segformer-finetuned-ihc

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

• eval_loss: 0.0326
• eval_mean_iou: 0.0
• eval_mean_accuracy: nan
• eval_overall_accuracy: nan
• eval_accuracy_background: nan
• eval_accuracy_tissue: nan
• eval_iou_background: 0.0
• eval_iou_tissue: 0.0
• eval_runtime: 19.1281
• eval_samples_per_second: 0.784
• eval_steps_per_second: 0.105
• epoch: 9.15
• step: 183

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: 1337
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: polynomial
• training_steps: 10000

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.2
• Tokenizers 0.13.3