• Hardware : NVIDIA GeForce RTX 2060
• Network Type: Yolo_v4
• TLT Version: 3.22.05
• Training spec file : tao_unet_05_08_24_train_v2.txt (1.7 KB)
I am trying to train a unet model to segment vehicles, there are two classes ‘background’ and ‘foreground’. I started my first model’s training with the default config given in the tao toolkit documentation for unet with some minor changes tao_unet_05_08_24_train.txt (1.7 KB) and the model was only outputting one class as mask and moreover the loss was stuck after the first epoch. So I reffered Problems encountered in training unet and inference unet - #27 by Morganh and made the modifications as per the moderator’s post and I could see better loss propagation output.log (5.0 MB) but the model when testing still gave only one output class. The tao unet evaluate output looked like this "{'foreground': {'precision': 1.0, 'Recall': 1.0, 'F1 Score': 1.0, 'iou': 1.0}, 'background': {'precision': nan, 'Recall': nan, 'F1 Score': nan, 'iou': nan}}".
Some additional context, the training dataset used is coco_2017_train with no pre processing applied to the images and using segmentation for the ‘bus’, ‘truck’ and ‘car’ classes.
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