Retail Product Checkout Dataset Tools
Project : RPC Dataset Project Page
Dataset : RPC Dataset
1. Install
pip install rpctool
or
pip install git+https://github.com/DIYer22/retail_product_checkout_tools
2. Usage
python -m rpctool {result json} {ground truth json}
3. Help
$ python -m rpctool -h
Evaluate resFile with annFile and return evaluation result in markdown format
positional arguments:
FILE path to result json(support bbox format)
FILE path to ground truth json
optional arguments:
-h, --help show this help message and exit
--mmap Evaluate mAP50 and mmAP
4. Example
Input:
python -m rpctool bbox_results.json ~/retail_product_checkout/instances_test2019.json
Return:
## result on RPC-Dataset
| diff | method | cAcc | mCIoU | ACD | mCCD |
| ---: | ---: | ---: | ---: | ---: | ---: |
| easy | default | 63.19% | 90.64% | 0.72 | 0.11 |
| medium | default | 43.02% | 90.64% | 1.24 | 0.11 |
| hard | default | 31.01% | 90.41% | 1.77 | 0.1 |
| averaged | default | 45.6% | 90.58% | 1.25 | 0.1 |
5. Result Format
Result format (i.e bbox_results.json) should has the same data structure as the data struecture of COCO Object Detection Result Format :
[{
"image_id" : int,
"category_id" : int,
"bbox" : [x,y,width,height],
"score" : float,
}]
