How to train your own Object Detector with TensorFlow’s Object Detector API
Dat Tran

Hi Dat, Thanks for the article. Very helpful.

I was trying to follow the post in medium to create my own object detection library, for classifying car models.

I tried implementing it for a car type classification — Trying to classify different kinds of coupe of a brand. I started with 2 types. When I try to classify my image, it always shows as type 1, though image is of type 2. Can someone pls help me? It is mostly what I got from the your github, with tweaks for multiple classes.

Here is my github repo url —

I had put only some images and couple of bounding label xmls for each class, for simplicity. Here is brief info abt my files.

custom_object_detection.ipynb — will consolidate all Xmls to a single csv for training and testing Updated the label map in script. Run the script python3 — csv_input=data/train_labels.csv — output_path=data/train.record python3 — csv_input=data/test_labels.csv — output_path=data/test.record

Am using ssd_mobilenet_v1_coco_11_06_2017 & ssd_mobilenet_v1_pets.config. It is present in training directory. Ran only 750 iterations.

python3 — logtostderr — train_dir=training/ — pipeline_config_path=training/ssd_mobilenet_v1_pets.config

Run the below to create the graph.

python3 — input_type image_tensor — pipeline_config_path training/ssd_mobilenet_v1_pets.config — trained_checkpoint_prefix training/model.ckpt-750 — output_directory C63AMG_inference_graph

Execute my_object_detection_tutorial.ipynb

Can you pls help? Thanks for your time.

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