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glpn-nyu-finetuned

This model is a fine-tuned version of vinvino02/glpn-nyu on the diode-subset dataset. It achieves the following results on the evaluation set:

  • Loss: 1.5286
  • Mae: 3.1196
  • Rmse: 3.5796
  • Abs Rel: 5.9353
  • Log Mae: 0.6899
  • Log Rmse: 0.8145
  • Delta1: 0.3012
  • Delta2: 0.3076
  • Delta3: 0.3093

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: 1e-05
  • train_batch_size: 24
  • eval_batch_size: 48
  • seed: 2022
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Mae Rmse Abs Rel Log Mae Log Rmse Delta1 Delta2 Delta3
No log 1.0 1 1.5476 3.2112 3.7133 6.1586 0.6980 0.8267 0.2998 0.3073 0.3091
No log 2.0 2 1.5441 3.1939 3.6889 6.1181 0.6965 0.8245 0.3001 0.3073 0.3091
No log 3.0 3 1.5410 3.1783 3.6668 6.0811 0.6951 0.8225 0.3003 0.3074 0.3092
No log 4.0 4 1.5381 3.1643 3.6465 6.0474 0.6939 0.8207 0.3005 0.3074 0.3092
No log 5.0 5 1.5355 3.1520 3.6285 6.0172 0.6928 0.8190 0.3007 0.3075 0.3092
No log 6.0 6 1.5333 3.1415 3.6128 5.9909 0.6918 0.8176 0.3009 0.3075 0.3092
No log 7.0 7 1.5315 3.1329 3.5999 5.9693 0.6911 0.8164 0.3010 0.3075 0.3093
No log 8.0 8 1.5301 3.1264 3.5901 5.9529 0.6905 0.8155 0.3011 0.3075 0.3093
No log 9.0 9 1.5291 3.1219 3.5832 5.9413 0.6901 0.8149 0.3012 0.3076 0.3093
No log 10.0 10 1.5286 3.1196 3.5796 5.9353 0.6899 0.8145 0.3012 0.3076 0.3093

Framework versions

  • Transformers 4.29.2
  • Pytorch 2.0.1+cu118
  • Tokenizers 0.13.3
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