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---
license: apache-2.0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-pavement
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-b0-pavement
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the reannayang/FL_pavement dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4558
- Mean Iou: 0.6310
- Mean Accuracy: 0.7758
- Overall Accuracy: 0.9687
- Per Category Iou: [0.0, 0.9582107718835582, 0.9831802335937301, 0.0, 0.9070478290281362, 0.9376628700260592]
- Per Category Accuracy: [nan, 0.964865692983352, 0.9920279343235298, 0.0, 0.9513585956798234, 0.9709244156080405]
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------:|
| 1.0751 | 10.0 | 20 | 1.1494 | 0.5858 | 0.7574 | 0.9436 | [0.0, 0.9446293533289976, 0.9477727359877293, 0.0, 0.7979866954491387, 0.8246812143200128] | [nan, 0.9457026861403329, 0.951885181714315, 0.0, 0.9519892783938614, 0.93727071521693] |
| 0.8732 | 20.0 | 40 | 0.6625 | 0.6209 | 0.7710 | 0.9635 | [0.0, 0.9549288283268325, 0.9793625315294657, 0.0, 0.8879129036248917, 0.903449846293977] | [nan, 0.9626825656214891, 0.9869186233102687, 0.0, 0.9430020497188206, 0.9623139192917564] |
| 0.4736 | 30.0 | 60 | 0.5124 | 0.6302 | 0.7753 | 0.9687 | [0.0, 0.9600678403726158, 0.9813250559739467, 0.0, 0.9086066088704611, 0.9311320479537963] | [nan, 0.968414870799714, 0.990282039102918, 0.0, 0.9501760655910022, 0.9678319134099385] |
| 0.503 | 40.0 | 80 | 0.4726 | 0.6305 | 0.7753 | 0.9680 | [0.0, 0.9574198453934862, 0.9821685397548652, 0.0, 0.9074944966980188, 0.9358034824412265] | [nan, 0.9645337554897355, 0.9906414881189263, 0.0, 0.953329479161192, 0.9678925507079404] |
| 0.6762 | 50.0 | 100 | 0.4558 | 0.6310 | 0.7758 | 0.9687 | [0.0, 0.9582107718835582, 0.9831802335937301, 0.0, 0.9070478290281362, 0.9376628700260592] | [nan, 0.964865692983352, 0.9920279343235298, 0.0, 0.9513585956798234, 0.9709244156080405] |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
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