | |
41 | SoftMax | SoftMax | |
42 | Negative | Negative | |
43 | Maximum | Maximum | No broadcast |
44 | Minimum | Minimum | No broadcast |
45 | Acos | Acos | |
46 | Acosh | Acosh | |
47 | Asin | Asin | |
48 | Asinh | Asinh | |
49 | Atan | Atan | |
50 | Atanh | Atanh | |
51 | Ceiling | Ceil | |
52 | Cos | Cos | |
53 | Cosh | Cosh | |
54 | Sin | Sin | |
55 | Sinh | Sinh | |
56 | Gather | Gather | |
57 | Divide | Divide, FloorDiv | |
58 | Erf | Erf | |
59 | Floor | Floor | |
60 | FloorMod | FloorMod | |
61 | HSwish | HardSwish | x*ReLU6(x+3)*0.16666667, You can replace swish and hard-swish with each other by using the "--replace_swish_and_hardswish" option |
62 | Log | Log | |
63 | Power | Pow | No broadcast |
64 | Mish | Mish | x*Tanh(softplus(x)) |
65 | Selu | Selu | |
66 | Equal | equal | |
67 | NotEqual | not_equal | |
68 | Greater | greater | |
69 | GreaterEqual | greater_equal | |
70 | Less | less | |
71 | LessEqual | less_equal | |
72 | Select | Select | No broadcast |
73 | LogicalAnd | logical_and | |
74 | LogicalNot | logical_not | |
75 | LogicalOr | logical_or | |
76 | LogicalXor | logical_xor | |
77 | Broadcast | broadcast_to, ones, Multiply | numpy / bidirectional mode, WIP |
78 | Split | Split | |
79 | VariadicSplit | Split, Slice, SplitV | |
80 | MVN | reduce_mean, sqrt, reduce_variance | (x-reduce_mean(x))/sqrt(reduce_variance(x)+eps) |
81 | NonZero | not_equal, boolean_mask | |
82 | ReduceL2 | Multiply, reduce_sum, rsqrt | |
83 | SpaceToDepth | SpaceToDepth | |
84 | DepthToSpace | DepthToSpace | |
85 | Sqrt | sqrt | |
86 | SquaredDifference | squared_difference | |
87 | FakeQuantize | subtract, multiply, round, greater, where, less_equal, add | |
88 | Tile | tile | |
89 | GatherND | gather_nd | |
90 | NonMaxSuppression | non_max_suppression | WIP. Only available for batch size 1. To simplify post-processing ignore all OPs after non_max_suppression. |
91 | Result | Identity | Output |
You do not need to install any packages other than Docker.
$ docker pull pinto0309/openvino2tensorflow
or
$ docker build -t pinto0309/openvino2tensorflow:latest .
# If conversion to TF-TRT is not required
$ xhost +local: && \
docker run -it --rm \
-v `pwd`:/home/user/workdir \
-v /tmp/.X11-unix/:/tmp/.X11-unix:rw \
--device /dev/video0:/dev/video0:mwr \
--net=host \
-e XDG_RUNTIME_DIR=$XDG_RUNTIME_DIR \
-e DISPLAY=$DISPLAY \
--privileged \
pinto0309/openvino2tensorflow:latest
$ cd workdir
# If you need to convert to TF-TRT
$ xhost +local: && \
docker run --gpus all -it --rm \
-v `pwd`:/home/user/workdir \
-v /tmp/.X11-unix/:/tmp/.X11-unix:rw \
--device /dev/video0:/dev/video0:mwr \
--net=host \
-e XDG_RUNTIME_DIR=$XDG_RUNTIME_DIR \
-e DISPLAY=$DISPLAY \
--privileged \
pinto0309/openvino2tensorflow:latest
$ cd workdir
To install using the Python Package Index (PyPI), use the following command.
$ pip3 install openvino2tensorflow --upgrade
To install with the latest source code of the main branch, use the following command.
