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segformer-b0-segments-floods
Tianmu28/segformer-b0-segments-floods
segformer-b0-segments-floods is a machine learning model from Tianmu28. Use it for the machine learning 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 other.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- other
- Model type
- segformer
- Access
- Public
- Created
- Dec 12, 2024
- Updated
- Dec 12, 2024
- SHA
- 0e1d6ac2
Base models
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-segments-sidewalk-finetuned
This model is a fine-tuned version of nvidia/segformer-b0-finetuned-ade-512-512 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2387
- Mean Iou: 0.8160
- Mean Accuracy: 0.8955
- Overall Accuracy: 0.9070
- Accuracy Background: 0.9351
- Accuracy Target: 0.8559
- Iou Background: 0.8665
- Iou Target: 0.7655
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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Target | Iou Background | Iou Target |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.2223 | 1.0 | 51 | 0.2299 | 0.8346 | 0.9082 | 0.9123 | 0.9335 | 0.8830 | 0.8608 | 0.8085 |
| 0.1991 | 2.0 | 102 | 0.2313 | 0.8371 | 0.9103 | 0.9136 | 0.9307 | 0.8900 | 0.8622 | 0.8120 |
| 0.1905 | 3.0 | 153 | 0.2269 | 0.8398 | 0.9112 | 0.9153 | 0.9368 | 0.8856 | 0.8653 | 0.8143 |
| 0.2218 | 4.0 | 204 | 0.2287 | 0.8407 | 0.9119 | 0.9158 | 0.9361 | 0.8877 | 0.8659 | 0.8155 |
| 0.2145 | 5.0 | 255 | 0.2275 | 0.8397 | 0.9125 | 0.9150 | 0.9279 | 0.8971 | 0.8637 | 0.8156 |
| 0.1905 | 6.0 | 306 | 0.2301 | 0.8395 | 0.9108 | 0.9152 | 0.9383 | 0.8832 | 0.8654 | 0.8137 |
| 0.2056 | 7.0 | 357 | 0.2278 | 0.8413 | 0.9116 | 0.9163 | 0.9410 | 0.8821 | 0.8672 | 0.8155 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3
- Library
- transformers
- Type
- segformer
- License
- other
- Created
- Dec 12, 2024
- Updated
- Dec 12, 2024