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S5_M1_fold1_deit_42510037
alirzb/S5_M1_fold1_deit_42510037
S5_M1_fold1_deit_42510037 is a image classification model from alirzb. 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
- deit
- Access
- Public
- Created
- Jan 2, 2024
- Updated
- Jan 3, 2024
- SHA
- 3007dee5
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. -->
S5_M1_fold1_deit_42510037
This model is a fine-tuned version of facebook/deit-base-distilled-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0030
- Accuracy: 0.9996
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0003 | 1.0 | 310 | 0.0054 | 0.9990 |
| 0.013 | 2.0 | 620 | 0.0055 | 0.9988 |
| 0.0002 | 3.0 | 930 | 0.0055 | 0.9982 |
| 0.0 | 4.0 | 1241 | 0.0052 | 0.9984 |
| 0.0 | 5.0 | 1550 | 0.0030 | 0.9996 |
Framework versions
- Transformers 4.36.2
- Pytorch 1.11.0+cu102
- Datasets 2.16.0
- Tokenizers 0.15.0
- Task
- Image Classification
- Library
- transformers
- Type
- deit
- License
- apache-2.0
- Created
- Jan 2, 2024
- Updated
- Jan 3, 2024