- Clone this repository.
- In the repository, execute `pip install -r requirements.txt`.
Note that due to inconsistencies with how `tensorflow` should be installed,
this package does not define a dependency on `tensorflow` as it will try to install that (which at least on Arch Linux results in an incorrect installation).
Please make sure `tensorflow` is installed as per your systems requirements.
Also, make sure Keras 2.1.3 or higher and OpenCV 3.x is installed.
- For Keras model - Download the pretrained weights and save it in /snapshots/keras.
- For tensorflow model get the desired model from here and extract it in /sanpshots/tensorfow
- You can even save custom pre trained model in the respective directory.
- Select the COCO object classes for which you need suggestions from the drop-down menu and add them. Or simply click on Add all classes.
- Select the desired model and click on Add model.
- Click on detect button.
- When annotating manually, select the object class from the List and while keep it selected, select the BBox.
- The final annotations can be found in the file annotations.csv in ./annotations/ . Also a xml file will saved.
For MSCOCO dataset
For any other dataset-
First change the labels in config.py (for keras model) or in tf_config.py( for tensorflow model). Then run:
- Windows 10
- Linux 16.04
- macOS High Sierra
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