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jaffe_V2
ricardoSLabs/jaffe_V2
jaffe_V2 is a image classification model from ricardoSLabs. 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
- beit
- Access
- Public
- Created
- Mar 6, 2025
- Updated
- Mar 6, 2025
- SHA
- 6660433d
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. -->
jaffe_V2
This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.9499
- Accuracy: 0.2344
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 1 | 2.1789 | 0.1562 |
| No log | 2.0 | 2 | 2.2850 | 0.1406 |
| No log | 3.0 | 3 | 2.1473 | 0.1562 |
| No log | 4.0 | 4 | 2.0046 | 0.1562 |
| No log | 5.0 | 5 | 1.9499 | 0.2344 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
- Task
- Image Classification
- Library
- transformers
- Type
- beit
- License
- apache-2.0
- Created
- Mar 6, 2025
- Updated
- Mar 6, 2025