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resnet-18-finetuned-papsmear

Image Classification
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resnet
Generated from Trainer
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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/resnet-18
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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: resnet-18-finetuned-papsmear
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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: 0.8897058823529411
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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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+ # resnet-18-finetuned-papsmear
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+
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+ This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2861
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+ - Accuracy: 0.8897
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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 | 0.9231 | 9 | 1.9256 | 0.1691 |
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+ | 1.9692 | 1.9487 | 19 | 1.6557 | 0.2868 |
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+ | 1.7979 | 2.9744 | 29 | 1.3300 | 0.5368 |
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+ | 1.5079 | 4.0 | 39 | 1.0482 | 0.6324 |
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+ | 1.217 | 4.9231 | 48 | 0.9019 | 0.6618 |
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+ | 0.9536 | 5.9487 | 58 | 0.7687 | 0.6691 |
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+ | 0.7881 | 6.9744 | 68 | 0.6150 | 0.7721 |
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+ | 0.68 | 8.0 | 78 | 0.5481 | 0.7868 |
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+ | 0.5678 | 8.9231 | 87 | 0.5341 | 0.7868 |
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+ | 0.5169 | 9.9487 | 97 | 0.4800 | 0.7941 |
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+ | 0.4838 | 10.9744 | 107 | 0.4356 | 0.8235 |
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+ | 0.4738 | 12.0 | 117 | 0.4573 | 0.8162 |
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+ | 0.3798 | 12.9231 | 126 | 0.4263 | 0.8088 |
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+ | 0.3431 | 13.9487 | 136 | 0.4159 | 0.8382 |
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+ | 0.3282 | 14.9744 | 146 | 0.3787 | 0.8603 |
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+ | 0.3167 | 16.0 | 156 | 0.4234 | 0.8382 |
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+ | 0.3186 | 16.9231 | 165 | 0.3853 | 0.8235 |
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+ | 0.2568 | 17.9487 | 175 | 0.3904 | 0.8456 |
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+ | 0.2528 | 18.9744 | 185 | 0.4013 | 0.8309 |
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+ | 0.2661 | 20.0 | 195 | 0.3275 | 0.8824 |
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+ | 0.2287 | 20.9231 | 204 | 0.3219 | 0.8824 |
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+ | 0.2465 | 21.9487 | 214 | 0.3410 | 0.8529 |
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+ | 0.2422 | 22.9744 | 224 | 0.3256 | 0.8603 |
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+ | 0.222 | 24.0 | 234 | 0.3232 | 0.875 |
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+ | 0.1917 | 24.9231 | 243 | 0.3307 | 0.8676 |
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+ | 0.194 | 25.9487 | 253 | 0.3146 | 0.8971 |
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+ | 0.212 | 26.9744 | 263 | 0.3125 | 0.8897 |
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+ | 0.1718 | 28.0 | 273 | 0.3015 | 0.9044 |
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+ | 0.1975 | 28.9231 | 282 | 0.3195 | 0.8824 |
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+ | 0.1948 | 29.9487 | 292 | 0.3536 | 0.8971 |
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+ | 0.1809 | 30.9744 | 302 | 0.3105 | 0.875 |
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+ | 0.1744 | 32.0 | 312 | 0.3032 | 0.8824 |
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+ | 0.1731 | 32.9231 | 321 | 0.2936 | 0.8971 |
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+ | 0.1513 | 33.9487 | 331 | 0.2889 | 0.8824 |
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+ | 0.1527 | 34.9744 | 341 | 0.2875 | 0.8897 |
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+ | 0.1693 | 36.0 | 351 | 0.2754 | 0.8897 |
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+ | 0.1743 | 36.9231 | 360 | 0.2875 | 0.8971 |
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+ | 0.1463 | 37.9487 | 370 | 0.2961 | 0.8971 |
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+ | 0.1429 | 38.9744 | 380 | 0.2848 | 0.8971 |
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+ | 0.1483 | 40.0 | 390 | 0.2873 | 0.8897 |
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+ | 0.1483 | 40.9231 | 399 | 0.2856 | 0.875 |
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+ | 0.1613 | 41.9487 | 409 | 0.2801 | 0.8971 |
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+ | 0.1358 | 42.9744 | 419 | 0.2838 | 0.9118 |
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+ | 0.1453 | 44.0 | 429 | 0.2783 | 0.8971 |
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+ | 0.1383 | 44.9231 | 438 | 0.2897 | 0.8897 |
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+ | 0.1655 | 45.9487 | 448 | 0.2847 | 0.9044 |
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+ | 0.1489 | 46.1538 | 450 | 0.2861 | 0.8897 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.44.2
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+ - Pytorch 2.4.1+cu121
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+ - Datasets 3.0.1
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+ - Tokenizers 0.19.1
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