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@@ -71,7 +71,7 @@ In order to use Tensorflow API, you need to feed data in Tensorflow record forma
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One additional thing you need to do it to edit the dictionary in the script at line 57. You need to the give name of the classes as key and the value of pixel for the colour of mask you have chosen for respective class while masking the classobjects using pisxelAnnotationTool as value.
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After doing above, one last thing is still remaining before we get our Tensorflow record file. You need to create a file for label map, in this repo its *label.pbtxt*, with the dictionary of the label and the id of objects. Check *label.pbtxt* given in the repository to understand the format, its pretty simple (Note: name of the label should be same as class names you had given in the dictionary). Now it time to create record file. From models/research as present working directory run the following command to create Tensorflow record:
Now that we have data in the right format to feed, we could go ahead with training our model. The first thing you need to do is to select the pre-trained model you would like to use. You could check and download a pret-rained model from [Tensorflow detection model zoo Github page](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md). Once downloaded, extract all file to the folder you had created for saving the pre-trained model files. Next you need to copy *models/research/sample/configs/<your_model_name.config>* and paste it in the project repo. You need to configure 5 paths in this file. Just open this file and search for PATH_TO_BE_CONFIGURED and replace it with the required path. I used pre-trained mask RCNN which is trained with inception V2 as feature extracter and I have added modified config file (along with PATH_TO_BE_CONFIGURED as comment above lines which has been modified) for same in this repo. You could also play with other hyperparameters if you want. Now you are all set to train your model, just run th following command with models/research as present working directory
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Now that we have data in the right format to feed, we could go ahead with training our model. The first thing you need to do is to select the pre-trained model you would like to use. You could check and download a pret-rained model from [Tensorflow detection model zoo Github page](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md). Once downloaded, extract all file to the folder you had created for saving the pre-trained model files. Next you need to copy *models/research/object_detection/sample/configs/<your_model_name.config>* and paste it in the project repo. You need to configure 5 paths in this file. Just open this file and search for PATH_TO_BE_CONFIGURED and replace it with the required path. I used pre-trained mask RCNN which is trained with inception V2 as feature extracter and I have added modified config file (along with PATH_TO_BE_CONFIGURED as comment above lines which has been modified) for same in this repo. You could also play with other hyperparameters if you want. Now you are all set to train your model, just run th following command with models/research as present working directory
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