https://github.com/zabir-nabil/tf-model-server4-yolov3
Simple code base and instructions to convert yolov3 darknet weights to tensorflow .pb to serve @ tensorflow model server
https://github.com/zabir-nabil/tf-model-server4-yolov3
daeknet-server darknet darknet-server darknet-yolov3-flask docker flask tensorflow tensorflow-darknet tensorflow-model-server yolov3
Last synced: 3 months ago
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Simple code base and instructions to convert yolov3 darknet weights to tensorflow .pb to serve @ tensorflow model server
- Host: GitHub
- URL: https://github.com/zabir-nabil/tf-model-server4-yolov3
- Owner: zabir-nabil
- License: mit
- Created: 2020-02-11T16:57:04.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-02-13T07:51:55.000Z (over 5 years ago)
- Last Synced: 2025-07-05T20:47:28.520Z (4 months ago)
- Topics: daeknet-server, darknet, darknet-server, darknet-yolov3-flask, docker, flask, tensorflow, tensorflow-darknet, tensorflow-model-server, yolov3
- Language: C
- Size: 2.94 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# tf-model-server4-yolov3
Simple code base and instructions to convert yolov3 darknet weights to tensorflow .pb to serve @ tensorflow model server
### Convert .weights to .ckpt
Use DW2TF repository
https://github.com/jinyu121/DW2TF
### Deploy to tf-model-server
> Go to export.py
> Change the path and filename for .ckpt files
> Change the input, output nodes for yolo (you can find it using Netron or the graph.log file which will be generated after running export.py [even if it fails, the graph.log will be generated])
> command
`python export.py`
> The model will be found in exported_model folder
> Copy the .pb / .pbtxt and the variables folder to serving path (~serving/versions/)
##### Run gRPC server
`tensorflow_model_server --port=9000 --model_name=yolo --model_base_path=/absolute_path_to/yolo_v3/serving/versions/`
##### Run REST Server
> To serve with GPU
```
nohup tensorflow_model_server \
--rest_api_port=8501 \
--model_name=yolo \
--model_base_path=/absolute_path_to/yolo_v3/serving/versions/
-t tensorflow/serving:gpu >server.log 2>&1
```
> To serve at CPU
```
nohup tensorflow_model_server \
--rest_api_port=8502 \
--model_name=yolo \
--model_base_path=/absolute_path_to/yolo_v3/serving/versions/
-t tensorflow/serving:latest >server.log 2>&1
```
##### Docker/ GPU
> Docker KILL
`docker container ls`
`docker stop ID`
> Clear GPU memory
`nvidia-smi | grep 'python' | awk '{ print $3 }' | xargs -n1 kill -9`
##### Inference / API call
> Look into the * test_api.py * for both gRPC and REST
### Flask Server for native darknet YOLOv3
> A light flask server for darknet yolov3 is in darknet_server folder
> Run darknet_server.py
> Has both numpy image and base64 image support
> It is slightly faster than tensorflow-model-server based on some benchmarks, but it has very limited functionalities
> Not scalable yet