datitran/raccoon_dataset
The dataset is used to train my own raccoon detector and I blogged about it on Medium
| repo name | datitran/raccoon_dataset |
| repo link | https://github.com/datitran/raccoon_dataset |
| homepage | https://medium.com/towards-data-science/how-to-train-your-own-object-detector-with-tensorflows-object-detector-api-bec72ecfe1d9 |
| language | Jupyter Notebook |
| size (curr.) | 49159 kB |
| stars (curr.) | 1009 |
| created | 2017-07-27 |
| license | MIT License |
Raccoon Detector Dataset
This is a dataset that I collected to train my own Raccoon detector with TensorFlow’s Object Detection API. Images are from Google and Pixabay. In total, there are 200 images (160 are used for training and 40 for validation).
Getting Started
Folder Structure:
+ annotations: contains the xml files in PASCAL VOC format
+ data: contains the input file for the TF object detection API and the label files (csv)
+ images: contains the image data in jpg format
+ training: contains the pipeline configuration file, frozen model and labelmap
- a few handy scripts: generate_tfrecord.py is used to generate the input files
for the TF API and xml_to_csv.py is used to convert the xml files into one csv
- a few jupyter notebooks: draw boxes is used to plot some of the data and
split labels is used to split the full labels into train and test labels
