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my_awesome_food_model
dantepalacio/my_awesome_food_model
my_awesome_food_model is a image classification model from dantepalacio. 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
- swinv2
- Access
- Public
- Created
- Dec 24, 2024
- Updated
- Dec 24, 2024
- SHA
- 9b8638cd
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. -->
my_awesome_food_model
This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window16-256 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.6589
- Accuracy: 0.7124
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 3.2193 | 1.0 | 76 | 0.7354 | 0.6446 |
| 2.7694 | 2.0 | 152 | 0.6906 | 0.6909 |
| 2.9082 | 2.9637 | 225 | 0.6589 | 0.7124 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
- Task
- Image Classification
- Library
- transformers
- Type
- swinv2
- License
- apache-2.0
- Created
- Dec 24, 2024
- Updated
- Dec 24, 2024