I'm getting Better Values for mAP with Resnet50 than You have Mentioned, Why is that ?

[KulunuGeeganage]

Dear @priya-dwivedi

Backbone - Resnet50
I am trying to use retinanet with Stanford Drone Data set. When I try to evaluate model resnet50_csv_12_inference.h5 you have shared here https://drive.google.com/drive/u/0/folders/1QpE_iRDq1hUzYNBXSBSnmfe6SgTYE3J4

I'm getting following mAP values.

Running network: 100% (2587 of 2587)
Parsing annotations: 100% (2587 of 2587)
11749 instances of class Biker with average precision: 0.7769
889 instances of class Car with average precision: 0.9921
101 instances of class Bus with average precision: 0.9109
266 instances of class Cart with average precision: 0.5335
485 instances of class Skater with average precision: 0.3929
22188 instances of class Pedestrian with average precision: 0.8732
Inference time for 2587 images: 1.9893
mAP using the weighted average of precisions among classes: 0.8355
mAP: 0.7466

My command -

python evaluate.py --save-path \path to save\ train_annotations.csv labels.csv \path to model..\resnet50_csv_12_inference.h5

But in your document http://cs230.stanford.edu/projects_winter_2019/reports/15767730.pdf I can see you have got lower values than me. Why is that ?

And also I have another question. The file resnet50_csv_12_inference.h5 you have given did you generate this model using small anchors values (16 32 128 256) ?

I must be thankful to you if you will kindly reply me soon.

Regards,
Kulunu.