Natan Bagrov

So it appears like the root cause is indeed integers. The suggestion is ok, but then the problem goes to the resolution (that the pipeline assumes it is an integer)....

One workaround (maybe even the proper way) is to use `getattr` explicitly and wrap the parameter (attribute name) with `str(...)`?

I agree regarding the ambiguity. Maybe square brackets should be the go-to if we're using integers.

Hello, @veb-101 , I refer you to [this document](https://github.com/Deci-AI/super-gradients/blob/master/documentation/source/qat_ptq_yolo_nas.md). Let me know if this helps!

To load the model for inference you should compile it to TensorRT. The INT8 QAT model is convertible to an ONNX with Q/DQ layers which are then compiled into actual...

> How to find the class names of the pre-trained model. Here the label is a numeric value. Addressed in #930

Hi @sivaji123256, regarding your original question of filtering classes. This is not available out-of-the-box, but you can achieve this by modifying the model a bit. As you can see in...

> @NatanBagrov. Thank you for providing the necessary references. The method you have mentioned to filter classes seems to be too complex for me to comprehend and implement as a...

Hello, I'm attaching here a general template. Fill-in your custom code: ```python setup_device(multi_gpu=MultiGPUMode.OFF, num_gpus=1) trainer = Trainer(experiment_name="my_experiment") net = models.get(Models.YoloNAS_S, pretrained_weights="coco") train_dataloader, val_dataloader = ... # YOUR DATALOADERS training_hyperparams =...

Hello @MehmetOKUYAR, This is very easy to achieve by modifying the dataset parameters. You can see a [reference](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/recipes/dataset_params/coco_detection_dataset_params.yaml#L5) in the default COCO dataset params. Please note that you should also...