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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-oldapp-oct-1 |
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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-oldapp-oct-1 |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the PushkarA07/oldapptiles5 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2630 |
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- Mean Iou: 0.4984 |
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- Mean Accuracy: 0.4989 |
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- Overall Accuracy: 0.9967 |
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- Accuracy Abnormality: 0.0 |
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- Iou Abnormality: 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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Abnormality | Iou Abnormality | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------:|:---------------:| |
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| 0.6912 | 0.7143 | 10 | 0.6575 | 0.4714 | 0.4784 | 0.9426 | 0.0133 | 0.0002 | |
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| 0.5802 | 1.4286 | 20 | 0.5878 | 0.4942 | 0.4947 | 0.9884 | 0.0 | 0.0 | |
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| 0.5498 | 2.1429 | 30 | 0.4770 | 0.4984 | 0.4989 | 0.9968 | 0.0 | 0.0 | |
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| 0.6084 | 2.8571 | 40 | 0.4125 | 0.4971 | 0.4976 | 0.9941 | 0.0 | 0.0 | |
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| 0.4675 | 3.5714 | 50 | 0.4355 | 0.4885 | 0.4992 | 0.9761 | 0.0213 | 0.0009 | |
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| 0.3863 | 4.2857 | 60 | 0.3699 | 0.4965 | 0.5005 | 0.9920 | 0.0081 | 0.0010 | |
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| 0.3954 | 5.0 | 70 | 0.3401 | 0.4983 | 0.4989 | 0.9967 | 0.0 | 0.0 | |
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| 0.3286 | 5.7143 | 80 | 0.3279 | 0.4983 | 0.4988 | 0.9967 | 0.0 | 0.0 | |
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| 0.3458 | 6.4286 | 90 | 0.2908 | 0.4974 | 0.4979 | 0.9948 | 0.0 | 0.0 | |
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| 0.3559 | 7.1429 | 100 | 0.2693 | 0.4989 | 0.4994 | 0.9978 | 0.0 | 0.0 | |
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| 0.3196 | 7.8571 | 110 | 0.2596 | 0.4977 | 0.4982 | 0.9954 | 0.0 | 0.0 | |
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| 0.3109 | 8.5714 | 120 | 0.2915 | 0.4958 | 0.4963 | 0.9916 | 0.0 | 0.0 | |
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| 0.2711 | 9.2857 | 130 | 0.2720 | 0.4991 | 0.4997 | 0.9983 | 0.0 | 0.0 | |
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| 0.3051 | 10.0 | 140 | 0.2630 | 0.4984 | 0.4989 | 0.9967 | 0.0 | 0.0 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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