Annotation is very boring work, so I think that can we use our custom trained model (ONNX model) to annotate our new Data.
So I created a python module that can Auto-Annotate your Dataset using your ONNX mode.
I also added new Auto-Annotator using YOLOv7 model (.pb)
Link to GitHub Repository:-
We can convert other types of model Tensorflow(.pb) or PyTorch(.pth) or other Models into ONNX. That’s why I choose ONNX model to build my Auto-Annotator module.
Convert To ONNX Model
1. From Tensorflow (.pb)
I hoping that, you are using Object Detection API for this.
https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/index.html
When the time of exporting,
Use:-
--input_type image_tensor
NOT
--input_type float_image_tensor
Example:-
python .\exporter_main_v2.py --input_type image_tensor --pipeline_config_path .\models\my_ssd_resnet50_v1_fpn\pipeline.config --trained_checkpoint_dir .\models\my_ssd_resnet50_v1_fpn\ --output_directory .\exported-models\my_model
pb to ONNX
Follow tensorflow-onnx:-
pip install -U tf2onnx
python -m tf2onnx.convert --saved-model tensorflow-model-path --opset 16 --output model.onnx
default of 13 for the ONNX opset. If you need a newer opset, or want to limit your model to use an older opset then you can provide the --opset argument to the command. If you are unsure about which opset to use, refer to the ONNX operator documentation.
2. From Pytorch (.pth) [pth to ONNX]
Checkout Links Below:
https://github.com/danielgatis/rembg/issues/193
https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html
Auto-Annotate — ONNX Model
Clone Git Repository:-
git clone https://github.com/naseemap47/autoAnnoter
cd autoAnnoter
Install Required Libraries:
pip3 install -r requirements.txt
Variables:
-x , --xml _____ for XML Annotations
-t, --txt _____ to annotate in (.txt) format
-i, --dataset _ path to Dataset
-c, --classes _ path to classes.txt file (names of object detection classes)
Example for classes.txt :
car
person
book
apple
mobile
bottle
....
-m, --model__ path to ONNX model
-s, --size__ Size of image used to train the your object detection model
-conf, --confidence __ Model detection Confidence (0<confidence<1)
For XML Annotations:
python3 autoAnnot.py -x -i <PATH_TO_DATA> -c <PATH_TO_classes.txt> -m <ONNX_MODEL_PATH> -s <SIZE_OF_IMAGE_WHEN_TRAIN_YOUR_MODEL> -conf <MODEL_OBJCET_DETECTION_CONFIDENCE>
For TXT Format Annotations:
python3 autoAnnot.py -t -i <PATH_TO_DATA> -c <PATH_TO_classes.txt> -m <ONNX_MODEL_PATH> -s <SIZE_OF_IMAGE_WHEN_TRAIN_YOUR_MODEL> -conf <MODEL_OBJCET_DETECTION_CONFIDENCE>
It will do all operation and the end of process you will get your Auto-Annotated data inside your data.
Auto-Annotated data will be with your corresponding Image data.
In this way you can easy check, the Annotation is Correct or NOT.
Auto-Annotate YOLOv7 Model
Clone Git Repository:-
git clone https://github.com/naseemap47/autoAnnoter
Variables:
-i, --dataset ____ path to Dataset
-m, --model ____ path to YOLOv7 model (.pt)
-c, --confidence ___ Model detection Confidence (0<confidence<1)
python3 autoAnotYolov7.py -i <PATH_TO_DATA> -m <YOLOv7_MODEL_PATH> -c <MODEL_OBJCET_DETECTION_CONFIDENCE>
It will do all operation and the end of process you will get your Auto-Annotated data inside your data.
Auto-Annotated data will be with your corresponding Image data.
In this way you can easy check, the Annotation is Correct or NOT.
Auto-Annotated data accuracy is Completely depends on your Custom Model.
So, its better to check the Annotation is Correct or NOT.
Let me know your feedback about my Auto-Annotator
Thank you…
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