File size: 1,562 Bytes
f8d45fd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
---
license: other
base_model: nvidia/mit-b5
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b5-miic-tl
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b5-miic-tl
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the yijisuk/ic-chip-sample dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2028
- eval_mean_iou: 0.3885
- eval_mean_accuracy: 0.7770
- eval_overall_accuracy: 0.7770
- eval_accuracy_unlabeled: nan
- eval_accuracy_circuit: 0.7770
- eval_iou_unlabeled: 0.0
- eval_iou_circuit: 0.7770
- eval_dice_coefficient: 0.7854
- eval_runtime: 1.8601
- eval_samples_per_second: 5.376
- eval_steps_per_second: 2.688
- epoch: 48.75
- step: 3900
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Framework versions
- Transformers 4.36.2
- Pytorch 1.11.0+cu115
- Datasets 2.15.0
- Tokenizers 0.15.0
|