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bit-50-finetuned-landscape-test
vintage-lavender619/bit-50-finetuned-landscape-test
bit-50-finetuned-landscape-test is a image classification model from vintage-lavender619. 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
- bit
- Access
- Public
- Created
- Jun 10, 2024
- Updated
- Jun 10, 2024
- SHA
- 8a52b731
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. -->
bit-50-finetuned-landscape-test
This model is a fine-tuned version of google/bit-50 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.1774
- Accuracy: 0.9366
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 252224972.8 | 0.9684 | 23 | 0.4457 | 0.8217 |
| 0.4535 | 1.9789 | 47 | 0.2388 | 0.9168 |
| 0.3491 | 2.9895 | 71 | 0.2022 | 0.9300 |
| 818423.4 | 4.0 | 95 | 0.1818 | 0.9339 |
| 0.2255 | 4.8421 | 115 | 0.1774 | 0.9366 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
- Task
- Image Classification
- Library
- transformers
- Type
- bit
- License
- apache-2.0
- Created
- Jun 10, 2024
- Updated
- Jun 10, 2024