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swin_arocr_tiny
gagan3012/swin_arocr_tiny
swin_arocr_tiny is a image feature extraction model from gagan3012. Use it for the image feature extraction task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image Feature Extraction
- Library
- transformers
- Model type
- swinv2
- Access
- Public
- Created
- Dec 15, 2022
- Updated
- Dec 15, 2022
- SHA
- 2292e376
Datasets
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. -->
swinv2_arocr_tiny_encoder
This model is a fine-tuned version of /lustre07/scratch/gagan30/arocr/models/swinv2_arocr_tiny/config.json on the /lustre07/scratch/gagan30/arocr/Hindawi dataset. It achieves the following results on the evaluation set:
- Loss: 0.0519
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: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0891 | 1.0 | 8078 | 0.0628 |
| 0.0465 | 2.0 | 16156 | 0.0595 |
| 0.0639 | 3.0 | 24234 | 0.0570 |
| 0.0608 | 4.0 | 32312 | 0.0548 |
| 0.0487 | 5.0 | 40390 | 0.0554 |
| 0.059 | 6.0 | 48468 | 0.0533 |
| 0.0677 | 7.0 | 56546 | 0.0525 |
| 0.0555 | 8.0 | 64624 | 0.0521 |
| 0.0502 | 9.0 | 72702 | 0.0520 |
| 0.0496 | 10.0 | 80780 | 0.0519 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.0
- Datasets 2.7.1
- Tokenizers 0.11.6
- Task
- Image Feature Extraction
- Library
- transformers
- Type
- swinv2
- Created
- Dec 15, 2022
- Updated
- Dec 15, 2022