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segformer-b1-finetuned-segments-pv_v1_normalized_t4_16batch
mouadenna/segformer-b1-finetuned-segments-pv_v1_normalized_t4_16batch
segformer-b1-finetuned-segments-pv_v1_normalized_t4_16batch is a image segmentation model from mouadenna. 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
- Jul 26, 2024
- Updated
- Jul 26, 2024
- SHA
- 396ba48e
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-b1-finetuned-segments-pv_v1_normalized_t4_16batch
This model is a fine-tuned version of nvidia/segformer-b3-finetuned-ade-512-512 on the mouadenna/satellite_PV_dataset_train_test_v1 dataset. It achieves the following results on the evaluation set:
- Loss: nan
- Mean Iou: 0.0
- Precision: 1.0
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: 0.0004
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Precision |
|---|---|---|---|---|---|
| 0.0056 | 0.9989 | 229 | 0.0099 | 0.7568 | 0.7827 |
| 0.0092 | 1.9978 | 458 | 0.0113 | 0.7247 | 0.7360 |
| 0.0069 | 2.9967 | 687 | 0.0051 | 0.8440 | 0.9106 |
| 0.0159 | 4.0 | 917 | 0.0057 | 0.8317 | 0.8635 |
| 0.0124 | 4.9989 | 1146 | 0.0099 | 0.8223 | 0.9292 |
| 0.0033 | 5.9978 | 1375 | 0.2415 | 0.0 | 1.0 |
| 0.0 | 6.9967 | 1604 | nan | 0.0 | 1.0 |
| 0.0 | 8.0 | 1834 | nan | 0.0 | 1.0 |
| 0.0 | 8.9989 | 2063 | nan | 0.0 | 1.0 |
| 0.0 | 9.9978 | 2292 | nan | 0.0 | 1.0 |
| 0.0 | 10.9967 | 2521 | nan | 0.0 | 1.0 |
| 0.0 | 12.0 | 2751 | nan | 0.0 | 1.0 |
| 0.0 | 12.9989 | 2980 | nan | 0.0 | 1.0 |
| 0.0 | 13.9978 | 3209 | nan | 0.0 | 1.0 |
| 0.0 | 14.9967 | 3438 | nan | 0.0 | 1.0 |
| 0.0 | 16.0 | 3668 | nan | 0.0 | 1.0 |
| 0.0 | 16.9989 | 3897 | nan | 0.0 | 1.0 |
| 0.0 | 17.9978 | 4126 | nan | 0.0 | 1.0 |
| 0.0 | 18.9967 | 4355 | nan | 0.0 | 1.0 |
| 0.0 | 20.0 | 4585 | nan | 0.0 | 1.0 |
| 0.0 | 20.9989 | 4814 | nan | 0.0 | 1.0 |
| 0.0 | 21.9978 | 5043 | nan | 0.0 | 1.0 |
| 0.0 | 22.9967 | 5272 | nan | 0.0 | 1.0 |
| 0.0 | 24.0 | 5502 | nan | 0.0 | 1.0 |
| 0.0 | 24.9989 | 5731 | nan | 0.0 | 1.0 |
| 0.0 | 25.9978 | 5960 | nan | 0.0 | 1.0 |
| 0.0 | 26.9967 | 6189 | nan | 0.0 | 1.0 |
| 0.0 | 28.0 | 6419 | nan | 0.0 | 1.0 |
| 0.0 | 28.9989 | 6648 | nan | 0.0 | 1.0 |
| 0.0 | 29.9978 | 6877 | nan | 0.0 | 1.0 |
| 0.0 | 30.9967 | 7106 | nan | 0.0 | 1.0 |
| 0.0 | 32.0 | 7336 | nan | 0.0 | 1.0 |
| 0.0 | 32.9989 | 7565 | nan | 0.0 | 1.0 |
| 0.0 | 33.9978 | 7794 | nan | 0.0 | 1.0 |
| 0.0 | 34.9967 | 8023 | nan | 0.0 | 1.0 |
| 0.0 | 36.0 | 8253 | nan | 0.0 | 1.0 |
| 0.0 | 36.9989 | 8482 | nan | 0.0 | 1.0 |
| 0.0 | 37.9978 | 8711 | nan | 0.0 | 1.0 |
| 0.0 | 38.9967 | 8940 | nan | 0.0 | 1.0 |
| 0.0 | 39.9564 | 9160 | nan | 0.0 | 1.0 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
- Task
- Image Segmentation
- Library
- transformers
- Type
- segformer
- License
- other
- Created
- Jul 26, 2024
- Updated
- Jul 26, 2024