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--- |
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license: other |
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base_model: nvidia/mit-b5 |
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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_Clean_Set1_240430_V2-Augmented_mit-b5_RGB |
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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_Clean_Set1_240430_V2-Augmented_mit-b5_RGB |
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This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the Hasano20/Clean_Set1_240430_V2-Augmented dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4812 |
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- Mean Iou: 0.6072 |
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- Mean Accuracy: 0.6905 |
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- Overall Accuracy: 0.8761 |
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- Accuracy Background: 0.8967 |
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- Accuracy Melt: 0.2439 |
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- Accuracy Substrate: 0.9311 |
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- Iou Background: 0.8325 |
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- Iou Melt: 0.1423 |
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- Iou Substrate: 0.8467 |
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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: 0.0001 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Melt | Accuracy Substrate | Iou Background | Iou Melt | Iou Substrate | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:| |
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| 0.2826 | 6.6667 | 20 | 0.4812 | 0.6072 | 0.6905 | 0.8761 | 0.8967 | 0.2439 | 0.9311 | 0.8325 | 0.1423 | 0.8467 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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