$ pip3 install git+https://github.com/PINTO0309/openvino2tensorflow --upgrade
usage: openvino2tensorflow
[-h]
--model_path MODEL_PATH
[--model_output_path MODEL_OUTPUT_PATH]
[--output_saved_model]
[--output_h5]
[--output_weight_and_json]
[--output_pb]
[--output_no_quant_float32_tflite]
[--output_weight_quant_tflite]
[--output_float16_quant_tflite]
[--output_integer_quant_tflite]
[--output_full_integer_quant_tflite]
[--output_integer_quant_type OUTPUT_INTEGER_QUANT_TYPE]
[--string_formulas_for_normalization STRING_FORMULAS_FOR_NORMALIZATION]
[--calib_ds_type CALIB_DS_TYPE]
[--ds_name_for_tfds_for_calibration DS_NAME_FOR_TFDS_FOR_CALIBRATION]
[--split_name_for_tfds_for_calibration SPLIT_NAME_FOR_TFDS_FOR_CALIBRATION]
[--download_dest_folder_path_for_the_calib_tfds DOWNLOAD_DEST_FOLDER_PATH_FOR_THE_CALIB_TFDS]
[--tfds_download_flg]
[--load_dest_file_path_for_the_calib_npy LOAD_DEST_FILE_PATH_FOR_THE_CALIB_NPY]
[--output_tfjs]
[--output_tftrt]
[--output_coreml]
[--output_edgetpu]
[--output_onnx]
[--onnx_opset ONNX_OPSET]
[--output_myriad]
[--vpu_number_of_shaves VPU_NUMBER_OF_SHAVES]
[--vpu_number_of_cmx_slices VPU_NUMBER_OF_CMX_SLICES]
[--replace_swish_and_hardswish]
[--optimizing_hardswish_for_edgetpu]
[--replace_prelu_and_minmax]
[--yolact]
[--restricted_resize_image_mode]
[--weight_replacement_config WEIGHT_REPLACEMENT_CONFIG]
[--debug]
[--debug_layer_number DEBUG_LAYER_NUMBER]
optional arguments:
-h, --help
show this help message and exit
--model_path MODEL_PATH
input IR model path (.xml)
--model_output_path MODEL_OUTPUT_PATH
The output folder path of the converted model file
--output_saved_model
saved_model output switch
--output_h5
.h5 output switch
--output_weight_and_json
weight of h5 and json output switch
--output_pb
.pb output switch
--output_no_quant_float32_tflite
float32 tflite output switch
--output_weight_quant_tflite
weight quant tflite output switch
--output_float16_quant_tflite
float16 quant tflite output switch
--output_integer_quant_tflite
integer quant tflite output switch
--output_full_integer_quant_tflite
full integer quant tflite output switch
--output_integer_quant_type OUTPUT_INTEGER_QUANT_TYPE
Input and output types when doing Integer Quantization
('int8 (default)' or 'uint8')
--string_formulas_for_normalization STRING_FORMULAS_FOR_NORMALIZATION
String formulas for normalization. It is evaluated by
Pythons eval() function.
Default: '(data - [127.5,127.5,127.5]) / [127.5,127.5,127.5]'
--calib_ds_type CALIB_DS_TYPE
Types of data sets for calibration. tfds or numpy
Default: numpy
--ds_name_for_tfds_for_calibration DS_NAME_FOR_TFDS_FOR_CALIBRATION
Dataset name for TensorFlow Datasets for calibration.
https://www.tensorflow.org/datasets/catalog/overview
--split_name_for_tfds_for_calibration SPLIT_NAME_FOR_TFDS_FOR_CALIBRATION
Split name for TensorFlow Datasets for calibration.
https://www.tensorflow.org/datasets/catalog/overview
--download_dest_folder_path_for_the_calib_tfds DOWNLOAD_DEST_FOLDER_PATH_FOR_THE_CALIB_TFDS
Download destination folder path for the calibration
dataset. Default: $HOME/TFDS
--tfds_download_flg
True to automatically download datasets from
TensorFlow Datasets. True or False
--load_dest_file_path_for_the_calib_npy LOAD_DEST_FILE_PATH_FOR_THE_CALIB_NPY
The path from which to load the .npy file containing
the numpy binary version of the calibration data.
Default: sample_npy/calibration_data_img_sample.npy
--output_tfjs
tfjs model output switch
--output_tftrt
tftrt model output switch
--output_coreml
coreml model output switch
--output_edgetpu
edgetpu model output switch
--output_onnx
onnx model output switch
--onnx_opset ONNX_OPSET
onnx opset version number
--output_myriad
myriad inference engine blob output switch
--vpu_number_of_shaves VPU_NUMBER_OF_SHAVES
vpu number of shaves. Default: 4
--vpu_number_of_cmx_slices VPU_NUMBER_OF_CMX_SLICES
vpu number of cmx slices. Default: 4
--replace_swish_and_hardswish
Replace swish and hard-swish with each other
--optimizing_hardswish_for_edgetpu
Optimizing hardswish for edgetpu
--replace_prelu_and_minmax
Replace prelu and minimum/maximum with each other
--yolact
Specify when converting the Yolact model
--restricted_resize_image_mode
Specify this if the upsampling contains OPs that are
not scaled by integer multiples. Optimization for
EdgeTPU will be disabled.
--weight_replacement_config WEIGHT_REPLACEMENT_CONFIG
Replaces the value of Const for each layer_id defined
in json. Specify the path to the json file.
