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---
license: other
base_model: nvidia/mit-b1
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b1-finetuned-segments-graffiti
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-b1-finetuned-segments-graffiti
This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the Adriatogi/graffiti dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2171
- Mean Iou: 0.8381
- Mean Accuracy: 0.9102
- Overall Accuracy: 0.9168
- Accuracy Not Graf: 0.9379
- Accuracy Graf: 0.8826
- Iou Not Graf: 0.8748
- Iou Graf: 0.8015
## 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: 0.0001
- 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
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Not Graf | Accuracy Graf | Iou Not Graf | Iou Graf |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------------:|:-------------:|:------------:|:--------:|
| 0.4076 | 0.42 | 20 | 0.5389 | 0.6053 | 0.7982 | 0.7541 | 0.6139 | 0.9825 | 0.6073 | 0.6033 |
| 0.3386 | 0.83 | 40 | 0.2883 | 0.7962 | 0.8984 | 0.8898 | 0.8625 | 0.9343 | 0.8290 | 0.7634 |
| 0.1964 | 1.25 | 60 | 0.2514 | 0.8061 | 0.9009 | 0.8964 | 0.8819 | 0.9200 | 0.8406 | 0.7716 |
| 0.1723 | 1.67 | 80 | 0.2259 | 0.8269 | 0.9058 | 0.9100 | 0.9235 | 0.8880 | 0.8641 | 0.7898 |
| 0.1981 | 2.08 | 100 | 0.2338 | 0.8119 | 0.9040 | 0.8999 | 0.8869 | 0.9210 | 0.8459 | 0.7778 |
| 0.2827 | 2.5 | 120 | 0.2106 | 0.8251 | 0.9080 | 0.9084 | 0.9095 | 0.9066 | 0.8601 | 0.7902 |
| 0.1864 | 2.92 | 140 | 0.2241 | 0.8232 | 0.8956 | 0.9097 | 0.9546 | 0.8365 | 0.8675 | 0.7790 |
| 0.1362 | 3.33 | 160 | 0.2185 | 0.8257 | 0.8978 | 0.9109 | 0.9525 | 0.8431 | 0.8688 | 0.7826 |
| 0.1264 | 3.75 | 180 | 0.2155 | 0.8237 | 0.9054 | 0.9079 | 0.9156 | 0.8952 | 0.8602 | 0.7871 |
| 0.1688 | 4.17 | 200 | 0.2241 | 0.8206 | 0.8985 | 0.9072 | 0.9346 | 0.8625 | 0.8618 | 0.7795 |
| 0.1198 | 4.58 | 220 | 0.2080 | 0.8331 | 0.9087 | 0.9137 | 0.9296 | 0.8877 | 0.8697 | 0.7965 |
| 0.111 | 5.0 | 240 | 0.2033 | 0.8369 | 0.9133 | 0.9154 | 0.9221 | 0.9044 | 0.8710 | 0.8027 |
| 0.2003 | 5.42 | 260 | 0.2214 | 0.8262 | 0.9118 | 0.9084 | 0.8976 | 0.9261 | 0.8586 | 0.7938 |
| 0.1369 | 5.83 | 280 | 0.2044 | 0.8396 | 0.9147 | 0.9170 | 0.9245 | 0.9048 | 0.8734 | 0.8058 |
| 0.1901 | 6.25 | 300 | 0.1968 | 0.8411 | 0.9119 | 0.9185 | 0.9393 | 0.8846 | 0.8771 | 0.8050 |
| 0.1887 | 6.67 | 320 | 0.2098 | 0.8367 | 0.9100 | 0.9159 | 0.9344 | 0.8857 | 0.8731 | 0.8002 |
| 0.0738 | 7.08 | 340 | 0.2205 | 0.8357 | 0.9127 | 0.9147 | 0.9211 | 0.9043 | 0.8699 | 0.8014 |
| 0.1166 | 7.5 | 360 | 0.2274 | 0.8317 | 0.9046 | 0.9135 | 0.9420 | 0.8672 | 0.8709 | 0.7924 |
| 0.1247 | 7.92 | 380 | 0.2225 | 0.8310 | 0.9051 | 0.9130 | 0.9381 | 0.8722 | 0.8698 | 0.7923 |
| 0.1212 | 8.33 | 400 | 0.2230 | 0.8345 | 0.9108 | 0.9143 | 0.9254 | 0.8961 | 0.8699 | 0.7991 |
| 0.0979 | 8.75 | 420 | 0.2226 | 0.8352 | 0.9076 | 0.9153 | 0.9400 | 0.8752 | 0.8730 | 0.7973 |
| 0.0984 | 9.17 | 440 | 0.2189 | 0.8354 | 0.9106 | 0.9149 | 0.9287 | 0.8925 | 0.8712 | 0.7997 |
| 0.1151 | 9.58 | 460 | 0.2185 | 0.8382 | 0.9098 | 0.9170 | 0.9396 | 0.8800 | 0.8751 | 0.8013 |
| 0.0989 | 10.0 | 480 | 0.2171 | 0.8381 | 0.9102 | 0.9168 | 0.9379 | 0.8826 | 0.8748 | 0.8015 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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