Downloads · 30 days
4
1% of all-time downloads
vit-base-augmented-ph2-patch-32
ahishamm/vit-base-augmented-ph2-patch-32
vit-base-augmented-ph2-patch-32 is a image classification model from ahishamm. 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. --
All-time downloads
463
Public
Repo size
4.2 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin350 MB · 100%
At a glance
- Task
- Image Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- vit
- Access
- Public
- Created
- Jun 29, 2023
- Updated
- Jun 29, 2023
- SHA
- 03d7a159
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. -->
vit-base-augmented-ph2-patch-32
This model is a fine-tuned version of google/vit-base-patch32-224-in21k on the ahishamm/Augmented_PH2_db_sharpened dataset. It achieves the following results on the evaluation set:
- Loss: 0.3904
- Accuracy: 0.8684
- Recall: 0.8684
- F1: 0.8684
- Precision: 0.8684
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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision |
|---|---|---|---|---|---|---|---|
| 0.1087 | 0.36 | 50 | 0.3904 | 0.8684 | 0.8684 | 0.8684 | 0.8684 |
| 0.066 | 0.72 | 100 | 0.7073 | 0.8274 | 0.8274 | 0.8274 | 0.8274 |
| 0.0092 | 1.09 | 150 | 0.6635 | 0.8154 | 0.8154 | 0.8154 | 0.8154 |
| 0.0716 | 1.45 | 200 | 0.7824 | 0.8342 | 0.8342 | 0.8342 | 0.8342 |
| 0.0056 | 1.81 | 250 | 0.5071 | 0.8957 | 0.8957 | 0.8957 | 0.8957 |
| 0.0023 | 2.17 | 300 | 0.5978 | 0.8855 | 0.8855 | 0.8855 | 0.8855 |
| 0.0019 | 2.54 | 350 | 0.6143 | 0.8855 | 0.8855 | 0.8855 | 0.8855 |
| 0.0016 | 2.9 | 400 | 0.6227 | 0.8889 | 0.8889 | 0.8889 | 0.8889 |
| 0.0015 | 3.26 | 450 | 0.6294 | 0.8889 | 0.8889 | 0.8889 | 0.8889 |
| 0.0014 | 3.62 | 500 | 0.6338 | 0.8889 | 0.8889 | 0.8889 | 0.8889 |
| 0.0014 | 3.99 | 550 | 0.6351 | 0.8889 | 0.8889 | 0.8889 | 0.8889 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Jun 29, 2023
- Updated
- Jun 29, 2023