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---
license: other
base_model: nvidia/segformer-b5-finetuned-cityscapes-1024-1024
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b5-cityscapes-finetuned-coastTrain
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-b5-cityscapes-finetuned-coastTrain
This model is a fine-tuned version of [nvidia/segformer-b5-finetuned-cityscapes-1024-1024](https://huggingface.co./nvidia/segformer-b5-finetuned-cityscapes-1024-1024) on the peldrak/coastTrain dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4253
- Mean Iou: 0.5585
- Mean Accuracy: 0.6197
- Overall Accuracy: 0.8740
- Accuracy Water: 0.9765
- Accuracy Whitewater: 0.0159
- Accuracy Sediment: 0.6122
- Accuracy Other Natural Terrain: 0.0
- Accuracy Vegetation: 0.9255
- Accuracy Development: 0.8619
- Accuracy Unknown: 0.9457
- Iou Water: 0.8021
- Iou Whitewater: 0.0158
- Iou Sediment: 0.5787
- Iou Other Natural Terrain: 0.0
- Iou Vegetation: 0.8069
- Iou Development: 0.7835
- Iou Unknown: 0.9224
- F1 Score: 0.8596
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Water | Accuracy Whitewater | Accuracy Sediment | Accuracy Other Natural Terrain | Accuracy Vegetation | Accuracy Development | Accuracy Unknown | Iou Water | Iou Whitewater | Iou Sediment | Iou Other Natural Terrain | Iou Vegetation | Iou Development | Iou Unknown | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-----------------:|:------------------------------:|:-------------------:|:--------------------:|:----------------:|:---------:|:--------------:|:------------:|:-------------------------:|:--------------:|:---------------:|:-----------:|:--------:|
| 1.7218 | 0.16 | 20 | 1.5229 | 0.3639 | 0.4826 | 0.6959 | 0.6530 | 0.0033 | 0.6952 | 0.0067 | 0.8127 | 0.2824 | 0.9249 | 0.5955 | 0.0030 | 0.3817 | 0.0063 | 0.5740 | 0.2453 | 0.7415 | 0.6829 |
| 1.3084 | 0.31 | 40 | 1.1275 | 0.4131 | 0.5060 | 0.7566 | 0.7956 | 0.0011 | 0.5967 | 0.0 | 0.9496 | 0.3131 | 0.8858 | 0.6821 | 0.0011 | 0.4697 | 0.0 | 0.6092 | 0.2891 | 0.8406 | 0.7379 |
| 1.1538 | 0.47 | 60 | 0.8108 | 0.4722 | 0.5593 | 0.8123 | 0.8802 | 0.0001 | 0.6946 | 0.0 | 0.9169 | 0.5078 | 0.9153 | 0.7833 | 0.0001 | 0.4768 | 0.0 | 0.7188 | 0.4376 | 0.8889 | 0.8002 |
| 0.9791 | 0.62 | 80 | 0.6995 | 0.5264 | 0.6143 | 0.8518 | 0.9168 | 0.0000 | 0.7471 | 0.0 | 0.8479 | 0.8453 | 0.9433 | 0.8301 | 0.0000 | 0.5727 | 0.0 | 0.7406 | 0.6249 | 0.9164 | 0.8421 |
| 1.0426 | 0.78 | 100 | 0.5931 | 0.5280 | 0.6063 | 0.8523 | 0.8932 | 0.0003 | 0.6361 | 0.0 | 0.9550 | 0.8282 | 0.9309 | 0.8097 | 0.0003 | 0.5481 | 0.0 | 0.7440 | 0.6697 | 0.9243 | 0.8402 |
| 0.8008 | 0.93 | 120 | 0.4687 | 0.5485 | 0.6225 | 0.8706 | 0.9263 | 0.0 | 0.7444 | 0.0 | 0.9248 | 0.8212 | 0.9410 | 0.8404 | 0.0 | 0.5924 | 0.0 | 0.7871 | 0.6857 | 0.9337 | 0.8595 |
| 1.0298 | 1.09 | 140 | 0.4732 | 0.5527 | 0.6244 | 0.8726 | 0.9421 | 0.0000 | 0.8164 | 0.0 | 0.9047 | 0.7891 | 0.9185 | 0.8289 | 0.0000 | 0.6400 | 0.0 | 0.7976 | 0.6991 | 0.9036 | 0.8617 |
| 0.4902 | 1.24 | 160 | 0.3911 | 0.5713 | 0.6310 | 0.8868 | 0.9694 | 0.0 | 0.7543 | 0.0 | 0.9348 | 0.8241 | 0.9344 | 0.8366 | 0.0 | 0.6816 | 0.0 | 0.8102 | 0.7408 | 0.9295 | 0.8744 |
| 0.8204 | 1.4 | 180 | 0.4865 | 0.5210 | 0.5894 | 0.8522 | 0.9765 | 0.0 | 0.4534 | 0.0 | 0.9521 | 0.8103 | 0.9336 | 0.7833 | 0.0 | 0.4303 | 0.0 | 0.7921 | 0.7097 | 0.9313 | 0.8322 |
| 1.1865 | 1.55 | 200 | 0.3980 | 0.5668 | 0.6352 | 0.8838 | 0.9644 | 0.0000 | 0.7632 | 0.0 | 0.8985 | 0.8816 | 0.9385 | 0.8442 | 0.0000 | 0.6333 | 0.0 | 0.8133 | 0.7431 | 0.9338 | 0.8722 |
| 0.5676 | 1.71 | 220 | 0.3955 | 0.5598 | 0.6352 | 0.8750 | 0.9299 | 0.0 | 0.8440 | 0.0 | 0.9085 | 0.8890 | 0.8747 | 0.8160 | 0.0 | 0.6601 | 0.0 | 0.8209 | 0.7499 | 0.8721 | 0.8647 |
| 0.9343 | 1.86 | 240 | 0.3969 | 0.5809 | 0.6445 | 0.8944 | 0.9593 | 0.0001 | 0.8201 | 0.0 | 0.9120 | 0.8658 | 0.9539 | 0.8589 | 0.0001 | 0.6678 | 0.0 | 0.8327 | 0.7744 | 0.9326 | 0.8829 |
| 0.5811 | 2.02 | 260 | 0.4253 | 0.5585 | 0.6197 | 0.8740 | 0.9765 | 0.0159 | 0.6122 | 0.0 | 0.9255 | 0.8619 | 0.9457 | 0.8021 | 0.0158 | 0.5787 | 0.0 | 0.8069 | 0.7835 | 0.9224 | 0.8596 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.1
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