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smids_3x_deit_base_adamax_0001_fold1
hkivancoral/smids_3x_deit_base_adamax_0001_fold1
smids_3x_deit_base_adamax_0001_fold1 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 9, 2023
- Updated
- Dec 9, 2023
- SHA
- 53250328
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_3x_deit_base_adamax_0001_fold1
This model is a fine-tuned version of facebook/deit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.7119
- Accuracy: 0.9098
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: 0.0001
- 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 |
|---|---|---|---|---|
| 0.3221 | 1.0 | 226 | 0.2895 | 0.8915 |
| 0.1579 | 2.0 | 452 | 0.3905 | 0.8664 |
| 0.0813 | 3.0 | 678 | 0.6613 | 0.8397 |
| 0.039 | 4.0 | 904 | 0.5507 | 0.8915 |
| 0.0377 | 5.0 | 1130 | 0.4323 | 0.9015 |
| 0.0016 | 6.0 | 1356 | 0.4901 | 0.9032 |
| 0.0006 | 7.0 | 1582 | 0.4428 | 0.9182 |
| 0.0001 | 8.0 | 1808 | 0.4887 | 0.9165 |
| 0.0056 | 9.0 | 2034 | 0.5309 | 0.9132 |
| 0.0002 | 10.0 | 2260 | 0.5356 | 0.9115 |
| 0.0005 | 11.0 | 2486 | 0.6183 | 0.8998 |
| 0.0 | 12.0 | 2712 | 0.5569 | 0.9032 |
| 0.0218 | 13.0 | 2938 | 0.6429 | 0.9048 |
| 0.0047 | 14.0 | 3164 | 0.6294 | 0.9015 |
| 0.0001 | 15.0 | 3390 | 0.6584 | 0.8982 |
| 0.0 | 16.0 | 3616 | 0.7150 | 0.8915 |
| 0.003 | 17.0 | 3842 | 0.7055 | 0.9032 |
| 0.0 | 18.0 | 4068 | 0.7249 | 0.9032 |
| 0.0033 | 19.0 | 4294 | 0.7504 | 0.8998 |
| 0.0056 | 20.0 | 4520 | 0.7091 | 0.9032 |
| 0.0 | 21.0 | 4746 | 0.7020 | 0.9082 |
| 0.0027 | 22.0 | 4972 | 0.6539 | 0.9082 |
| 0.0024 | 23.0 | 5198 | 0.6895 | 0.9065 |
| 0.0 | 24.0 | 5424 | 0.7110 | 0.9048 |
| 0.0 | 25.0 | 5650 | 0.6678 | 0.9065 |
| 0.0 | 26.0 | 5876 | 0.6869 | 0.9048 |
| 0.0 | 27.0 | 6102 | 0.6975 | 0.9065 |
| 0.0 | 28.0 | 6328 | 0.7125 | 0.9082 |
| 0.0 | 29.0 | 6554 | 0.6594 | 0.9082 |
| 0.0 | 30.0 | 6780 | 0.6721 | 0.9115 |
| 0.0 | 31.0 | 7006 | 0.7233 | 0.9065 |
| 0.0 | 32.0 | 7232 | 0.7025 | 0.9082 |
| 0.0029 | 33.0 | 7458 | 0.7410 | 0.9032 |
| 0.0 | 34.0 | 7684 | 0.6915 | 0.9065 |
| 0.0 | 35.0 | 7910 | 0.6943 | 0.9032 |
| 0.0 | 36.0 | 8136 | 0.6848 | 0.9098 |
| 0.0 | 37.0 | 8362 | 0.6997 | 0.9082 |
| 0.0 | 38.0 | 8588 | 0.6917 | 0.9082 |
| 0.0 | 39.0 | 8814 | 0.7108 | 0.9065 |
| 0.0 | 40.0 | 9040 | 0.6898 | 0.9115 |
| 0.0026 | 41.0 | 9266 | 0.6995 | 0.9082 |
| 0.0027 | 42.0 | 9492 | 0.7042 | 0.9082 |
| 0.0 | 43.0 | 9718 | 0.7153 | 0.9082 |
| 0.0 | 44.0 | 9944 | 0.7075 | 0.9065 |
| 0.0 | 45.0 | 10170 | 0.7108 | 0.9065 |
| 0.0 | 46.0 | 10396 | 0.7067 | 0.9098 |
| 0.0 | 47.0 | 10622 | 0.7091 | 0.9098 |
| 0.0 | 48.0 | 10848 | 0.7104 | 0.9082 |
| 0.0 | 49.0 | 11074 | 0.7114 | 0.9098 |
| 0.0 | 50.0 | 11300 | 0.7119 | 0.9098 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Dec 9, 2023
- Updated
- Dec 9, 2023