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---
license: other
base_model: nvidia/segformer-b0-finetuned-ade-512-512
tags:
- generated_from_trainer
model-index:
- name: segformer-breastcancer
results: []
datasets:
- as-cle-bert/breastcancer-semantic-segmentation
pipeline_tag: image-segmentation
---
<!-- 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-breastcancer
This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1986
- Mean Iou: 0.4951
- Mean Accuracy: 0.5647
- Overall Accuracy: 0.5716
- Per Category Iou: [0.41886373003284666, 0.5713219432574086]
- Per Category Accuracy: [0.542773911636187, 0.5866474640793707]
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------------------------------:|:------------------------------------------:|
| 0.9179 | 1.25 | 20 | 0.8275 | 0.1056 | 0.2990 | 0.2215 | [0.15928433223106872, 0.05189369644942194] | [0.5449101796407185, 0.053152424747755486] |
| 0.7951 | 2.5 | 40 | 0.7554 | 0.3808 | 0.6154 | 0.6539 | [0.2962250026735109, 0.46535774064135604] | [0.4931218643793494, 0.7375983290380178] |
| 0.6317 | 3.75 | 60 | 0.5784 | 0.2076 | 0.3576 | 0.3005 | [0.24602488191071786, 0.16910477266308951] | [0.5386308464152776, 0.17651220374955784] |
| 0.5525 | 5.0 | 80 | 0.4935 | 0.3310 | 0.4279 | 0.3908 | [0.3572223576675606, 0.30487703968490387] | [0.5453956950962939, 0.31031549514039786] |
| 0.4365 | 6.25 | 100 | 0.4277 | 0.4259 | 0.5007 | 0.5093 | [0.3753112405986087, 0.4765198093920762] | [0.473248098397799, 0.528071150639244] |
| 0.3658 | 7.5 | 120 | 0.3757 | 0.3739 | 0.4207 | 0.4501 | [0.2934911929427469, 0.45430117531467024] | [0.32736688784592977, 0.5140397864133273] |
| 0.357 | 8.75 | 140 | 0.3155 | 0.4305 | 0.5273 | 0.5652 | [0.31276016750127367, 0.5482260296446353] | [0.40734746722770676, 0.6473124799973049] |
| 0.2889 | 10.0 | 160 | 0.3121 | 0.4761 | 0.5439 | 0.5495 | [0.39972203089638886, 0.5525428502787649] | [0.5259588930247613, 0.56174305590648] |
| 0.2536 | 11.25 | 180 | 0.2611 | 0.4607 | 0.5411 | 0.5586 | [0.37248963582652733, 0.5489196143472734] | [0.4856772940605276, 0.5965098455370829] |
| 0.3375 | 12.5 | 200 | 0.2522 | 0.3905 | 0.4676 | 0.4535 | [0.3615823724169426, 0.4193968866718472] | [0.512558666450882, 0.4227348526959422] |
| 0.1835 | 13.75 | 220 | 0.2393 | 0.4343 | 0.4809 | 0.5004 | [0.3816968232451229, 0.4869246466631396] | [0.41924259588930246, 0.5425994239223811] |
| 0.1878 | 15.0 | 240 | 0.2364 | 0.3883 | 0.4769 | 0.4591 | [0.3594858252766199, 0.4170536161683648] | [0.5331607056157954, 0.42058719490626106] |
| 0.1804 | 16.25 | 260 | 0.2388 | 0.3503 | 0.4221 | 0.3934 | [0.3722368961671656, 0.3283766624340039] | [0.5131736526946108, 0.3310593427324945] |
| 0.2296 | 17.5 | 280 | 0.2108 | 0.3845 | 0.4523 | 0.4383 | [0.36382381172455475, 0.4051134890024848] | [0.4968765172357987, 0.40781915879192143] |
| 0.1752 | 18.75 | 300 | 0.2065 | 0.4408 | 0.5307 | 0.5278 | [0.37362255868123995, 0.5080655748465653] | [0.539941738145331, 0.5215102666464534] |
| 0.1404 | 20.0 | 320 | 0.2025 | 0.4192 | 0.5049 | 0.4948 | [0.37603680369849973, 0.4624047452321127] | [0.5370771969574365, 0.4727289571647548] |
| 0.1044 | 21.25 | 340 | 0.1993 | 0.4134 | 0.5006 | 0.4938 | [0.36164057945015027, 0.46514651056315] | [0.5219938501375627, 0.4791635083463877] |
| 0.1047 | 22.5 | 360 | 0.1995 | 0.4409 | 0.5612 | 0.5654 | [0.35316826827766823, 0.5286988461568266] | [0.5477909046771322, 0.5746205804571564] |
