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---
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library_name: transformers
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license: other
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base_model: nvidia/mit-b0
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tags:
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- vision
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- image-segmentation
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- generated_from_trainer
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model-index:
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- name: segformer-b0-finetuned-segments-sidewalk-oct-22
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# segformer-b0-finetuned-segments-sidewalk-oct-22
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the adsjnajefwnb/di dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.1425
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- Mean Iou: 0.4608
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- Mean Accuracy: 0.6144
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- Overall Accuracy: 0.9524
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- Accuracy Unlabeled: nan
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- Accuracy Anqvan: nan
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- Accuracy Baohuxiang: nan
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- Accuracy Biaozhi: nan
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- Accuracy Dianlan: 0.9591
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- Accuracy Miehuoqi: 0.8841
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- Accuracy Zhaoming: 0.0
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- Iou Unlabeled: 0.0
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- Iou Anqvan: nan
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- Iou Baohuxiang: nan
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- Iou Biaozhi: nan
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- Iou Dianlan: 0.9591
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- Iou Miehuoqi: 0.8841
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- Iou Zhaoming: 0.0
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 6e-05
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- train_batch_size: 2
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- eval_batch_size: 2
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- seed: 42
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 50
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Anqvan | Accuracy Baohuxiang | Accuracy Biaozhi | Accuracy Dianlan | Accuracy Miehuoqi | Accuracy Zhaoming | Iou Unlabeled | Iou Anqvan | Iou Baohuxiang | Iou Biaozhi | Iou Dianlan | Iou Miehuoqi | Iou Zhaoming |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:---------------:|:-------------------:|:----------------:|:----------------:|:-----------------:|:-----------------:|:-------------:|:----------:|:--------------:|:-----------:|:-----------:|:------------:|:------------:|
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| 1.5348 | 10.0 | 20 | 1.8080 | 0.3181 | 0.6370 | 0.9720 | nan | nan | nan | nan | 0.9776 | 0.9334 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.9759 | 0.9328 | 0.0 |
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| 1.5003 | 20.0 | 40 | 1.5287 | 0.3865 | 0.6442 | 0.9726 | nan | nan | nan | nan | 0.9773 | 0.9552 | 0.0 | 0.0 | nan | nan | 0.0 | 0.9773 | 0.9552 | 0.0 |
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| 1.4909 | 30.0 | 60 | 1.2543 | 0.4770 | 0.6359 | 0.9695 | nan | nan | nan | nan | 0.9750 | 0.9328 | 0.0 | 0.0 | nan | nan | nan | 0.9750 | 0.9328 | 0.0 |
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| 1.331 | 40.0 | 80 | 1.1463 | 0.4671 | 0.6229 | 0.9613 | nan | nan | nan | nan | 0.9677 | 0.9009 | 0.0 | 0.0 | nan | nan | nan | 0.9677 | 0.9009 | 0.0 |
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| 1.0843 | 50.0 | 100 | 1.1425 | 0.4608 | 0.6144 | 0.9524 | nan | nan | nan | nan | 0.9591 | 0.8841 | 0.0 | 0.0 | nan | nan | nan | 0.9591 | 0.8841 | 0.0 |
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### Framework versions
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- Transformers 4.46.3
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- Pytorch 1.12.1+cu113
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- Datasets 3.1.0
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- Tokenizers 0.20.3
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