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segformer-b0-finetuned-segments-water-2
imadd/segformer-b0-finetuned-segments-water-2
segformer-b0-finetuned-segments-water-2 is a image segmentation model from imadd. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image Segmentation
- Library
- transformers
- License
- apache-2.0
- Model type
- segformer
- Access
- Public
- Created
- Jul 7, 2022
- Updated
- Jul 7, 2022
- SHA
- e6efa638
Model card
From the Hugging Face model README
<!-- 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-finetuned-segments-water-2
This model is a fine-tuned version of nvidia/mit-b0 on the imadd/water_dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.5845
- Mean Iou: nan
- Mean Accuracy: nan
- Overall Accuracy: nan
- Per Category Iou: [nan, nan]
- Per Category Accuracy: [nan, nan]
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|---|---|---|---|---|---|---|---|---|
| 0.5241 | 6.67 | 20 | 0.5845 | nan | nan | nan | [nan, nan] | [nan, nan] |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
- Task
- Image Segmentation
- Library
- transformers
- Type
- segformer
- License
- apache-2.0
- Created
- Jul 7, 2022
- Updated
- Jul 7, 2022