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100rab25
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bridalMakeupClassifier_binary

Image Classification
Transformers
Safetensors
swin
Generated from Trainer
Eval Results
Inference Endpoints
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README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: microsoft/swin-tiny-patch4-window7-224
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - imagefolder
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+ metrics:
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+ - accuracy
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+ - precision
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+ - recall
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+ - f1
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+ model-index:
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+ - name: bridalMakeupClassifier_binary
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+ results:
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+ - task:
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+ name: Image Classification
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+ type: image-classification
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+ dataset:
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+ name: imagefolder
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+ type: imagefolder
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+ config: default
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+ split: train
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+ args: default
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9969230769230769
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+ - name: Precision
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+ type: precision
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+ value: 0.9888888888888889
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+ - name: Recall
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+ type: recall
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+ value: 1.0
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+ - name: F1
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+ type: f1
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+ value: 0.9944134078212291
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # bridalMakeupClassifier_binary
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+
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+ This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0075
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+ - Accuracy: 0.9969
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+ - Precision: 0.9889
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+ - Recall: 1.0
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+ - F1: 0.9944
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 128
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 20
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 0.2966 | 1.0 | 23 | 0.1290 | 0.9662 | 0.9432 | 0.9326 | 0.9379 |
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+ | 0.1233 | 2.0 | 46 | 0.0407 | 0.9877 | 0.9670 | 0.9888 | 0.9778 |
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+ | 0.0469 | 3.0 | 69 | 0.0594 | 0.9815 | 0.9368 | 1.0 | 0.9674 |
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+ | 0.0394 | 4.0 | 92 | 0.0557 | 0.9877 | 0.9670 | 0.9888 | 0.9778 |
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+ | 0.0909 | 5.0 | 115 | 0.0401 | 0.9908 | 0.9674 | 1.0 | 0.9834 |
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+ | 0.05 | 6.0 | 138 | 0.0252 | 0.9877 | 0.9670 | 0.9888 | 0.9778 |
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+ | 0.0451 | 7.0 | 161 | 0.0279 | 0.9877 | 0.9885 | 0.9663 | 0.9773 |
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+ | 0.0231 | 8.0 | 184 | 0.0278 | 0.9938 | 0.9780 | 1.0 | 0.9889 |
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+ | 0.0404 | 9.0 | 207 | 0.0256 | 0.9877 | 0.9775 | 0.9775 | 0.9775 |
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+ | 0.0297 | 10.0 | 230 | 0.0260 | 0.9908 | 0.9778 | 0.9888 | 0.9832 |
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+ | 0.0327 | 11.0 | 253 | 0.0230 | 0.9938 | 0.9780 | 1.0 | 0.9889 |
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+ | 0.0221 | 12.0 | 276 | 0.0140 | 0.9969 | 0.9889 | 1.0 | 0.9944 |
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+ | 0.0294 | 13.0 | 299 | 0.0106 | 0.9969 | 0.9889 | 1.0 | 0.9944 |
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+ | 0.0292 | 14.0 | 322 | 0.0132 | 0.9969 | 0.9889 | 1.0 | 0.9944 |
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+ | 0.0064 | 15.0 | 345 | 0.0231 | 0.9908 | 0.9674 | 1.0 | 0.9834 |
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+ | 0.02 | 16.0 | 368 | 0.0087 | 0.9969 | 0.9889 | 1.0 | 0.9944 |
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+ | 0.0356 | 17.0 | 391 | 0.0114 | 0.9969 | 0.9889 | 1.0 | 0.9944 |
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+ | 0.0232 | 18.0 | 414 | 0.0072 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0351 | 19.0 | 437 | 0.0087 | 0.9969 | 0.9889 | 1.0 | 0.9944 |
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+ | 0.0155 | 20.0 | 460 | 0.0075 | 0.9969 | 0.9889 | 1.0 | 0.9944 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.45.1
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+ - Pytorch 2.4.1+cu121
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+ - Datasets 2.21.0
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+ - Tokenizers 0.20.0
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