upwellingdetection_SST_outputs
This model is a fine-tuned version of nvidia/mit-b0 on the greenkwd/upwellingdetection_SST dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.1872
- eval_mean_iou: 0.5366
- eval_mean_accuracy: 0.8745
- eval_overall_accuracy: 0.9028
- eval_accuracy_land: nan
- eval_accuracy_upwelling: 0.9525
- eval_accuracy_not_upwelling: 0.7965
- eval_iou_land: 0.0
- eval_iou_upwelling: 0.8852
- eval_iou_not_upwelling: 0.7245
- eval_runtime: 22.2484
- eval_samples_per_second: 11.866
- eval_steps_per_second: 0.764
- epoch: 35.6
- step: 1780
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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100
Framework versions
- Transformers 4.41.2
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.19.1
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Base model
nvidia/mit-b0