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ViT_Flower102_4

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Training in progress, step 100

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  1. README.md +87 -0
  2. model.safetensors +1 -1
  3. runs/Apr04_04-38-31_17332283cf54/events.out.tfevents.1712205598.17332283cf54.1249.0 +2 -2
  4. runs/Apr04_04-38-31_17332283cf54/events.out.tfevents.1712207788.17332283cf54.1249.1 +3 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: google/vit-base-patch16-224-in21k
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ - precision
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+ - recall
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+ - f1
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+ model-index:
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+ - name: ViT_Flower102_4
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+ results: []
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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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+ # ViT_Flower102_4
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+
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+ This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0651
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+ - Accuracy: 0.9873
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+ - Precision: 0.9873
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+ - Recall: 0.9873
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+ - F1: 0.9873
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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: 0.0002
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+ - train_batch_size: 16
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+ - eval_batch_size: 8
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+ - seed: 42
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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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+ - num_epochs: 5
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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 | Precision | Recall | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 2.5217 | 0.22 | 100 | 2.8392 | 0.8363 | 0.8363 | 0.8363 | 0.8363 |
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+ | 1.2157 | 0.45 | 200 | 1.5467 | 0.9304 | 0.9304 | 0.9304 | 0.9304 |
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+ | 0.5702 | 0.67 | 300 | 0.7642 | 0.9598 | 0.9598 | 0.9598 | 0.9598 |
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+ | 0.367 | 0.89 | 400 | 0.4966 | 0.9637 | 0.9637 | 0.9637 | 0.9637 |
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+ | 0.1299 | 1.11 | 500 | 0.2458 | 0.9784 | 0.9784 | 0.9784 | 0.9784 |
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+ | 0.1142 | 1.34 | 600 | 0.1678 | 0.9833 | 0.9833 | 0.9833 | 0.9833 |
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+ | 0.043 | 1.56 | 700 | 0.1746 | 0.9706 | 0.9706 | 0.9706 | 0.9706 |
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+ | 0.0683 | 1.78 | 800 | 0.1554 | 0.9745 | 0.9745 | 0.9745 | 0.9745 |
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+ | 0.0364 | 2.0 | 900 | 0.1132 | 0.9843 | 0.9843 | 0.9843 | 0.9843 |
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+ | 0.019 | 2.23 | 1000 | 0.0939 | 0.9843 | 0.9843 | 0.9843 | 0.9843 |
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+ | 0.0535 | 2.45 | 1100 | 0.1033 | 0.9833 | 0.9833 | 0.9833 | 0.9833 |
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+ | 0.0164 | 2.67 | 1200 | 0.0698 | 0.9902 | 0.9902 | 0.9902 | 0.9902 |
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+ | 0.1128 | 2.9 | 1300 | 0.0810 | 0.9853 | 0.9853 | 0.9853 | 0.9853 |
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+ | 0.0127 | 3.12 | 1400 | 0.0725 | 0.9873 | 0.9873 | 0.9873 | 0.9873 |
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+ | 0.0112 | 3.34 | 1500 | 0.0702 | 0.9902 | 0.9902 | 0.9902 | 0.9902 |
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+ | 0.0383 | 3.56 | 1600 | 0.0860 | 0.9843 | 0.9843 | 0.9843 | 0.9843 |
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+ | 0.0091 | 3.79 | 1700 | 0.0750 | 0.9843 | 0.9843 | 0.9843 | 0.9843 |
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+ | 0.0079 | 4.01 | 1800 | 0.0674 | 0.9882 | 0.9882 | 0.9882 | 0.9882 |
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+ | 0.0083 | 4.23 | 1900 | 0.0659 | 0.9873 | 0.9873 | 0.9873 | 0.9873 |
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+ | 0.0078 | 4.45 | 2000 | 0.0652 | 0.9882 | 0.9882 | 0.9882 | 0.9882 |
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+ | 0.0073 | 4.68 | 2100 | 0.0652 | 0.9873 | 0.9873 | 0.9873 | 0.9873 |
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+ | 0.0076 | 4.9 | 2200 | 0.0651 | 0.9873 | 0.9873 | 0.9873 | 0.9873 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.39.3
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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