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FFPP-Raw_1FPS_faces-expand-0-aligned-normalize-image-mean-std

Image Classification
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swin
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README.md ADDED
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+ ---
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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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+ - recall
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+ - precision
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+ - f1
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+ model-index:
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+ - name: FFPP-Raw_1FPS_faces-expand-0-aligned-normalize-image-mean-std
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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: test
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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: 0.99837772836593
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+ - name: Recall
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+ type: recall
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+ value: 0.9926393819168928
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+ - name: Precision
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+ type: precision
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+ value: 0.9998948309407373
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+ - name: F1
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+ type: f1
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+ value: 0.9962538967332931
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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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+ # FFPP-Raw_1FPS_faces-expand-0-aligned-normalize-image-mean-std
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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: 0.0031
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+ - Accuracy: 0.9984
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+ - Recall: 0.9926
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+ - Precision: 0.9999
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+ - F1: 0.9963
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+ - Roc Auc: 1.0000
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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: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 64
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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 | Recall | Precision | F1 | Roc Auc |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
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+ | 0.1087 | 1.0 | 1377 | 0.0767 | 0.9720 | 0.9020 | 0.9667 | 0.9332 | 0.9965 |
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+ | 0.0647 | 2.0 | 2755 | 0.0408 | 0.9847 | 0.9458 | 0.9831 | 0.9641 | 0.9990 |
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+ | 0.0295 | 3.0 | 4132 | 0.0499 | 0.9862 | 0.9947 | 0.9449 | 0.9692 | 0.9995 |
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+ | 0.0537 | 4.0 | 5510 | 0.0201 | 0.9927 | 0.9736 | 0.9928 | 0.9831 | 0.9998 |
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+ | 0.0253 | 5.0 | 6887 | 0.0115 | 0.9958 | 0.9826 | 0.9979 | 0.9902 | 0.9999 |
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+ | 0.0069 | 6.0 | 8265 | 0.0058 | 0.9979 | 0.9928 | 0.9974 | 0.9951 | 1.0000 |
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+ | 0.0226 | 7.0 | 9642 | 0.0113 | 0.9960 | 0.9823 | 0.9994 | 0.9908 | 0.9999 |
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+ | 0.0096 | 8.0 | 11020 | 0.0147 | 0.9957 | 0.9806 | 0.9995 | 0.9900 | 0.9999 |
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+ | 0.0301 | 9.0 | 12397 | 0.0071 | 0.9972 | 0.9878 | 0.9993 | 0.9935 | 1.0000 |
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+ | 0.0133 | 10.0 | 13775 | 0.0055 | 0.9978 | 0.9901 | 0.9998 | 0.9949 | 1.0000 |
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+ | 0.0074 | 11.0 | 15152 | 0.0049 | 0.9980 | 0.9915 | 0.9992 | 0.9953 | 1.0000 |
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+ | 0.0036 | 12.0 | 16530 | 0.0034 | 0.9983 | 0.9935 | 0.9987 | 0.9961 | 1.0000 |
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+ | 0.0039 | 13.0 | 17907 | 0.0037 | 0.9982 | 0.9926 | 0.9993 | 0.9959 | 1.0000 |
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+ | 0.0106 | 14.0 | 19285 | 0.0033 | 0.9983 | 0.9931 | 0.9992 | 0.9961 | 1.0000 |
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+ | 0.0033 | 15.0 | 20662 | 0.0033 | 0.9984 | 0.9926 | 0.9998 | 0.9962 | 1.0000 |
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+ | 0.0214 | 16.0 | 22040 | 0.0032 | 0.9984 | 0.9931 | 0.9994 | 0.9963 | 1.0000 |
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+ | 0.0041 | 17.0 | 23417 | 0.0032 | 0.9984 | 0.9926 | 0.9998 | 0.9962 | 1.0000 |
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+ | 0.0101 | 18.0 | 24795 | 0.0031 | 0.9984 | 0.9926 | 0.9999 | 0.9963 | 1.0000 |
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+ | 0.0023 | 19.0 | 26172 | 0.0031 | 0.9984 | 0.9928 | 0.9997 | 0.9963 | 1.0000 |
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+ | 0.002 | 19.99 | 27540 | 0.0031 | 0.9984 | 0.9926 | 0.9999 | 0.9963 | 1.0000 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.39.2
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+ - Pytorch 2.2.2
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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