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smoker_cls_base_MS_V1
ccclllwww/smoker_cls_base_MS_V1
smoker_cls_base_MS_V1 is a image classification model from ccclllwww. 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
- resnet
- Access
- Public
- Created
- May 18, 2025
- Updated
- May 18, 2025
- SHA
- 0e11eb13
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. -->
smoker_cls_base_MS_V1
This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.6667
- Accuracy: 0.7624
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6939 | 1.0 | 15 | 0.6830 | 0.6436 |
| 0.633 | 2.0 | 30 | 0.6732 | 0.7030 |
| 0.6766 | 3.0 | 45 | 0.6678 | 0.7030 |
| 0.6208 | 4.0 | 60 | 0.6704 | 0.7129 |
| 0.6698 | 4.7018 | 70 | 0.6696 | 0.6733 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
- Task
- Image Classification
- Library
- transformers
- Type
- resnet
- License
- apache-2.0
- Created
- May 18, 2025
- Updated
- May 18, 2025