I have my model trained and ready , Can anyone help me on how to perform evaluation of object detection model on fastai?

Can anyone help me on how to perform evaluation of object detection model on fastai?
I have already trained the model , and I have test data also ready , its an object detection model retinanet trained on midog 2021 challenge dataset.
I need various evaluation metrics for my model based on iou thresholding on bounding boxes predictions of model over ground truth bounding boxes(classic MSCOCO format object detection to classification evals)
This is my sample code:

train, valid ,test = ObjectItemListSlide(train_images), ObjectItemListSlide(valid_images), ObjectItemListSlide(test_images)
item_list = ItemLists(".", train, valid)
lls = item_list.label_from_func(lambda x: x.y, label_cls=SlideObjectCategoryList)
lls = lls.transform(tfms, tfm_y=True, size=patch_size)
data = lls.databunch(bs=batch_size, collate_fn=bb_pad_collate,num_workers=0).normalize()

learn = Learner(data, model, loss_func=crit, 
                callback_fns=[ShowGraph,CSVLogger,partial(GradientClipping, clip=2.0)])  
learn.split([model.encoder[6], model.c5top5])
item_list_t = ItemLists(".", train, test)
lls_t = item_list.label_from_func(lambda x: x.y, label_cls=SlideObjectCategoryList)
lls_t = lls_t.transform(tfms, tfm_y=True, size=patch_size)
data_t= lls_t.databunch(bs=batch_size, collate_fn=bb_pad_collate,num_workers=0).normalize()
detect_thresh = 0.5 
nms_thresh = 0.2 

show_results_side_by_side(learn, anchors, detect_thresh=detect_thresh, nms_thresh=nms_thresh, image_count=image_count)

I can see the results after the last function but its just prediction of box over with score over random patches of my data ,
I need the precision,recall, accuracy , confusion matrix ,roc auc curve ,etc, on all the test images . The metric for classification is iou =0.5 over the bounding box if the bounding boxe predicted by machine has iou >0.5 it is to be considered as true positive for positive ground truth, and vice versa.
Can you guys please share a notebook on how can I perform such an evaluation of model? Any kind of notebooks, resources, code snippets are welcome.
Thanking all of you for the great support on this wonderful platform.
You can mail me, or message me on this forum, all suggestions are really welcome.
Warm regards,