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segformer-finetuned-sidewalk-10k-steps
fuji12345/segformer-finetuned-sidewalk-10k-steps
segformer-finetuned-sidewalk-10k-steps is a image segmentation model from fuji12345. 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 other.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image Segmentation
- Library
- transformers
- License
- other
- Model type
- segformer
- Access
- Public
- Created
- May 29, 2025
- Updated
- May 29, 2025
- SHA
- 84efe7ef
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-finetuned-sidewalk-10k-steps
This model is a fine-tuned version of nvidia/mit-b0 on the fuji12345/sample-segmentation_dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.1181
- Mean Iou: 0.0
- Mean Accuracy: nan
- Overall Accuracy: nan
- Accuracy Hallucination: nan
- Accuracy Normal: nan
- Iou Hallucination: 0.0
- Iou Normal: 0.0
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: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: polynomial
- training_steps: 200
Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Hallucination | Accuracy Normal | Iou Hallucination | Iou Normal |
|---|---|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 21 | 0.4468 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 |
| No log | 2.0 | 42 | 0.2196 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 |
| No log | 3.0 | 63 | 0.2136 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 |
| No log | 4.0 | 84 | 0.1816 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 |
| 0.3217 | 5.0 | 105 | 0.1449 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 |
| 0.3217 | 6.0 | 126 | 0.1380 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 |
| 0.3217 | 7.0 | 147 | 0.1239 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 |
| 0.3217 | 8.0 | 168 | 0.1247 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 |
| 0.3217 | 9.0 | 189 | 0.1163 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 |
| 0.1596 | 9.5238 | 200 | 0.1181 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 |
Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
- Task
- Image Segmentation
- Library
- transformers
- Type
- segformer
- License
- other
- Created
- May 29, 2025
- Updated
- May 29, 2025