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End of training

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README.md ADDED
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+ ---
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+ base_model: nateraw/vit-age-classifier
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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: image_classification
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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: 0.34375
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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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+ # image_classification
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+
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+ This model is a fine-tuned version of [nateraw/vit-age-classifier](https://huggingface.co/nateraw/vit-age-classifier) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.8469
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+ - Accuracy: 0.3438
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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: 1e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - gradient_accumulation_steps: 8
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+ - total_train_batch_size: 512
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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: 0.3
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+ - num_epochs: 10
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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 | 1 | 1.8452 | 0.3125 |
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+ | No log | 1.6 | 2 | 1.8435 | 0.35 |
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+ | No log | 2.4 | 3 | 1.8282 | 0.3688 |
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+ | No log | 4.0 | 5 | 1.8112 | 0.3563 |
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+ | No log | 4.8 | 6 | 1.8180 | 0.3312 |
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+ | No log | 5.6 | 7 | 1.8291 | 0.3375 |
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+ | No log | 6.4 | 8 | 1.8036 | 0.3563 |
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+ | 1.6711 | 8.0 | 10 | 1.8134 | 0.3375 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.42.4
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+ - Pytorch 2.4.0+cu121
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+ - Datasets 2.21.0
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+ - Tokenizers 0.19.1
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