'weight_replacement_config.json'
--debug
debug mode switch
--debug_layer_number DEBUG_LAYER_NUMBER
The last layer number to output when debugging. Used
only when --debug=True
usage: saved_model_to_tflite
[-h]
--saved_model_dir_path SAVED_MODEL_DIR_PATH
[--signature_def SIGNATURE_DEF]
[--input_shapes INPUT_SHAPES]
[--model_output_dir_path MODEL_OUTPUT_DIR_PATH]
[--output_no_quant_float32_tflite]
[--output_weight_quant_tflite]
[--output_float16_quant_tflite]
[--output_integer_quant_tflite]
[--output_full_integer_quant_tflite]
[--output_integer_quant_type OUTPUT_INTEGER_QUANT_TYPE]
[--string_formulas_for_normalization STRING_FORMULAS_FOR_NORMALIZATION]
[--calib_ds_type CALIB_DS_TYPE]
[--ds_name_for_tfds_for_calibration DS_NAME_FOR_TFDS_FOR_CALIBRATION]
[--split_name_for_tfds_for_calibration SPLIT_NAME_FOR_TFDS_FOR_CALIBRATION]
[--download_dest_folder_path_for_the_calib_tfds DOWNLOAD_DEST_FOLDER_PATH_FOR_THE_CALIB_TFDS]
[--tfds_download_flg]
[--load_dest_file_path_for_the_calib_npy LOAD_DEST_FILE_PATH_FOR_THE_CALIB_NPY]
[--output_tfjs]
[--output_tftrt]
[--output_coreml]
[--output_edgetpu]
[--output_onnx]
[--onnx_opset ONNX_OPSET]
optional arguments:
-h, --help
show this help message and exit
--saved_model_dir_path SAVED_MODEL_DIR_PATH
Input saved_model dir path
--signature_def SIGNATURE_DEF
Specifies the signature name to load from saved_model
--input_shapes INPUT_SHAPES
Overwrites an undefined input dimension (None or -1).
Specify the input shape in [n,h,w,c] format.
For non-4D tensors, specify [a,b,c,d,e], [a,b], etc.
A comma-separated list if there are multiple inputs.
(e.g.) --input_shapes [1,256,256,3],[1,64,64,3],[1,2,16,16,3]
--model_output_dir_path MODEL_OUTPUT_DIR_PATH
The output folder path of the converted model file
--output_no_quant_float32_tflite
float32 tflite output switch
--output_weight_quant_tflite
weight quant tflite output switch
--output_float16_quant_tflite
float16 quant tflite output switch
--output_integer_quant_tflite
integer quant tflite output switch
--output_full_integer_quant_tflite
full integer quant tflite output switch
--output_integer_quant_type OUTPUT_INTEGER_QUANT_TYPE
Input and output types when doing Integer Quantization
('int8 (default)' or 'uint8')
--string_formulas_for_normalization STRING_FORMULAS_FOR_NORMALIZATION
String formulas for normalization. It is evaluated by
Pythons eval() function.
Default: '(data - [127.5,127.5,127.5]) / [127.5,127.5,127.5]'
--calib_ds_type CALIB_DS_TYPE
Types of data sets for calibration. tfds or numpy
Default: numpy
--ds_name_for_tfds_for_calibration DS_NAME_FOR_TFDS_FOR_CALIBRATION
Dataset name for TensorFlow Datasets for calibration.
https://www.tensorflow.org/datasets/catalog/overview
--split_name_for_tfds_for_calibration SPLIT_NAME_FOR_TFDS_FOR_CALIBRATION
Split name for TensorFlow Datasets for calibration.
https://www.tensorflow.org/datasets/catalog/overview
--download_dest_folder_path_for_the_calib_tfds DOWNLOAD_DEST_FOLDER_PATH_FOR_THE_CALIB_TFDS
Download destination folder path for the calibration
dataset. Default: $HOME/TFDS
--tfds_download_flg
True to automatically download datasets from
TensorFlow Datasets. True or False
--load_dest_file_path_for_the_calib_npy LOAD_DEST_FILE_PATH_FOR_THE_CALIB_NPY
The path from which to load the .npy file containing
the numpy binary version of the calibration data.
