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--- |
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license: mit |
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base_model: openmmlab/upernet-swin-small |
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tags: |
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- image-segmentation |
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- vision |
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- generated_from_trainer |
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model-index: |
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- name: upernet-swin-small-finetuned |
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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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# upernet-swin-small-finetuned |
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This model is a fine-tuned version of [openmmlab/upernet-swin-small](https://huggingface.co/openmmlab/upernet-swin-small) on the jpodivin/plantorgans dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2914 |
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- Mean Iou: 0.4182 |
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- Mean Accuracy: 0.5282 |
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- Overall Accuracy: 0.7341 |
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- Accuracy Void: nan |
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- Accuracy Fruit: 0.8590 |
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- Accuracy Leaf: 0.7032 |
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- Accuracy Flower: 0.0 |
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- Accuracy Stem: 0.5505 |
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- Iou Void: 0.0 |
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- Iou Fruit: 0.8554 |
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- Iou Leaf: 0.6976 |
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- Iou Flower: 0.0 |
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- Iou Stem: 0.5381 |
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- Median Iou: 0.5381 |
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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: 0.0006 |
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- train_batch_size: 10 |
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- eval_batch_size: 10 |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Void | Accuracy Fruit | Accuracy Leaf | Accuracy Flower | Accuracy Stem | Iou Void | Iou Fruit | Iou Leaf | Iou Flower | Iou Stem | Median Iou | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------:|:--------------:|:-------------:|:---------------:|:-------------:|:--------:|:---------:|:--------:|:----------:|:--------:|:----------:| |
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| 0.8566 | 1.0 | 575 | 0.3365 | 0.3723 | 0.4705 | 0.6560 | nan | 0.8000 | 0.6122 | 0.0 | 0.4699 | 0.0 | 0.7976 | 0.6041 | 0.0 | 0.4598 | 0.4598 | |
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| 0.3338 | 2.0 | 1150 | 0.3030 | 0.3922 | 0.4937 | 0.7155 | nan | 0.8558 | 0.7024 | 0.0 | 0.4166 | 0.0 | 0.8517 | 0.6972 | 0.0 | 0.4119 | 0.4119 | |
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| 0.3477 | 3.0 | 1725 | 0.2914 | 0.4182 | 0.5282 | 0.7341 | nan | 0.8590 | 0.7032 | 0.0 | 0.5505 | 0.0 | 0.8554 | 0.6976 | 0.0 | 0.5381 | 0.5381 | |
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### Framework versions |
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- Transformers 4.38.0.dev0 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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