Closed Amy-Liao closed 2 years ago
To use the pre-trained model, we should use the real train.jsonl and eval.jsonl, not the fake data generated by fake_testing_set.py .
You can run cp ../data/annotations/downloads/*.jsonl ../data/annotations/
and then python3 decide_cates.py
. As a result, the top-10 categories in cates.json are
{
"cate_id": 0,
"text": "中",
"trainval": 13924
},
{
"cate_id": 1,
"text": "国",
"trainval": 9410
},
{
"cate_id": 2,
"text": "大",
"trainval": 8843
},
{
"cate_id": 3,
"text": "电",
"trainval": 6908
},
{
"cate_id": 4,
"text": "店",
"trainval": 6622
},
{
"cate_id": 5,
"text": "路",
"trainval": 6555
},
{
"cate_id": 6,
"text": "车",
"trainval": 6541
},
{
"cate_id": 7,
"text": "家",
"trainval": 6200
},
{
"cate_id": 8,
"text": "公",
"trainval": 5946
},
{
"cate_id": 9,
"text": "行",
"trainval": 5672
},
Thank you so much! I've successfully reproduced the performance :)
Hi, sorry for bothering. I used validation set as test dataset by
cd ../prepare && python3 fake_testing_set.py
and tried to reproduce the paper evaluation results by running!cd ../judge && python3 classification_perf.py inception_v4
However, I got thisPerformance for 店、路、车 are both 0.0% And in the "cls_precision_by_model_size" file, I got this The accuracy are all about 0.2
I also have read this issue https://github.com/yuantailing/ctw-baseline/issues/29#issuecomment-445824509 , but I did run your
classsification/decide_cates.py
without modification to generate cates.json. What reasons might it be for the performance I got? Thanks for your help :)