Closed rbumbi closed 1 year ago
That's interesting. I saw you also use initialize_box_type: mask2box
to generate box from mask in query selection. It is expected that you box AP is higher than mask AP.
Is there anything special in your dataset?
Hello, thank you for your response. In my dataset aren't any special things, only images from stable (IP cams are placed on the ceiling), where I detect standing and lying cows. Masks in my dataset were created manually, and bboxes were automatically created from the masks; respectively, bboxes are precise. I expected higher box AP than mask AP, but my results were a little bit surprised me. Isn't anything bad in my config?
Not yet. You can wait for the result of more epochs. You can also check if your box output needs further post-processing.
Thank you for your advice, I will try it.
I'm closing this issue, feel free to reopen it if needed
Hello,
first, I would to thank you for your excellent work; your network is impressive. Could I ask you for your advice about the proper train parameters for detection? When I train on a custom dataset with two classes, the evaluation results of segmentation are fine (about AP 80 after the second epoch). Still, detection results, bbox, are worse (about AP 58 after the second epoch).
Thank you very much
Best Regards
Roman Bumbalek
Here is config:
dataloader: evaluator: {target: detectron2.evaluation.COCOEvaluator, dataset_name: '${..test.dataset.names}', output_dir: ./output/dab_detr_r50_50ep} test: target: detectron2.data.build_detection_test_loader dataset: {target: detectron2.data.get_detection_dataset_dicts, filter_empty: false, names: cow_val} mapper: target: detrex.data.COCOInstanceNewBaselineDatasetMapper augmentation: