apkonsta

/

table-transformer-detection-ifrs

like 5

Hi!

Thank you for reaching out. I fine-tuned the model using the training scripts provided in the Microsoft Table Transformer repository https://github.com/microsoft/table-transformer. Specifically, you can find the details in the Model Training section of the README.

@apkonsta thank u for ur response , after the fine-tuning the results are like this :
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.169
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.359
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.142
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.169
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.227
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.547
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.742
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.742

Any thoughts about this