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mobilenet_v2_1.0_224-finetuned-papsmear

Image Classification
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mobilenet_v2
Generated from Trainer
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README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: other
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+ base_model: google/mobilenet_v2_1.0_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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+ model-index:
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+ - name: mobilenet_v2_1.0_224-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: 0.7647058823529411
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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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+ # mobilenet_v2_1.0_224-finetuned-papsmear
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+
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+ This model is a fine-tuned version of [google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobilenet_v2_1.0_224) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6048
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+ - Accuracy: 0.7647
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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: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 32
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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: 60
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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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+ | 1.7932 | 0.9935 | 38 | 1.7607 | 0.25 |
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+ | 1.6542 | 1.9869 | 76 | 1.5736 | 0.3971 |
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+ | 1.4692 | 2.9804 | 114 | 1.4805 | 0.3676 |
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+ | 1.2759 | 4.0 | 153 | 1.2177 | 0.5809 |
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+ | 1.1521 | 4.9935 | 191 | 1.0727 | 0.6471 |
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+ | 1.078 | 5.9869 | 229 | 0.9996 | 0.6176 |
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+ | 1.0235 | 6.9804 | 267 | 0.8680 | 0.7059 |
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+ | 0.9554 | 8.0 | 306 | 0.9273 | 0.6397 |
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+ | 0.7437 | 8.9935 | 344 | 0.7389 | 0.7059 |
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+ | 0.7876 | 9.9869 | 382 | 0.6774 | 0.7426 |
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+ | 0.7698 | 10.9804 | 420 | 0.6569 | 0.7206 |
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+ | 0.7597 | 12.0 | 459 | 0.6758 | 0.7574 |
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+ | 0.6114 | 12.9935 | 497 | 0.8279 | 0.7132 |
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+ | 0.6847 | 13.9869 | 535 | 0.7505 | 0.7132 |
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+ | 0.5902 | 14.9804 | 573 | 0.7919 | 0.6691 |
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+ | 0.629 | 16.0 | 612 | 0.6117 | 0.7868 |
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+ | 0.5071 | 16.9935 | 650 | 0.6048 | 0.7353 |
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+ | 0.5453 | 17.9869 | 688 | 0.8086 | 0.7279 |
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+ | 0.5071 | 18.9804 | 726 | 0.7835 | 0.7059 |
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+ | 0.5328 | 20.0 | 765 | 0.6139 | 0.75 |
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+ | 0.5053 | 20.9935 | 803 | 0.5981 | 0.7868 |
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+ | 0.4436 | 21.9869 | 841 | 0.5219 | 0.8015 |
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+ | 0.5025 | 22.9804 | 879 | 0.4959 | 0.8088 |
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+ | 0.4984 | 24.0 | 918 | 0.5701 | 0.7794 |
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+ | 0.4655 | 24.9935 | 956 | 0.7179 | 0.7206 |
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+ | 0.3848 | 25.9869 | 994 | 0.5075 | 0.8088 |
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+ | 0.3824 | 26.9804 | 1032 | 0.6645 | 0.7426 |
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+ | 0.4901 | 28.0 | 1071 | 0.7288 | 0.6985 |
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+ | 0.397 | 28.9935 | 1109 | 0.7251 | 0.7279 |
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+ | 0.3818 | 29.9869 | 1147 | 0.6250 | 0.7941 |
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+ | 0.3412 | 30.9804 | 1185 | 0.7065 | 0.7279 |
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+ | 0.3627 | 32.0 | 1224 | 0.6877 | 0.7426 |
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+ | 0.3557 | 32.9935 | 1262 | 0.4245 | 0.8529 |
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+ | 0.441 | 33.9869 | 1300 | 0.6974 | 0.75 |
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+ | 0.3036 | 34.9804 | 1338 | 0.6458 | 0.7426 |
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+ | 0.3213 | 36.0 | 1377 | 0.5579 | 0.7941 |
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+ | 0.402 | 36.9935 | 1415 | 0.4578 | 0.8382 |
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+ | 0.2897 | 37.9869 | 1453 | 0.5369 | 0.7868 |
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+ | 0.348 | 38.9804 | 1491 | 0.6819 | 0.7941 |
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+ | 0.3929 | 40.0 | 1530 | 0.5810 | 0.7868 |
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+ | 0.3173 | 40.9935 | 1568 | 0.7875 | 0.7426 |
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+ | 0.3499 | 41.9869 | 1606 | 0.5051 | 0.8015 |
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+ | 0.3053 | 42.9804 | 1644 | 0.7510 | 0.7426 |
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+ | 0.4109 | 44.0 | 1683 | 0.6529 | 0.75 |
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+ | 0.3846 | 44.9935 | 1721 | 0.9615 | 0.7132 |
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+ | 0.3222 | 45.9869 | 1759 | 0.8889 | 0.6691 |
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+ | 0.3293 | 46.9804 | 1797 | 0.4698 | 0.8676 |
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+ | 0.293 | 48.0 | 1836 | 0.5996 | 0.8015 |
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+ | 0.2363 | 48.9935 | 1874 | 0.5007 | 0.8309 |
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+ | 0.2811 | 49.9869 | 1912 | 0.6748 | 0.7941 |
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+ | 0.2403 | 50.9804 | 1950 | 0.6595 | 0.7941 |
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+ | 0.2553 | 52.0 | 1989 | 0.5987 | 0.7794 |
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+ | 0.2959 | 52.9935 | 2027 | 0.5459 | 0.8235 |
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+ | 0.3066 | 53.9869 | 2065 | 0.6198 | 0.7868 |
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+ | 0.2981 | 54.9804 | 2103 | 0.4886 | 0.8309 |
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+ | 0.2658 | 56.0 | 2142 | 0.6422 | 0.7794 |
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+ | 0.2371 | 56.9935 | 2180 | 0.5000 | 0.8382 |
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+ | 0.2331 | 57.9869 | 2218 | 0.8854 | 0.7132 |
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+ | 0.2777 | 58.9804 | 2256 | 0.6190 | 0.8015 |
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+ | 0.3047 | 59.6078 | 2280 | 0.6048 | 0.7647 |
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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 2.4.0
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+ - Datasets 2.21.0
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+ - Tokenizers 0.19.1
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