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smids_5x_deit_tiny_adamax_00001_fold3
hkivancoral/smids_5x_deit_tiny_adamax_00001_fold3
smids_5x_deit_tiny_adamax_00001_fold3 is a image classification model from hkivancoral. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- vit
- Access
- Public
- Created
- Dec 14, 2023
- Updated
- Dec 18, 2023
- SHA
- 012c0369
Base models
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. -->
smids_5x_deit_tiny_adamax_00001_fold3
This model is a fine-tuned version of facebook/deit-small-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.8422
- Accuracy: 0.8983
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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2997 | 1.0 | 375 | 0.3235 | 0.88 |
| 0.2668 | 2.0 | 750 | 0.2793 | 0.8967 |
| 0.1125 | 3.0 | 1125 | 0.2572 | 0.9117 |
| 0.1056 | 4.0 | 1500 | 0.2703 | 0.9117 |
| 0.1075 | 5.0 | 1875 | 0.3070 | 0.8983 |
| 0.0954 | 6.0 | 2250 | 0.3649 | 0.8917 |
| 0.0479 | 7.0 | 2625 | 0.3675 | 0.91 |
| 0.0335 | 8.0 | 3000 | 0.4528 | 0.905 |
| 0.0032 | 9.0 | 3375 | 0.4970 | 0.9 |
| 0.0009 | 10.0 | 3750 | 0.5488 | 0.9167 |
| 0.0002 | 11.0 | 4125 | 0.5799 | 0.8983 |
| 0.0004 | 12.0 | 4500 | 0.6150 | 0.9067 |
| 0.0001 | 13.0 | 4875 | 0.6403 | 0.9083 |
| 0.0001 | 14.0 | 5250 | 0.6886 | 0.9017 |
| 0.0008 | 15.0 | 5625 | 0.6997 | 0.9083 |
| 0.0 | 16.0 | 6000 | 0.7289 | 0.9067 |
| 0.0 | 17.0 | 6375 | 0.7468 | 0.905 |
| 0.0 | 18.0 | 6750 | 0.7378 | 0.905 |
| 0.0 | 19.0 | 7125 | 0.7534 | 0.9033 |
| 0.0 | 20.0 | 7500 | 0.7571 | 0.9083 |
| 0.0 | 21.0 | 7875 | 0.7624 | 0.9033 |
| 0.0 | 22.0 | 8250 | 0.7704 | 0.9083 |
| 0.0049 | 23.0 | 8625 | 0.8162 | 0.9017 |
| 0.0 | 24.0 | 9000 | 0.7799 | 0.9033 |
| 0.0 | 25.0 | 9375 | 0.8193 | 0.9033 |
| 0.0 | 26.0 | 9750 | 0.7928 | 0.9033 |
| 0.0 | 27.0 | 10125 | 0.7850 | 0.9017 |
| 0.0 | 28.0 | 10500 | 0.8132 | 0.9 |
| 0.0 | 29.0 | 10875 | 0.8205 | 0.8983 |
| 0.0038 | 30.0 | 11250 | 0.8084 | 0.905 |
| 0.0 | 31.0 | 11625 | 0.8179 | 0.9017 |
| 0.0 | 32.0 | 12000 | 0.8194 | 0.8983 |
| 0.0 | 33.0 | 12375 | 0.8163 | 0.9017 |
| 0.0 | 34.0 | 12750 | 0.8152 | 0.9067 |
| 0.0 | 35.0 | 13125 | 0.8374 | 0.8983 |
| 0.0 | 36.0 | 13500 | 0.8315 | 0.8983 |
| 0.0 | 37.0 | 13875 | 0.8335 | 0.8967 |
| 0.0 | 38.0 | 14250 | 0.8285 | 0.8983 |
| 0.0 | 39.0 | 14625 | 0.8274 | 0.9033 |
| 0.0022 | 40.0 | 15000 | 0.8347 | 0.9017 |
| 0.0 | 41.0 | 15375 | 0.8356 | 0.9 |
| 0.0 | 42.0 | 15750 | 0.8391 | 0.9 |
| 0.0 | 43.0 | 16125 | 0.8395 | 0.8983 |
| 0.0 | 44.0 | 16500 | 0.8400 | 0.8983 |
| 0.0 | 45.0 | 16875 | 0.8413 | 0.8983 |
| 0.0 | 46.0 | 17250 | 0.8418 | 0.8983 |
| 0.0 | 47.0 | 17625 | 0.8416 | 0.8983 |
| 0.0 | 48.0 | 18000 | 0.8421 | 0.8983 |
| 0.0 | 49.0 | 18375 | 0.8423 | 0.8983 |
| 0.0 | 50.0 | 18750 | 0.8422 | 0.8983 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Dec 14, 2023
- Updated
- Dec 18, 2023