https://github.com/sayakpaul/vehicle-number-plate-detection
This project demonstrates the use of TensorFlow Object Detection API to automatically number plates (Indian) from vehicles
https://github.com/sayakpaul/vehicle-number-plate-detection
deep-learning google-cloud-platform object-detection ssdmobilenet tensorflow-object-detection-api
Last synced: 6 months ago
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This project demonstrates the use of TensorFlow Object Detection API to automatically number plates (Indian) from vehicles
- Host: GitHub
- URL: https://github.com/sayakpaul/vehicle-number-plate-detection
- Owner: sayakpaul
- License: mit
- Created: 2019-08-24T11:27:44.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2019-09-12T10:48:39.000Z (about 6 years ago)
- Last Synced: 2025-04-30T18:06:26.321Z (6 months ago)
- Topics: deep-learning, google-cloud-platform, object-detection, ssdmobilenet, tensorflow-object-detection-api
- Language: Jupyter Notebook
- Homepage:
- Size: 49.7 MB
- Stars: 40
- Watchers: 2
- Forks: 13
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Vehicle-Number-Plate-Detection
This project demonstrates the use of [TensorFlow Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection) to automatically number plates (Indian) from vehicles.
Dataset used: https://www.kaggle.com/dataturks/vehicle-number-plate-detection
## File description:
- Data-Images.zip: Contains the images of the cars, number plates and annotations in `.txt` files (YOLO format)
- Data_prep_and_visualization.ipynb: A notebook demonstrating the process of preparing the dataset (`.csv` files) for creating TFRecords (otherwise TensorFlow Object Detection API won't work)
- Indian_Number_plates.json: Configuration file which contains image download paths and annotations
- exported_graph: Contains the inference graph in `.pb` and `.tflite` formats which can be used to run inference on both CPU platforms and on-device platforms
- label_map.pbtxt: Contains the encodings of the dataset classes which,in this case, is 1: **license_plate**
- ssd_mobilenet_v1_pets.config: Training and evaluation pipeline configuration file as needed by TensorFlow Object Detection API
- test.record & train.record: `TFRecords` files of testing and training sets respectively
- test_labels.csv & train_labels.csv: `.csv` files as required by the `generate_tfrecord.py` script
## Demo inference (as collected from TensorBoard):
To kick-start the model training process and to export the trained model's inference graph (using the `export_tflite_ssd_graph.py` script), I followed:
- [TensorFlow Object Detection API's official documentation](https://github.com/tensorflow/models/tree/master/research/object_detection)
- [Training and serving a realtime mobile object detector in 30 minutes with Cloud TPUs](https://medium.com/tensorflow/training-and-serving-a-realtime-mobile-object-detector-in-30-minutes-with-cloud-tpus-b78971cf1193)
I used **SSD_MobileNet_V1** architecture which was pretrained on the COCO dataset.
To convert the frozen inference graph, I ran the following command:
```
tflite_convert \
--output_file=detect.tflite \
--graph_def_file=frozen_inference_graph.pb \
--input_shapes=1,300,300,3 \
--input_arrays=normalized_input_image_tensor \
--output_arrays='TFLite_Detection_PostProcess','TFLite_Detection_PostProcess:1','TFLite_Detection_PostProcess:2','TFLite_Detection_PostProcess:3' \
--inference_type=QUANTIZED_UINT8 \
--mean_values=128 \
--std_dev_values=128 \
--change_concat_input_ranges=false \
--allow_custom_ops
```
**Note**: To be able convert an inference graph to its `.tflite` variant you need to enable _quantization aware training_ and you can specify that in the `.config` file itself.