Default: sample_npy/calibration_data_img_sample.npy
--output_tfjs
tfjs model output switch
--output_tftrt
tftrt model output switch
--output_coreml
coreml model output switch
--output_edgetpu
edgetpu model output switch
--output_onnx
onnx model output switch
--onnx_opset ONNX_OPSET
onnx opset version number
usage: pb_to_saved_model
[-h]
--pb_file_path PB_FILE_PATH
--inputs INPUTS
--outputs OUTPUTS
[--model_output_path MODEL_OUTPUT_PATH]
optional arguments:
-h, --help
show this help message and exit
--pb_file_path PB_FILE_PATH
Input .pb file path (.pb)
--inputs INPUTS
(e.g.1) input:0,input:1,input:2
(e.g.2) images:0,input:0,param:0
--outputs OUTPUTS
(e.g.1) output:0,output:1,output:2
(e.g.2) Identity:0,Identity:1,output:0
--model_output_path MODEL_OUTPUT_PATH
The output folder path of the converted model file
usage: pb_to_tflite
[-h]
--pb_file_path PB_FILE_PATH
--inputs INPUTS
--outputs OUTPUTS
[--model_output_path MODEL_OUTPUT_PATH]
optional arguments:
-h, --help
show this help message and exit
--pb_file_path PB_FILE_PATH
Input .pb file path (.pb)
--inputs INPUTS
(e.g.1) input,input_1,input_2
(e.g.2) images,input,param
--outputs OUTPUTS
(e.g.1) output,output_1,output_2
(e.g.2) Identity,Identity_1,output
--model_output_path MODEL_OUTPUT_PATH
The output folder path of the converted model file
usage: saved_model_to_pb
[-h]
--saved_model_dir_path SAVED_MODEL_DIR_PATH
[--model_output_dir_path MODEL_OUTPUT_DIR_PATH]
[--signature_name SIGNATURE_NAME]
optional arguments:
-h, --help
show this help message and exit
--saved_model_dir_path SAVED_MODEL_DIR_PATH
Input saved_model dir path
--model_output_dir_path MODEL_OUTPUT_DIR_PATH
The output folder path of the converted model file (.pb)
--signature_name SIGNATURE_NAME
Signature name to be extracted from saved_model
usage: ir_weight_extractor
[-h]
-m MODEL
-o OUTPUT_PATH
optional arguments:
-h, --help
show this help message and exit
-m MODEL, --model MODEL
input IR model path
-o OUTPUT_PATH, --output_path OUTPUT_PATH
weights output folder path
OutOfMemory may occur when converting to saved_model or h5 when the file size of the original model is large, please try the conversion to a pb file alone.
$ openvino2tensorflow \
--model_path=openvino/448x448/FP32/Resnet34_3inputs_448x448_20200609.xml \
--output_saved_model \
--output_pb \
--output_weight_quant_tflite \
--output_float16_quant_tflite \
--output_no_quant_float32_tflite
This tool is useful if you want to check the internal structure of pb files, tflite files, .h5 files, coreml files and IR (.xml) files. https://lutzroeder.github.io/netron/
$ pb_to_saved_model \
--pb_file_path model_float32.pb \
--inputs inputs:0 \
--outputs Identity:0
$ pb_to_tflite \
--pb_file_path model_float32.pb \
--inputs inputs \
--outputs Identity,Identity_1,Identity_2
$ saved_model_to_pb \
--saved_model_dir_path saved_model \
--model_output_dir_path pb_from_saved_model \
--signature_name serving_default
$ python3 ${INTEL_OPENVINO_DIR}/deployment_tools/model_optimizer/mo_tf.py \
--saved_model_dir saved_model \
--output_dir openvino/reverse
$ saved_model_cli show \
--dir saved_model \
--tag_set serve \
--signature_def serving_default
If the transformation behavior of Reshape
, Transpose
, etc. does not go as expected, you can force the Const
content to change by defining weights and constant values in a JSON file and having it read in.
$ openvino2tensorflow \
--model_path=xxx.xml \
--output_saved_model \
--output_pb \
--output_weight_quant_tflite \
--output_float16_quant_tflite \
--output_no_quant_float32_tflite \
--weight_replacement_config weight_replacement_config_sample.json
Structure of JSON sample
{
"format_version": 1,
"layers": [
{
"layer_id": "1123",
"replace_mode": "direct",
"values": [
1,
2,
513,
513
]
},
{
"layer_id": "1125",
"replace_mode": "npy",
"values": "weights_sample/1125.npy"
}
]
}
$ view_npy --npy_file_path sample_npy/calibration_data_img_sample.npy
Press the Q
button to display the next image. calibration_data_img_sample.npy
contains 20 images extracted from the MS-COCO data set.
https://digital-standard.com/threedpose/models/Resnet34_3inputs_448x448_20200609.onnx
ONNX | OpenVINO | TFLite |
---|---|---|
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- u-2-net
- mobilenet-v2-pytorch
- midasnet
- footprints
- efficientnet-b0-pytorch
- efficientdet-d0
- dense_depth
- deeplabv3
- colorization-v2-norebal
- age-gender-recognition-retail-0013
- resnet
- arcface
- emotion-ferplus
- mosaic
- retinanet
- shufflenet-v2
- squeezenet
- version-RFB-320
- yolov4
- yolov4x-mish
- ThreeDPoseUnityBarracuda - Resnet34_3inputs_448x448
- efficientnet-lite4
- nanodet
- yolov4-tiny
- yolov5s
- yolact
- MiDaS v2
- MODNet
- Person Reidentification
- DeepSort