Open Linaom1214 opened 1 month ago
Hi! Thanks for your interest!
This is because all images in the ColonDB dataset are abnormal. Thus, the ColonDB dataset only supports the anomaly localization task.
Hi! Thanks for your interest!
This is because all images in the ColonDB dataset are abnormal. Thus, the ColonDB dataset only supports the anomaly localization task. @caoyunkang
thanks! In order to migrate this project to other tasks, do I need to construct data that includes both anomalies and normal data? If I only want to achieve the segmentation of anomaly targets (using only one type of segmentation data), is this not feasible?
thanks! In order to migrate this project to other tasks, do I need to construct data that includes both anomalies and normal data? If I only want to achieve the segmentation of anomaly targets (using only one type of segmentation data), is this not feasible?
Hi, it depends on the aimed task. Technically, the auxiliary data utilized for training should comprise both normal and abnormal samples, with both image-level and pixel-level annotations. For the testing data, arbitrary inputs are acceptable. @Linaom1214
@caoyunkang Thank you very much for your answer. I have one more question. I want to perform validation on a single-category task. Can I achieve this by providing the following labels?
{
"img_path": "xx",
"mask_path": "xx",
"cls_name": "object",
"specie_name": "",
"anomaly": 0
},
{
"img_path": "xx",
"mask_path": "xx",
"cls_name": "object",
"specie_name": "",
"anomaly": 1 },
@Linaom1214
I am not very clear about your question. You should offer both img_path
and mask_path
. For the cls_name
, it would be fine to simply use object
.
@Linaom1214 I am not very clear about your question. You should offer both
img_path
andmask_path
. For thecls_name
, it would be fine to simply useobject
.
Thank you for your patient explanation, it has been very helpful to me.
great work! But when I try to reproduce this program, the metrics “I-Auroc、I-F1、I-AP” is always zero. is right?