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---
license: other
base_model: nvidia/mit-b3
tags:
- generated_from_trainer
model-index:
- name: segformer-roof
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-roof
This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1424
- Mean Iou: 0.6570
- Mean Accuracy: 0.7338
- Overall Accuracy: 0.9484
- Per Category Iou: [0.9476783882507867, 0.42934908622698137, 0.5939862114533478]
- Per Category Accuracy: [0.9826387957018243, 0.5398301458913112, 0.6788943731278633]
## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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 | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------------------------------------------:|:-------------------------------------------------------------:|
| 0.2499 | 1.0 | 930 | 0.1815 | 0.5867 | 0.6662 | 0.9365 | [0.9366364285161356, 0.31719261329477677, 0.5063121008274123] | [0.9792975820886569, 0.391370646753873, 0.6280270152281657] |
| 0.1757 | 2.0 | 1860 | 0.1714 | 0.6022 | 0.6773 | 0.9395 | [0.9393634573331426, 0.3425379991526554, 0.5248483866382888] | [0.9812229375223134, 0.428240950515148, 0.6224989524490145] |
| 0.1594 | 3.0 | 2790 | 0.1629 | 0.6084 | 0.6710 | 0.9420 | [0.9416754823459575, 0.34843136321450174, 0.5350688263682425] | [0.9853655573464773, 0.43172953399691005, 0.5959325404903174] |
| 0.1523 | 4.0 | 3720 | 0.1596 | 0.6076 | 0.6748 | 0.9431 | [0.9431751130845217, 0.3239168742399479, 0.5556505610312258] | [0.9852091404732521, 0.3708811641866297, 0.6682165559673183] |
| 0.1439 | 5.0 | 4650 | 0.1523 | 0.6302 | 0.7055 | 0.9446 | [0.9440239408727975, 0.37488313572379306, 0.5716850871490047] | [0.9823225423399531, 0.45512728622828424, 0.6790873748340986] |
| 0.1371 | 6.0 | 5580 | 0.1507 | 0.6435 | 0.7255 | 0.9454 | [0.9448592138832133, 0.40938956878632793, 0.5763842195624918] | [0.9805292644632151, 0.5269108861841263, 0.6691477422245232] |
| 0.1353 | 7.0 | 6510 | 0.1483 | 0.6535 | 0.7471 | 0.9454 | [0.945006495944485, 0.428182455906198, 0.5872114398937206] | [0.977372124880732, 0.5920333447023434, 0.6719542751488515] |
| 0.1313 | 8.0 | 7440 | 0.1475 | 0.6543 | 0.7416 | 0.9465 | [0.9458449483191159, 0.4282098022538787, 0.5888713805135868] | [0.9792916130278106, 0.5663642070609791, 0.6792639127879244] |
| 0.1274 | 9.0 | 8370 | 0.1446 | 0.6511 | 0.7257 | 0.9477 | [0.9470057563004056, 0.41812909680083715, 0.5881565577705948] | [0.983095828320569, 0.5191575508625893, 0.6747574079503997] |
| 0.1274 | 10.0 | 9300 | 0.1424 | 0.6570 | 0.7338 | 0.9484 | [0.9476783882507867, 0.42934908622698137, 0.5939862114533478] | [0.9826387957018243, 0.5398301458913112, 0.6788943731278633] |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
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