https://github.com/benehiko/tensorflowobjectdetectautolabel
Auto Label training data using a small trained model
https://github.com/benehiko/tensorflowobjectdetectautolabel
pascal-voc tensorflow tensorflow-object-detection-api
Last synced: 2 months ago
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Auto Label training data using a small trained model
- Host: GitHub
- URL: https://github.com/benehiko/tensorflowobjectdetectautolabel
- Owner: Benehiko
- License: apache-2.0
- Created: 2019-02-01T23:26:24.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-02-02T00:24:19.000Z (over 6 years ago)
- Last Synced: 2025-02-13T12:17:16.084Z (4 months ago)
- Topics: pascal-voc, tensorflow, tensorflow-object-detection-api
- Language: Python
- Homepage:
- Size: 17.4 MB
- Stars: 1
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# TensorflowObjectdetectAutoLabel
Auto Label training data using a small trained model trained by using a small hand labelled dataset
### Requirements
- Python (3 or 2)
- [Tensorflow](https://github.com/tensorflow/tensorflow)
- [pascal_voc_writer](https://github.com/AndrewCarterUK/pascal-voc-writer)
- numpy
- Pillow
### How to use it
This repository gives you all of the required dataset and trained model to test the supplied scripts with. The dataset supplied is a small sample set of [Stanford Ai car dataset](http://ai.stanford.edu/~jkrause/cars/car_dataset.html)
To use your own dataset and custom model follow the [Tensorflow Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection) to train the model and hand label the images with [labelImg](https://github.com/tzutalin/labelImg) or any PascalVoc labelling tool. Train for a couple thousand steps to get an adequate detection confidence (80% +). I used 200 hand labelled images to train with.
After you have acquired the frozen graph, add it to a folder and specify it inside the python scripts under MODEL_NAME and PATH_TO_CKPT. Also ensure to have the label map added here and specified under PATH_TO_LABELS.
The Dataset/Raw folder is a dump folder for images scrapped or gathered which you would like to be sorted using your trained model.
Run object_detection_runner.py to sort your classes into folders under the Dataset/ path.
python3 object_detection_runner.py
Then run auto_label.py to label the sorted classes (specify the classes in CLASS_NAME list). Change the other constants as required.
python3 auto_label.py
After the run is complete, check the labelled images by going to [labelImg](https://github.com/tzutalin/labelImg) and selecting "Open Dir" and selecting "Change Save Dir" to the class folder under "Dataset/". This will load the generated annotations for you to verify on each image.
Please note that auto_label and object_detection_runner is not owned by me and a lot of the coding done was made by Google and [other open source contributors](https://github.com/bourdakos1/Custom-Object-Detection)
I only added some extra features to generate PascalVoc xml files for each class detected.
### Sources
[Stanford Ai Car Dataset](http://ai.stanford.edu/~jkrause/cars/car_dataset.html)
3D Object Representations for Fine-Grained Categorization
Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei
4th IEEE Workshop on 3D Representation and Recognition, at ICCV 2013 (3dRR-13). Sydney, Australia. Dec. 8, 2013.
[Tensorflow](https://github.com/tensorflow/tensorflow)
[Tensorflow Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection)
### License
[Apache License 2.0](https://github.com/Benehiko/TensorflowObjectdetectAutoLabel/blob/master/LICENSE)