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vit-base-patch16-224-finetuned-trash-classifications-albumentations
mshidayat66/vit-base-patch16-224-finetuned-trash-classifications-albumentations
vit-base-patch16-224-finetuned-trash-classifications-albumentations is a machine learning model from mshidayat66. Use it for the machine learning task on the model card, and read the license before you ship it in a product. 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
- License
- apache-2.0
- Model type
- vit
- Access
- Public
- Created
- Nov 29, 2024
- Updated
- Nov 30, 2024
- SHA
- e0b8e0b8
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. -->
vit-base-patch16-224-finetuned-trash-classifications-albumentations
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.2783
- Accuracy: 0.9090
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: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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: 0.05
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4924 | 1.0 | 63 | 0.4184 | 0.8437 |
| 0.1551 | 1.99 | 126 | 0.4079 | 0.8714 |
| 0.04 | 2.99 | 189 | 0.2783 | 0.9090 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.5.1+cu124
- Datasets 2.19.1
- Tokenizers 0.13.3
- Type
- vit
- License
- apache-2.0
- Created
- Nov 29, 2024
- Updated
- Nov 30, 2024