mAP for test set

My project is object detection and I am using google colab to training my model with YOLOv5. But, the notebook that roboflow gives to me to use as an example, does not have something that I want.
I would like to calculate the mAP for my test set (test images in the same dataset version used to train the model).

When I train the dataset with the roboflow model, I got this metrics:

Captura de tela 2023-07-13 054349

But, using the model YOLOv5 in colab, and exporting it and its weights to the roboflow, I got only the part of Training Graphs.
The part of AP for each class in Validation set, I got when the training finished. And when I ran inference with trained weights on Test set, I only got images with labels as result, and not metrics.
The command used:

%cd /content/yolov5/

!python --weights runs/train/yolov5s_results/weights/ --img 256 --conf 0.5 --source TDM-15/test/images

I think this can be easy, but I have no idea what command I can use to get the mAP, or compare the ground truth with the predictions.

Edit1: I was able to calculate the mAP for all class, changing the code to this:

!python --data /content/yolov5/test.yaml --weights runs/train/yolov5s_results/weights/ --img 256 --conf 0.5 --iou-thres 0.5 --task "test"

But how can I calculate this for each class?

Hi @jvprimakipr

Can you share which notebook you’re using? Is it our YOLOv5 notebook?

If so, according to this GitHub issue, you can get per class stats using the --verbose on