I'm evaluating supergradients and YoloNAS and I'm trying to use the coco recipe to train a model from scratch. I am getting< .20onPrecision@0.50:0.95`. For an out of the box recipe I assumed the numbers would be more like a expect, getting a closer match to recall. Recall raises as expected, as well does MAP.
On other builds (fine tuning with my own data) I see similar issues with the precision metrics. when I run an external evaluation on a test dataset my precision number is much higher so, I'm thinking this may be an issue with metrics calculation.
I'm using the following dataset:
To use this Dataset you need to:
- Download coco dataset:
annotations: http://images.cocodataset.org/annotations/annotations_trainval2017.zip
train2017: http://images.cocodataset.org/zips/train2017.zip
val2017: http://images.cocodataset.org/zips/val2017.zip
- Unzip and organize it as below:
coco
βββ annotations
β ββ instances_train2017.json
β ββ instances_val2017.json
β ββ ...
βββ images
βββ train2017
β ββ 000000000001.jpg
β ββ ...
βββ val2017
π‘ Your Question
I'm evaluating
supergradients
andYoloNAS and I'm trying to use the coco recipe to train a model from scratch. I am getting
< .20on
Precision@0.50:0.95`. For an out of the box recipe I assumed the numbers would be more like a expect, getting a closer match to recall. Recall raises as expected, as well does MAP.On other builds (fine tuning with my own data) I see similar issues with the precision metrics. when I run an external evaluation on a test dataset my precision number is much higher so, I'm thinking this may be an issue with metrics calculation.
I'm using the following dataset:
Here is the command I am using:
Versions
super-gradients==3.7.1