paul commited on
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update model card README.md
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README.md
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---
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license: apache-2.0
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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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- precision
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- recall
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- f1
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model-index:
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- name: vit-base-patch16-224-FV2-finetuned-memes
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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: 0.8647604327666152
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- name: Precision
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type: precision
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value: 0.865115560305398
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- name: Recall
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type: recall
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value: 0.8647604327666152
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- name: F1
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type: f1
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value: 0.8646314523408155
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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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# vit-base-patch16-224-FV2-finetuned-memes
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5458
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- Accuracy: 0.8648
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- Precision: 0.8651
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- Recall: 0.8648
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- F1: 0.8646
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.00012
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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: 4
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- total_train_batch_size: 256
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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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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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| 0.994 | 0.99 | 20 | 0.7937 | 0.7257 | 0.7148 | 0.7257 | 0.7025 |
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| 0.509 | 1.99 | 40 | 0.4634 | 0.8346 | 0.8461 | 0.8346 | 0.8303 |
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| 0.2698 | 2.99 | 60 | 0.3851 | 0.8594 | 0.8619 | 0.8594 | 0.8586 |
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| 0.1381 | 3.99 | 80 | 0.4186 | 0.8624 | 0.8716 | 0.8624 | 0.8634 |
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| 0.0899 | 4.99 | 100 | 0.4038 | 0.8586 | 0.8624 | 0.8586 | 0.8594 |
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| 0.0708 | 5.99 | 120 | 0.4170 | 0.8563 | 0.8612 | 0.8563 | 0.8580 |
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| 0.0629 | 6.99 | 140 | 0.4414 | 0.8594 | 0.8599 | 0.8594 | 0.8585 |
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| 0.0554 | 7.99 | 160 | 0.4617 | 0.8539 | 0.8563 | 0.8539 | 0.8550 |
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| 0.0582 | 8.99 | 180 | 0.4712 | 0.8648 | 0.8667 | 0.8648 | 0.8651 |
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| 0.0582 | 9.99 | 200 | 0.4753 | 0.8632 | 0.8647 | 0.8632 | 0.8636 |
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| 0.0535 | 10.99 | 220 | 0.4653 | 0.8694 | 0.8690 | 0.8694 | 0.8684 |
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| 0.0516 | 11.99 | 240 | 0.4937 | 0.8679 | 0.8692 | 0.8679 | 0.8681 |
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| 0.0478 | 12.99 | 260 | 0.5109 | 0.8725 | 0.8741 | 0.8725 | 0.8718 |
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| 0.0484 | 13.99 | 280 | 0.5144 | 0.8640 | 0.8660 | 0.8640 | 0.8647 |
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| 0.0472 | 14.99 | 300 | 0.5249 | 0.8679 | 0.8688 | 0.8679 | 0.8678 |
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| 0.043 | 15.99 | 320 | 0.5324 | 0.8709 | 0.8711 | 0.8709 | 0.8704 |
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| 0.0473 | 16.99 | 340 | 0.5352 | 0.8648 | 0.8660 | 0.8648 | 0.8647 |
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| 0.0502 | 17.99 | 360 | 0.5389 | 0.8694 | 0.8692 | 0.8694 | 0.8687 |
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| 0.0489 | 18.99 | 380 | 0.5564 | 0.8648 | 0.8666 | 0.8648 | 0.8651 |
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| 0.04 | 19.99 | 400 | 0.5458 | 0.8648 | 0.8651 | 0.8648 | 0.8646 |
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### Framework versions
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- Transformers 4.24.0.dev0
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- Pytorch 1.11.0+cu102
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- Datasets 2.6.1.dev0
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- Tokenizers 0.13.1
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