jms0923/tada

🚛 TrAsh DAtaset based on Korea Ministry of Environment guidelines

TADA is large-scale high-resolution trash datasets for solving trash-detection problems. It was intended for general household goods. And dataset was classified based on guidelines for separating and discharging recyclables from the Ministry of Environment. There were 10 categories, and 33,434 objects were annotated for 4977 images with 1280 x 720 resolution.

For convenience, annotations are provided in COCO data format. If you want to know about more detail Data format reference COCO data format

Models

AP performance by Category of each model

Model	mAP	Config	Checkpoint
SSD	0.310	Download	Download
YOLO v3	0.445	Download	Download
Cascade RCNN	0.432	Download	Download

Getting started

1. Install mmdetection

   This implementation is based on mmdetection. Please refer to INSTALL.md for installation and dataset preparation.

2. Copy the codes to mmdetection directory

   cp -r configs/ ${MMDETECTION_PATH}/

3. Prepare data

   Download from

   The directories should be arranged like this:

   >   mmdetection
   >     ├── mmdet
   >     ├── tools
   >     ├── configs
   >     ├── data
   >     │   ├── coco
   >     │   │   ├── annotations
   >     │   │   ├── train2017
   >     │   │   ├── val2017
   >     │   │   ├── test2017
   

4. Train with GPUs

   CONFIG_FILE=configs/ ${CONFIG_FILE}
   ./ tools/dist_train.py ${CONFIG_FILE} ${NUM_GPUs}

5. Calculate parameters and flops

    python tools/get_flops.py ${CONFIG_FILE} --shape $SIZE $SIZE

6. Test

   python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} --out  ${OUTPUT_FILE} --eval bbox

More usages can reference mmdetection documentation.

TODO

• Release pre-train models.
• Update our arxiv paper.

Citation

If you find our research useful, please cite the paper:

@inproceedings{sun2018fishnet,
  title={Large-Scale High-Resolution Dataset for Trash Detection},
  author={Min-Seok Jo, Seong-Soo Han, Chang-Sung Jeong},
  booktitle={},
  pages={--},
  year={2021}
}

Contact

You can contact MinSeok Jo by sending email to [email protected]

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