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vit-msn-small-wbc-classifier-mono-V-all
Melo1512/vit-msn-small-wbc-classifier-mono-V-all
vit-msn-small-wbc-classifier-mono-V-all 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
- Dec 12, 2024
- Updated
- Dec 12, 2024
- SHA
- ded46c37
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-wbc-classifier-mono-V-all
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: 0.1140
- Accuracy: 0.9585
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1974 | 1.0 | 208 | 0.1642 | 0.9372 |
| 0.1589 | 2.0 | 416 | 0.1334 | 0.9508 |
| 0.134 | 3.0 | 624 | 0.1466 | 0.9431 |
| 0.1488 | 4.0 | 832 | 0.1155 | 0.9566 |
| 0.1169 | 5.0 | 1040 | 0.1140 | 0.9585 |
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
- Dec 12, 2024
- Updated
- Dec 12, 2024