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