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hchcsuim
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FFPP-Raw_1FPS_faces-expand-40-aligned_metric

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-40-aligned_metric-acc-precision-recall-f1
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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: 0.8064887989445775
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+ - name: Recall
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+ type: recall
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+ value: 0.599275070479259
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+ - name: Precision
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+ type: precision
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+ value: 0.26912642430819317
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+ - name: F1
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+ type: f1
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+ value: 0.37144283574638043
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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-40-aligned_metric-acc-precision-recall-f1
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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: 0.4464
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+ - Accuracy: 0.8065
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+ - Recall: 0.5993
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+ - Precision: 0.2691
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+ - F1: 0.3714
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+ - Roc Auc: 0.8135
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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: 3
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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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+ | 0.1821 | 1.0 | 1348 | 0.1286 | 0.9464 | 0.8533 | 0.8953 | 0.8738 | 0.9858 |
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+ | 0.1333 | 2.0 | 2696 | 0.0715 | 0.9725 | 0.9129 | 0.9586 | 0.9352 | 0.9960 |
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+ | 0.0809 | 3.0 | 4044 | 0.0520 | 0.9804 | 0.9344 | 0.9743 | 0.9539 | 0.9980 |
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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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+ "train_loss": 0.18140344330571173,
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+ "train_runtime": 1283.9356,
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+ "train_samples_per_second": 201.545,
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+ "train_steps_per_second": 3.15
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+ }
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+ {
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+ "hidden_size": 768,
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+ "0": "Fake",
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+ }
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