metadata
library_name: transformers
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-breastcancer-oct-1
results: []
segformer-b0-finetuned-breastcancer-oct-1
This model is a fine-tuned version of nvidia/mit-b0 on the as-cle-bert/breastcancer-semantic-segmentation dataset. It achieves the following results on the evaluation set:
- Loss: 0.2413
- Mean Iou: 0.4091
- Mean Accuracy: 0.5395
- Overall Accuracy: 0.9467
- Accuracy Ignore: 0.1246
- Accuracy Benign Breast Cancer: 0.5579
- Accuracy Malignant Breast Cancer: 0.4873
- Accuracy Background: 0.9882
- Iou Ignore: 0.1150
- Iou Benign Breast Cancer: 0.1215
- Iou Malignant Breast Cancer: 0.4523
- Iou Background: 0.9478
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: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Ignore | Accuracy Benign Breast Cancer | Accuracy Malignant Breast Cancer | Accuracy Background | Iou Ignore | Iou Benign Breast Cancer | Iou Malignant Breast Cancer | Iou Background |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.074 | 0.625 | 10 | 0.2831 | 0.3265 | 0.5114 | 0.9244 | 0.0 | 0.7065 | 0.3650 | 0.9742 | 0.0 | 0.0562 | 0.3140 | 0.9359 |
0.0747 | 1.25 | 20 | 0.2687 | 0.3706 | 0.4979 | 0.9394 | 0.0 | 0.5673 | 0.4396 | 0.9846 | 0.0 | 0.1507 | 0.3934 | 0.9382 |
0.1038 | 1.875 | 30 | 0.2365 | 0.3962 | 0.5469 | 0.9464 | 0.0206 | 0.6556 | 0.5268 | 0.9845 | 0.0205 | 0.1487 | 0.4688 | 0.9469 |
0.0884 | 2.5 | 40 | 0.2424 | 0.3985 | 0.5438 | 0.9450 | 0.0784 | 0.6292 | 0.4807 | 0.9869 | 0.0752 | 0.1318 | 0.4410 | 0.9461 |
0.0591 | 3.125 | 50 | 0.2473 | 0.3919 | 0.5478 | 0.9384 | 0.1061 | 0.6262 | 0.4793 | 0.9797 | 0.1006 | 0.1115 | 0.4161 | 0.9393 |
0.0566 | 3.75 | 60 | 0.2776 | 0.3640 | 0.4351 | 0.9388 | 0.1037 | 0.2770 | 0.3692 | 0.9904 | 0.0984 | 0.0697 | 0.3485 | 0.9393 |
0.063 | 4.375 | 70 | 0.2262 | 0.4058 | 0.5094 | 0.9472 | 0.1072 | 0.4018 | 0.5440 | 0.9844 | 0.1015 | 0.0892 | 0.4848 | 0.9478 |
0.0358 | 5.0 | 80 | 0.2398 | 0.4065 | 0.5087 | 0.9464 | 0.1084 | 0.4191 | 0.5220 | 0.9854 | 0.1026 | 0.1078 | 0.4699 | 0.9457 |
0.0319 | 5.625 | 90 | 0.2659 | 0.3844 | 0.4737 | 0.9423 | 0.1103 | 0.3823 | 0.4122 | 0.9902 | 0.1034 | 0.1010 | 0.3917 | 0.9416 |
0.0499 | 6.25 | 100 | 0.2393 | 0.4030 | 0.5532 | 0.9423 | 0.1065 | 0.6272 | 0.4963 | 0.9826 | 0.1006 | 0.1285 | 0.4407 | 0.9423 |
0.0394 | 6.875 | 110 | 0.2415 | 0.4024 | 0.5233 | 0.9463 | 0.1086 | 0.5205 | 0.4751 | 0.9890 | 0.1026 | 0.1137 | 0.4464 | 0.9470 |
0.037 | 7.5 | 120 | 0.2475 | 0.3916 | 0.4752 | 0.9458 | 0.1124 | 0.3507 | 0.4465 | 0.9912 | 0.1053 | 0.0904 | 0.4245 | 0.9462 |
0.0588 | 8.125 | 130 | 0.2458 | 0.4041 | 0.5223 | 0.9455 | 0.1276 | 0.5146 | 0.4577 | 0.9895 | 0.1171 | 0.1240 | 0.4292 | 0.9460 |
0.0426 | 8.75 | 140 | 0.2463 | 0.4046 | 0.5264 | 0.9459 | 0.1225 | 0.5322 | 0.4614 | 0.9897 | 0.1134 | 0.1252 | 0.4333 | 0.9467 |
0.0848 | 9.375 | 150 | 0.2388 | 0.4078 | 0.5297 | 0.9469 | 0.1154 | 0.5298 | 0.4849 | 0.9888 | 0.1078 | 0.1253 | 0.4506 | 0.9475 |
0.0574 | 10.0 | 160 | 0.2413 | 0.4091 | 0.5395 | 0.9467 | 0.1246 | 0.5579 | 0.4873 | 0.9882 | 0.1150 | 0.1215 | 0.4523 | 0.9478 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1