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---
license: cc
base_model: nvidia/segformer-b0-finetuned-cityscapes-1024-1024
tags:
- generated_from_trainer
- medical
- computer-vision
- image-segmentation
- breast-cancer
model-index:
- name: segformer-v1-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-v1-breastcancer
This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-cityscapes-1024-1024](https://huggingface.co/nvidia/segformer-b0-finetuned-cityscapes-1024-1024) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2084
- Mean Iou: 0.6074
- Mean Accuracy: 0.7133
- Overall Accuracy: 0.6718
- Per Category Iou: [0.6503515075769412, 0.5644565972298056]
- Per Category Accuracy: [0.7843872475128127, 0.6421245639664888]
## 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: 3
- eval_batch_size: 3
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------------------------------------:|:---------------------------------------------:|
| 1.0349 | 1.82 | 20 | 0.9385 | 0.1001 | 0.3453 | 0.5410 | [0.00702490904002239, 0.19315512632820392] | [0.00945884835694905, 0.6811663337407948] |
| 0.8631 | 3.64 | 40 | 0.8712 | 0.1270 | 0.3748 | 0.5931 | [9.482867619168036e-05, 0.25396146547703435] | [0.00011305396442568585, 0.7494807350208202] |
| 0.6657 | 5.45 | 60 | 0.6510 | 0.1313 | 0.2115 | 0.3347 | [0.00014806040864672785, 0.26239433754030744] | [0.00015073861923424781, 0.42294505232402135] |
| 0.6924 | 7.27 | 80 | 0.5721 | 0.1917 | 0.3061 | 0.4833 | [0.002107933665379521, 0.38125252274350296] | [0.0021291829966837506, 0.6101487731433172] |
| 0.5177 | 9.09 | 100 | 0.4836 | 0.1991 | 0.3081 | 0.4876 | [0.0, 0.3981538560328774] | [0.0, 0.6162060363932699] |
| 0.3851 | 10.91 | 120 | 0.4029 | 0.2127 | 0.2893 | 0.4440 | [0.023690796530116853, 0.40166184061437743] | [0.02372249020198975, 0.5548632022499826] |
| 0.3266 | 12.73 | 140 | 0.3811 | 0.2300 | 0.3350 | 0.5130 | [0.028268806709322924, 0.43178856750464767] | [0.02946940006029545, 0.6405295012074774] |
| 0.3397 | 14.55 | 160 | 0.3353 | 0.2616 | 0.3719 | 0.5640 | [0.04190433583118965, 0.4812188969936601] | [0.042357552004823634, 0.7015294713932202] |
| 0.3008 | 16.36 | 180 | 0.3363 | 0.3885 | 0.4376 | 0.4135 | [0.4227194892852987, 0.35420100310527275] | [0.47910385890865237, 0.3961867565069616] |
| 0.2558 | 18.18 | 200 | 0.3163 | 0.4200 | 0.4832 | 0.4322 | [0.48242302607476784, 0.35761699452079404] | [0.570677570093458, 0.3956699760492134] |
| 0.2686 | 20.0 | 220 | 0.2771 | 0.4777 | 0.5444 | 0.5868 | [0.4603203796001692, 0.49515000498355427] | [0.4716046126017486, 0.6171352474086441] |
| 0.1953 | 21.82 | 240 | 0.2811 | 0.4756 | 0.5676 | 0.5920 | [0.46844517569632155, 0.4827354154204578] | [0.5257386192342478, 0.6095276427854467] |
| 0.1623 | 23.64 | 260 | 0.2612 | 0.4833 | 0.5416 | 0.5447 | [0.506478482184174, 0.46020570281796136] | [0.5361961109436237, 0.5469524860121443] |
| 0.1851 | 25.45 | 280 | 0.2620 | 0.5107 | 0.5880 | 0.5313 | [0.5881106780729983, 0.4333538137452822] | [0.6852389207114863, 0.49066316846049113] |
| 0.1315 | 27.27 | 300 | 0.2230 | 0.6652 | 0.7361 | 0.6967 | [0.7577948727059535, 0.5726185409040606] | [0.8037006331022007, 0.6684903053973743] |
| 0.1294 | 29.09 | 320 | 0.2330 | 0.5189 | 0.6179 | 0.6328 | [0.506419446816051, 0.5313992809888866] | [0.5923462466083811, 0.6434165151108594] |
| 0.1532 | 30.91 | 340 | 0.2326 | 0.5319 | 0.6251 | 0.6503 | [0.5461152173144251, 0.5176845532961513] | [0.581945281881218, 0.6683163888971706] |
| 0.1074 | 32.73 | 360 | 0.2280 | 0.5790 | 0.6418 | 0.5960 | [0.6624514966740577, 0.4955288623414331] | [0.7205682845945132, 0.5631018753167765] |
| 0.1184 | 34.55 | 380 | 0.2168 | 0.6385 | 0.7453 | 0.7145 | [0.7140882114917724, 0.5629577265658137] | [0.7980479348809165, 0.6925007205112151] |
| 0.1411 | 36.36 | 400 | 0.2191 | 0.5935 | 0.6776 | 0.6459 | [0.6633485862587079, 0.5236754959973609] | [0.7320432619837203, 0.6231328821442413] |
| 0.1224 | 38.18 | 420 | 0.2068 | 0.6114 | 0.6869 | 0.6689 | [0.6632029659025639, 0.5596692813228747] | [0.717949201085318, 0.6559037198254872] |
| 0.0892 | 40.0 | 440 | 0.2096 | 0.5867 | 0.6817 | 0.6756 | [0.6250170137471076, 0.548339821945447] | [0.692191739523666, 0.6711785575862378] |
| 0.103 | 41.82 | 460 | 0.2117 | 0.5693 | 0.6553 | 0.6511 | [0.6029494984137872, 0.5356447598629901] | [0.6625150738619234, 0.6480725082734564] |
| 0.0996 | 43.64 | 480 | 0.2082 | 0.6011 | 0.7024 | 0.6743 | [0.6408627400521119, 0.5614076241331366] | [0.7507725354235755, 0.6540800810947796] |
| 0.1095 | 45.45 | 500 | 0.2065 | 0.6254 | 0.7302 | 0.6836 | [0.6779631615467104, 0.5728211009174312] | [0.8100504974374435, 0.6502936704332012] |
| 0.097 | 47.27 | 520 | 0.2083 | 0.6079 | 0.7042 | 0.6628 | [0.6564823383005202, 0.5592888498683055] | [0.7753052457039493, 0.6330858749987578] |
| 0.0866 | 49.09 | 540 | 0.2084 | 0.6074 | 0.7133 | 0.6718 | [0.6503515075769412, 0.5644565972298056] | [0.7843872475128127, 0.6421245639664888] |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |