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vit-384-tongue-image-segmented-augmented
e1010101/vit-384-tongue-image-segmented-augmented
vit-384-tongue-image-segmented-augmented is a image classification model from e1010101. 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
- Nov 7, 2024
- Updated
- Nov 8, 2024
- SHA
- ce2bc43c
Base models
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. -->
segmented-augmented
This model is a fine-tuned version of google/vit-base-patch16-384 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5815
- Precision: 0.8308
- Recall: 0.9136
- F1: 0.8703
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|---|---|---|---|---|---|---|
| 0.081 | 1.0 | 327 | 0.3693 | 0.8464 | 0.8970 | 0.8710 |
| 0.0151 | 2.0 | 654 | 0.4906 | 0.8171 | 0.8904 | 0.8521 |
| 0.0066 | 3.0 | 981 | 0.5194 | 0.8416 | 0.9003 | 0.8700 |
| 0.0029 | 4.0 | 1308 | 0.5671 | 0.8308 | 0.9136 | 0.8703 |
| 0.0026 | 5.0 | 1635 | 0.5815 | 0.8308 | 0.9136 | 0.8703 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Nov 7, 2024
- Updated
- Nov 8, 2024