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Dhyey8
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swin-tiny-patch4-window7-224-finetuned-teeth_dataset-finetuned-teeth_dataset-V2

Image Classification
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swin
Generated from Trainer
Eval Results
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  2. model.safetensors +1 -1
  3. runs/Apr10_05-09-05_baa961a2407f/events.out.tfevents.1712725765.baa961a2407f.812.0 +2 -2
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: Dhyey8/swin-tiny-patch4-window7-224-finetuned-teeth_dataset
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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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+ model-index:
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+ - name: swin-tiny-patch4-window7-224-finetuned-teeth_dataset-finetuned-teeth_dataset-V2
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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: 0.9173913043478261
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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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+ # swin-tiny-patch4-window7-224-finetuned-teeth_dataset-finetuned-teeth_dataset-V2
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+
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+ This model is a fine-tuned version of [Dhyey8/swin-tiny-patch4-window7-224-finetuned-teeth_dataset](https://huggingface.co/Dhyey8/swin-tiny-patch4-window7-224-finetuned-teeth_dataset) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3191
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+ - Accuracy: 0.9174
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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: 50
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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 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 0.8 | 3 | 1.1267 | 0.8391 |
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+ | No log | 1.87 | 7 | 1.0719 | 0.8304 |
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+ | 0.5709 | 2.93 | 11 | 0.9447 | 0.8478 |
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+ | 0.5709 | 4.0 | 15 | 0.8442 | 0.8652 |
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+ | 0.5709 | 4.8 | 18 | 0.7065 | 0.8826 |
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+ | 0.3317 | 5.87 | 22 | 0.6930 | 0.8891 |
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+ | 0.3317 | 6.93 | 26 | 0.5630 | 0.8978 |
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+ | 0.1576 | 8.0 | 30 | 0.5882 | 0.8826 |
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+ | 0.1576 | 8.8 | 33 | 0.5198 | 0.9087 |
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+ | 0.1576 | 9.87 | 37 | 0.4425 | 0.9043 |
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+ | 0.0883 | 10.93 | 41 | 0.4727 | 0.8978 |
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+ | 0.0883 | 12.0 | 45 | 0.4314 | 0.9022 |
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+ | 0.0883 | 12.8 | 48 | 0.4011 | 0.9022 |
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+ | 0.051 | 13.87 | 52 | 0.4045 | 0.9174 |
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+ | 0.051 | 14.93 | 56 | 0.3745 | 0.9109 |
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+ | 0.0415 | 16.0 | 60 | 0.3597 | 0.9152 |
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+ | 0.0415 | 16.8 | 63 | 0.4016 | 0.9065 |
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+ | 0.0415 | 17.87 | 67 | 0.3804 | 0.9152 |
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+ | 0.0307 | 18.93 | 71 | 0.3519 | 0.9217 |
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+ | 0.0307 | 20.0 | 75 | 0.4131 | 0.8935 |
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+ | 0.0307 | 20.8 | 78 | 0.4047 | 0.9 |
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+ | 0.0262 | 21.87 | 82 | 0.3450 | 0.9174 |
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+ | 0.0262 | 22.93 | 86 | 0.3639 | 0.9109 |
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+ | 0.0208 | 24.0 | 90 | 0.3843 | 0.9043 |
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+ | 0.0208 | 24.8 | 93 | 0.3797 | 0.8978 |
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+ | 0.0208 | 25.87 | 97 | 0.3660 | 0.9152 |
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+ | 0.0141 | 26.93 | 101 | 0.3445 | 0.9152 |
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+ | 0.0141 | 28.0 | 105 | 0.3131 | 0.9239 |
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+ | 0.0141 | 28.8 | 108 | 0.3069 | 0.9196 |
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+ | 0.0114 | 29.87 | 112 | 0.3006 | 0.9196 |
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+ | 0.0114 | 30.93 | 116 | 0.3097 | 0.9239 |
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+ | 0.014 | 32.0 | 120 | 0.3121 | 0.9174 |
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+ | 0.014 | 32.8 | 123 | 0.3242 | 0.9174 |
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+ | 0.014 | 33.87 | 127 | 0.3291 | 0.9217 |
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+ | 0.016 | 34.93 | 131 | 0.3156 | 0.9217 |
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+ | 0.016 | 36.0 | 135 | 0.3081 | 0.9261 |
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+ | 0.016 | 36.8 | 138 | 0.3084 | 0.9261 |
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+ | 0.0114 | 37.87 | 142 | 0.3148 | 0.9196 |
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+ | 0.0114 | 38.93 | 146 | 0.3191 | 0.9174 |
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+ | 0.0091 | 40.0 | 150 | 0.3191 | 0.9174 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.38.2
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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