Train on custom dataset for panoptic segmentation

I‘ve been trying to used it for a nuclei panoptic segmentation task. Dataset is prepared like ADE20K panoptic do. However, in evalutaion, it doesn’t proposed any instance after a period time of training.

File “/home/—/anaconda3/envs/mask2former/lib/python3.8/site-packages/panopticapi/evaluation.py”, line 224, in pq_compute results[name], per_class_results = pq_stat.pq_average(categories, isthing=isthing) File “/home/—/anaconda3/envs/mask2former/lib/python3.8/site-packages/panopticapi/evaluation.py”, line 73, in pq_average return {‘pq’: pq / n, ‘sq’: sq / n, ‘rq’: rq / n, ‘n’: n}, per_class_results ZeroDivisionError: division by zero

There several possible reasons accounting for it I assume:

• Dataset not well prepared: Are semantic and instance images label folder a must for panoptic? The labeled data I owned is not Detectorn2 format. But I referred to prepare_ade20k_sem_seg, prepare_ade20k_ins_seg and prepare_ade20k_pan_seg. Converted the labeled data to panoptic images (in a folder) and label json file. Commented the line "sem_seg_file_name": sem_label_file, in dataset_dict.
• Configure file not well modified: Another reason maybe model not convergen. Is there any configuration like Mask-RCNN’s anchor size or ratio in panopitc segmentation? Because nuclei in whole slide images (crop multiple patches in size 256*256, with one nuclei around (8~16)*(8~16) pixels) is rather small compared to common things in a natural image captioned by camera.

[bowenc0221]

The overlap is calculated with mask instead of box.

[unanan]

@JasonRichard Hi! Did you resolve this problem?