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
- Downloads last month
- 20
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for Isaacks/segformer-finetuned-ihc
Base model
nvidia/mit-b0