custom-object-test6

This model is a fine-tuned version of nvidia/mit-b0 on the sungile/custom-object-masking5 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3457
  • Mean Iou: 0.3368
  • Mean Accuracy: 0.6736
  • Overall Accuracy: 0.6736
  • Accuracy Unknown: nan
  • Accuracy Background: 0.6736
  • Accuracy Object: nan
  • Iou Unknown: 0.0
  • Iou Background: 0.6736
  • Iou Object: nan

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 6e-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Unknown Accuracy Background Accuracy Object Iou Unknown Iou Background Iou Object
0.7667 0.25 20 1.0272 0.3026 0.9079 0.9079 nan 0.9079 nan 0.0 0.9079 0.0
0.8127 0.5 40 0.8213 0.2803 0.8409 0.8409 nan 0.8409 nan 0.0 0.8409 0.0
0.5588 0.75 60 0.7310 0.4304 0.8608 0.8608 nan 0.8608 nan 0.0 0.8608 nan
0.6156 1.0 80 0.5317 0.3130 0.6261 0.6261 nan 0.6261 nan 0.0 0.6261 nan
0.5077 1.25 100 0.4617 0.3482 0.6964 0.6964 nan 0.6964 nan 0.0 0.6964 nan
0.5612 1.5 120 0.4336 0.3341 0.6683 0.6683 nan 0.6683 nan 0.0 0.6683 nan
0.4468 1.75 140 0.3946 0.3442 0.6883 0.6883 nan 0.6883 nan 0.0 0.6883 nan
0.292 2.0 160 0.3554 0.3041 0.6081 0.6081 nan 0.6081 nan 0.0 0.6081 nan
0.3769 2.25 180 0.3798 0.3402 0.6805 0.6805 nan 0.6805 nan 0.0 0.6805 nan
0.3386 2.5 200 0.3493 0.3147 0.6293 0.6293 nan 0.6293 nan 0.0 0.6293 nan
0.2689 2.75 220 0.3736 0.3492 0.6984 0.6984 nan 0.6984 nan 0.0 0.6984 nan
0.2539 3.0 240 0.3457 0.3368 0.6736 0.6736 nan 0.6736 nan 0.0 0.6736 nan

Framework versions

  • Transformers 4.47.1
  • Pytorch 2.1.0+cu118
  • Datasets 3.2.0
  • Tokenizers 0.21.0
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