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TensorFlow Object Detection Starter is a sample project also a guide of how you can train your own data for object detection.

Now we're working on the easiest one, which is called "Image Retraining".

• InstallPython 3.6.x (At this moment the Tensorflow does not support Python 3.7.x)
• InstallTensorFlow &TensorFlow Hub

pip install tensorflowpip install tensorflow_hub

Note: if you have seen this kind of error:could not find a version that satisfies the requirement tensorflow, please Use Python 3.6.x.

• Prepare the images to be trained & verified (tested), please check themhere for example, tag the pictures by naming the folders

There is an official tutorial ofHow to Retrain an Image Classifier for New Categories

python retrain.py --image_dir=samples/retrain/ \--saved_model_dir=result/saved_model/ \--output_graph=result/foo.pb \--output_labels=result/foo.txt \-—bottleneck_dir=result/bottleneck/ \--summaries_dir=result/retrain_logs/ \-—intermediate_output_graphs_dir=result/intermediate_graph/ \--tfhub_module=https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/1 \--how_many_training_steps=4000

After running through the training process, you'll get the trainedmodel (foo.pb),label (foo.txt) andsaved_model files from theresult folder.

Verify the model withlabel_image.py, which comes from the official tensorflow repository, and we only changed the default settings so we don't have to type too many parameters. Check out theoriginal folder here for label_image.py

Execute thelabel_image.py:

python label_image.py

And you should see the output something like:

r2d2 0.99754924bb8 0.0024507595

• There is an official link of Tensorflow TFLite:Introduction to TensorFlow Lite

For the trained data, we can easily usetflite_convert command like this:

tflite_convert --output_file=result/foo.tflite --input_format=TENSORFLOW_GRAPHDEF --output_format=TFLITE --input_array=Placeholder --output_array=final_result --inference_type=FLOAT --input_data_type=FLOAT --graph_def_file=result/foo.pb