Downloads · 30 days
10
25% of all-time downloads
segcrack9k_conglomerate_segformer_aug
varcoder/segcrack9k_conglomerate_segformer_aug
segcrack9k_conglomerate_segformer_aug is a machine learning model from varcoder. 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. --
All-time downloads
40
Public
Repo size
2.7 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin339 MB · 100%
At a glance
- Library
- transformers
- License
- other
- Model type
- segformer
- Access
- Public
- Created
- Aug 7, 2023
- Updated
- Aug 7, 2023
- SHA
- 243b46fe
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. -->
segcrack9k_conglomerate_segformer_aug
This model is a fine-tuned version of nvidia/mit-b5 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0362
- Mean Iou: 0.3412
- Mean Accuracy: 0.6823
- Overall Accuracy: 0.6823
- Accuracy Background: nan
- Accuracy Crack: 0.6823
- Iou Background: 0.0
- Iou Crack: 0.6823
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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Crack | Iou Background | Iou Crack |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0323 | 0.14 | 1000 | 0.0445 | 0.3573 | 0.7146 | 0.7146 | nan | 0.7146 | 0.0 | 0.7146 |
| 0.0222 | 0.27 | 2000 | 0.0394 | 0.3591 | 0.7181 | 0.7181 | nan | 0.7181 | 0.0 | 0.7181 |
| 0.0335 | 0.41 | 3000 | 0.0404 | 0.2907 | 0.5813 | 0.5813 | nan | 0.5813 | 0.0 | 0.5813 |
| 0.013 | 0.54 | 4000 | 0.0384 | 0.3244 | 0.6489 | 0.6489 | nan | 0.6489 | 0.0 | 0.6489 |
| 0.0159 | 0.68 | 5000 | 0.0382 | 0.3088 | 0.6176 | 0.6176 | nan | 0.6176 | 0.0 | 0.6176 |
| 0.0608 | 0.81 | 6000 | 0.0366 | 0.3251 | 0.6502 | 0.6502 | nan | 0.6502 | 0.0 | 0.6502 |
| 0.1738 | 0.95 | 7000 | 0.0362 | 0.3412 | 0.6823 | 0.6823 | nan | 0.6823 | 0.0 | 0.6823 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
- Library
- transformers
- Type
- segformer
- License
- other
- Created
- Aug 7, 2023
- Updated
- Aug 7, 2023