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ViT_Flower102_2

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  2. model.safetensors +1 -1
  3. runs/Apr03_09-11-38_66437d7433cf/events.out.tfevents.1712135532.66437d7433cf.8793.3 +2 -2
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_2
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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_2
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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: 0.1533
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+ - Accuracy: 0.9716
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+ - Precision: 0.9716
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+ - Recall: 0.9716
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+ - F1: 0.9716
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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: 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 | Precision | Recall | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 0.053 | 0.22 | 100 | 0.3198 | 0.9235 | 0.9235 | 0.9235 | 0.9235 |
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+ | 0.1225 | 0.45 | 200 | 0.4087 | 0.9167 | 0.9167 | 0.9167 | 0.9167 |
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+ | 0.1985 | 0.67 | 300 | 0.2068 | 0.9569 | 0.9569 | 0.9569 | 0.9569 |
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+ | 0.0804 | 0.89 | 400 | 0.3181 | 0.9333 | 0.9333 | 0.9333 | 0.9333 |
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+ | 0.1672 | 1.11 | 500 | 0.3582 | 0.9275 | 0.9275 | 0.9275 | 0.9275 |
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+ | 0.1287 | 1.34 | 600 | 0.2700 | 0.9451 | 0.9451 | 0.9451 | 0.9451 |
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+ | 0.0147 | 1.56 | 700 | 0.3691 | 0.9206 | 0.9206 | 0.9206 | 0.9206 |
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+ | 0.0416 | 1.78 | 800 | 0.2535 | 0.9471 | 0.9471 | 0.9471 | 0.9471 |
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+ | 0.0211 | 2.0 | 900 | 0.2575 | 0.9471 | 0.9471 | 0.9471 | 0.9471 |
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+ | 0.088 | 2.23 | 1000 | 0.1908 | 0.9529 | 0.9529 | 0.9529 | 0.9529 |
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+ | 0.1849 | 2.45 | 1100 | 0.2201 | 0.9529 | 0.9529 | 0.9529 | 0.9529 |
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+ | 0.0009 | 2.67 | 1200 | 0.2229 | 0.9549 | 0.9549 | 0.9549 | 0.9549 |
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+ | 0.0599 | 2.9 | 1300 | 0.1781 | 0.9608 | 0.9608 | 0.9608 | 0.9608 |
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+ | 0.0004 | 3.12 | 1400 | 0.1751 | 0.9667 | 0.9667 | 0.9667 | 0.9667 |
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+ | 0.0004 | 3.34 | 1500 | 0.1684 | 0.9686 | 0.9686 | 0.9686 | 0.9686 |
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+ | 0.0352 | 3.56 | 1600 | 0.1502 | 0.9755 | 0.9755 | 0.9755 | 0.9755 |
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+ | 0.0003 | 3.79 | 1700 | 0.1597 | 0.9745 | 0.9745 | 0.9745 | 0.9745 |
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+ | 0.0003 | 4.01 | 1800 | 0.2573 | 0.9559 | 0.9559 | 0.9559 | 0.9559 |
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+ | 0.0005 | 4.23 | 1900 | 0.1907 | 0.9667 | 0.9667 | 0.9667 | 0.9667 |
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+ | 0.0741 | 4.45 | 2000 | 0.2038 | 0.9637 | 0.9637 | 0.9637 | 0.9637 |
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+ | 0.0025 | 4.68 | 2100 | 0.1929 | 0.9647 | 0.9647 | 0.9647 | 0.9647 |
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+ | 0.0293 | 4.9 | 2200 | 0.1740 | 0.9608 | 0.9608 | 0.9608 | 0.9608 |
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+ | 0.0003 | 5.12 | 2300 | 0.2598 | 0.9569 | 0.9569 | 0.9569 | 0.9569 |
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+ | 0.0037 | 5.35 | 2400 | 0.1772 | 0.9618 | 0.9618 | 0.9618 | 0.9618 |
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+ | 0.0213 | 5.57 | 2500 | 0.2911 | 0.9520 | 0.9520 | 0.9520 | 0.9520 |
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+ | 0.027 | 5.79 | 2600 | 0.2540 | 0.9520 | 0.9520 | 0.9520 | 0.9520 |
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+ | 0.0155 | 6.01 | 2700 | 0.2252 | 0.9549 | 0.9549 | 0.9549 | 0.9549 |
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+ | 0.0002 | 6.24 | 2800 | 0.3040 | 0.9431 | 0.9431 | 0.9431 | 0.9431 |
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+ | 0.011 | 6.46 | 2900 | 0.1923 | 0.9598 | 0.9598 | 0.9598 | 0.9598 |
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+ | 0.0006 | 6.68 | 3000 | 0.2089 | 0.9637 | 0.9637 | 0.9637 | 0.9637 |
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+ | 0.0002 | 6.9 | 3100 | 0.2206 | 0.9578 | 0.9578 | 0.9578 | 0.9578 |
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+ | 0.0006 | 7.13 | 3200 | 0.2267 | 0.9627 | 0.9627 | 0.9627 | 0.9627 |
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+ | 0.0001 | 7.35 | 3300 | 0.1735 | 0.9637 | 0.9637 | 0.9637 | 0.9637 |
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+ | 0.0001 | 7.57 | 3400 | 0.1611 | 0.9686 | 0.9686 | 0.9686 | 0.9686 |
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+ | 0.0003 | 7.8 | 3500 | 0.1584 | 0.9676 | 0.9676 | 0.9676 | 0.9676 |
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+ | 0.0001 | 8.02 | 3600 | 0.1591 | 0.9716 | 0.9716 | 0.9716 | 0.9716 |
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+ | 0.0005 | 8.24 | 3700 | 0.1596 | 0.9706 | 0.9706 | 0.9706 | 0.9706 |
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+ | 0.0002 | 8.46 | 3800 | 0.1563 | 0.9716 | 0.9716 | 0.9716 | 0.9716 |
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+ | 0.0002 | 8.69 | 3900 | 0.1550 | 0.9716 | 0.9716 | 0.9716 | 0.9716 |
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+ | 0.0001 | 8.91 | 4000 | 0.1542 | 0.9706 | 0.9706 | 0.9706 | 0.9706 |
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+ | 0.0001 | 9.13 | 4100 | 0.1538 | 0.9716 | 0.9716 | 0.9716 | 0.9716 |
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+ | 0.0001 | 9.35 | 4200 | 0.1536 | 0.9716 | 0.9716 | 0.9716 | 0.9716 |
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+ | 0.0001 | 9.58 | 4300 | 0.1534 | 0.9716 | 0.9716 | 0.9716 | 0.9716 |
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+ | 0.0001 | 9.8 | 4400 | 0.1533 | 0.9716 | 0.9716 | 0.9716 | 0.9716 |
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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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