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vit-msn-small-corect_cleaned_dataset_lateral_flow_ivalidation
Melo1512/vit-msn-small-corect_cleaned_dataset_lateral_flow_ivalidation
vit-msn-small-corect_cleaned_dataset_lateral_flow_ivalidation is a image classification model from Melo1512. 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
- vit_msn
- Access
- Public
- Created
- Jan 14, 2025
- Updated
- Jan 14, 2025
- SHA
- a4f259d1
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-msn-small-corect_cleaned_dataset_lateral_flow_ivalidation
This model is a fine-tuned version of facebook/vit-msn-small on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.2318
- Accuracy: 0.9231
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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.9231 | 3 | 0.6468 | 0.5604 |
| No log | 1.8462 | 6 | 0.4227 | 0.8462 |
| No log | 2.7692 | 9 | 0.3390 | 0.8608 |
| 0.5336 | 4.0 | 13 | 0.3115 | 0.8864 |
| 0.5336 | 4.9231 | 16 | 0.2986 | 0.8938 |
| 0.5336 | 5.8462 | 19 | 0.2318 | 0.9231 |
| 0.3565 | 6.7692 | 22 | 0.2767 | 0.9121 |
| 0.3565 | 8.0 | 26 | 0.2490 | 0.9084 |
| 0.3565 | 8.9231 | 29 | 0.3151 | 0.8938 |
| 0.3166 | 9.8462 | 32 | 0.2404 | 0.9231 |
| 0.3166 | 10.7692 | 35 | 0.2520 | 0.9158 |
| 0.3166 | 12.0 | 39 | 0.2515 | 0.9048 |
| 0.2657 | 12.9231 | 42 | 0.2344 | 0.9121 |
| 0.2657 | 13.8462 | 45 | 0.2187 | 0.9194 |
| 0.2657 | 14.7692 | 48 | 0.2289 | 0.9194 |
| 0.259 | 16.0 | 52 | 0.2251 | 0.9194 |
| 0.259 | 16.9231 | 55 | 0.2238 | 0.9231 |
| 0.259 | 17.8462 | 58 | 0.2312 | 0.9121 |
| 0.2514 | 18.4615 | 60 | 0.2305 | 0.9084 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1
- Task
- Image Classification
- Library
- transformers
- Type
- vit_msn
- License
- apache-2.0
- Created
- Jan 14, 2025
- Updated
- Jan 14, 2025