Vit 384 Large Patch Tongue Image Segmented
This model is a fine-tuned version of google/vit-large-patch32-384 on an unknown dataset. It achieves the following results on the evaluation set: Loss: 0.6965 Precision: 0.8085 Recall: 0.8837 F1: 0.8444
Model source
- Kind
- Other
- File size
- 1.14 GB
- Base model
- google/vit-large-patch32-384
- Version
- vRevision 3203aca
- Updated
- Oct 7, 2026
- Downloads
- 5
- Source
- Hugging Face
Source description
segmented-augmented
This model is a fine-tuned version of google/vit-large-patch32-384 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6965
- Precision: 0.8085
- Recall: 0.8837
- F1: 0.8444
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 |
|---|---|---|---|---|---|---|
| 0.0857 | 1.0 | 327 | 0.4359 | 0.8176 | 0.8040 | 0.8107 |
| 0.0164 | 2.0 | 654 | 0.5654 | 0.8043 | 0.8605 | 0.8315 |
| 0.0056 | 3.0 | 981 | 0.6437 | 0.8182 | 0.8671 | 0.8419 |
| 0.002 | 4.0 | 1308 | 0.6739 | 0.8055 | 0.8804 | 0.8413 |
| 0.003 | 5.0 | 1635 | 0.6965 | 0.8085 | 0.8837 | 0.8444 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
Sources
1 source
Sources
1 sourceVerified Oct 7
Model artifacts
1 artifact
Model artifacts
1 artifactSource excerpts
3 excerpts
Source excerpts
3 excerpts--- library_name: transformers license: apache-2.0 base_model: google/vit-large-patch32-384 tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: segmented-augmented results: [] --- <!-- 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-large-patch32-384](https://huggingface.co/google/vit-large-patch32-384) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6965 - Precision: 0.8085 - Recall: 0.8837 - F1: 0.8444 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | 0.0857 | 1.0 | 327 | 0.4359 | 0.8176 | 0.8040 | 0.8107 | | 0.0164 | 2.0 | 654 | 0.5654 | 0.8043 | 0.8605 | 0.8315 | | 0.0056 | 3.0 | 981 | 0.6437 | 0.8182 | 0.8671 | 0.8419 | | 0.002 | 4.0 | 1308 | 0.6739 | 0.8055 | 0.8804 | 0.8413 | | 0.003 | 5.0 | 1635 | 0.6965 | 0.8085 | 0.8837 | 0.8444 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.0 - Tokenizers 0.19.1
Source context: 5 downloads · 0 likes · Pipeline image-classification · Library transformers · Repo e1010101/vit-384-large-patch-tongue-image-segmented
Source context: 2 downloads · 0 likes · Pipeline image-classification · Library transformers · Repo e1010101/vit-384-large-patch-tongue-image-segmented
