segformer-b1-kelp-rgb-agg-imgaug-jan-22

This model is a fine-tuned version of nvidia/mit-b1 on the samitizerxu/kelp_data dataset. It achieves the following results on the evaluation set:

• eval_accuracy_kelp: nan
• eval_iou_kelp: 0.0
• eval_loss: 0.3223
• eval_mean_iou: 0.0205
• eval_mean_accuracy: 0.0410
• eval_overall_accuracy: 0.0410
• eval_runtime: 62.0057
• eval_samples_per_second: 27.272
• eval_steps_per_second: 3.419
• epoch: 1.16
• step: 570

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.2
• num_epochs: 40

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.2+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0