| 0.0969 | 23.75 | 380 | 0.1934 | 0.4208 | 0.5256 | 0.5171 | [0.3610564616784075, 0.480532337904731] | [0.5524356692021363, 0.49872824970101237] |
| 0.1198 | 25.0 | 400 | 0.2100 | 0.4047 | 0.4892 | 0.4726 | [0.377810637529348, 0.43159533203482664] | [0.5416895937854022, 0.4366988394225748] |
| 0.116 | 26.25 | 420 | 0.2038 | 0.4208 | 0.5123 | 0.5040 | [0.3659432240473206, 0.47558361909786334] | [0.5386632141123159, 0.48590968046220967] |
| 0.0803 | 27.5 | 440 | 0.2035 | 0.4643 | 0.5486 | 0.5520 | [0.3885018236229309, 0.5400125204269953] | [0.537854021686357, 0.5594101099937676] |
| 0.1031 | 28.75 | 460 | 0.2068 | 0.4193 | 0.5268 | 0.5199 | [0.3565531095848628, 0.48207738324971056] | [0.5486324648001295, 0.5049522461973824] |
| 0.0652 | 30.0 | 480 | 0.1906 | 0.4799 | 0.5572 | 0.5719 | [0.39256244632789455, 0.5671483599490623] | [0.5104709499919081, 0.6039045260835144] |
| 0.0865 | 31.25 | 500 | 0.1946 | 0.4660 | 0.5319 | 0.5360 | [0.4022848534304187, 0.5297039831736081] | [0.5185952419485353, 0.5451176579581248] |
| 0.0781 | 32.5 | 520 | 0.2018 | 0.4170 | 0.4977 | 0.4881 | [0.37508619500758517, 0.4588260589120619] | [0.5281922641204079, 0.46729664628497314] |
| 0.0922 | 33.75 | 540 | 0.1932 | 0.4649 | 0.5558 | 0.5608 | [0.39512968947922955, 0.5346638407173079] | [0.5401683120245995, 0.571521215490087] |
| 0.0802 | 35.0 | 560 | 0.2029 | 0.4519 | 0.5364 | 0.5344 | [0.3877223005943433, 0.5161263869184783] | [0.5426606246965529, 0.5300756312429464] |
| 0.0737 | 36.25 | 580 | 0.1983 | 0.4605 | 0.5598 | 0.5666 | [0.3930664524057094, 0.5280028151990147] | [0.5383719048389707, 0.5812993750736941] |
| 0.0766 | 37.5 | 600 | 0.2097 | 0.4902 | 0.5645 | 0.5701 | [0.41298901286924217, 0.5674679408239331] | [0.5468846091600582, 0.5821500160021561] |
| 0.0663 | 38.75 | 620 | 0.1926 | 0.5041 | 0.5653 | 0.5781 | [0.42229021548076295, 0.5859655697770101] | [0.5249069428710147, 0.6057405629390065] |
| 0.0572 | 40.0 | 640 | 0.1944 | 0.4884 | 0.5550 | 0.5643 | [0.41379925802215733, 0.5630840363400389] | [0.525295355235475, 0.5846429834756683] |
| 0.1065 | 41.25 | 660 | 0.1949 | 0.4713 | 0.5603 | 0.5687 | [0.4052270716602772, 0.537297205601135] | [0.5337271403139666, 0.5868664409520441] |
| 0.0881 | 42.5 | 680 | 0.1945 | 0.4557 | 0.5355 | 0.5362 | [0.38861418270649184, 0.5228113541121006] | [0.5329341317365269, 0.5379672208465983] |
| 0.0616 | 43.75 | 700 | 0.2055 | 0.4851 | 0.5479 | 0.5493 | [0.4288067420034476, 0.5413945423770796] | [0.543486000971031, 0.5522512506948305] |
| 0.135 | 45.0 | 720 | 0.2017 | 0.4950 | 0.5702 | 0.5770 | [0.4186215922560253, 0.5714192766576933] | [0.5487133840427254, 0.5917428874627318] |
| 0.0683 | 46.25 | 740 | 0.1986 | 0.4880 | 0.5579 | 0.5633 | [0.41617258731503165, 0.5599071727881785] | [0.5407347467227707, 0.5750585342025031] |
| 0.0962 | 47.5 | 760 | 0.2010 | 0.4907 | 0.5660 | 0.5730 | [0.41037067786677084, 0.571094427269902] | [0.543955332578087, 0.5881213468762106] |
| 0.0534 | 48.75 | 780 | 0.2061 | 0.4941 | 0.5671 | 0.5740 | [0.4158937943809818, 0.5723742349360128] | [0.5450234665803528, 0.5891404315528829] |
| 0.069 | 50.0 | 800 | 0.1986 | 0.4951 | 0.5647 | 0.5716 | [0.41886373003284666, 0.5713219432574086] | [0.542773911636187, 0.5866474640793707] |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |