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st_vit_trained-8epoch-ucf101-subset
Tommidi/st_vit_trained-8epoch-ucf101-subset
st_vit_trained-8epoch-ucf101-subset is a machine learning model from Tommidi. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- Model type
- st_vit
- Access
- Public
- Created
- Feb 7, 2024
- Updated
- Feb 8, 2024
- SHA
- 6cb916b6
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. -->
st_vit_trained-8epoch-ucf101-subset
This model is a fine-tuned version of Tommidi/st_vit_untrained on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0648
- Accuracy: 0.9733
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 296
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6314 | 0.13 | 38 | 0.1264 | 0.9333 |
| 0.3547 | 1.13 | 76 | 0.0077 | 1.0 |
| 0.0189 | 2.13 | 114 | 0.5103 | 0.9333 |
| 0.0611 | 3.13 | 152 | 0.1508 | 0.9333 |
| 0.0027 | 4.13 | 190 | 0.0018 | 1.0 |
| 0.0812 | 5.13 | 228 | 0.0943 | 0.9333 |
| 0.0005 | 6.13 | 266 | 0.0635 | 0.9667 |
| 0.3035 | 7.1 | 296 | 0.0530 | 0.9667 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
- Library
- transformers
- Type
- st_vit
- Created
- Feb 7, 2024
- Updated
- Feb 8, 2024