paul
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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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model-index:
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- name: mit-b2-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: 0.8523956723338485
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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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# mit-b2-finetuned-memes
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This model is a fine-tuned version of [aaraki/vit-base-patch16-224-in21k-finetuned-cifar10](https://huggingface.co/aaraki/vit-base-patch16-224-in21k-finetuned-cifar10) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4137
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- Accuracy: 0.8524
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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: 32
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- eval_batch_size: 32
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 128
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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: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 0.9727 | 0.99 | 40 | 0.8400 | 0.7334 |
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| 0.5305 | 1.99 | 80 | 0.5147 | 0.8284 |
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| 0.3124 | 2.99 | 120 | 0.4698 | 0.8145 |
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| 0.2263 | 3.99 | 160 | 0.3892 | 0.8563 |
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| 0.1453 | 4.99 | 200 | 0.3874 | 0.8570 |
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| 0.1255 | 5.99 | 240 | 0.4097 | 0.8470 |
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| 0.0989 | 6.99 | 280 | 0.3860 | 0.8570 |
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| 0.0755 | 7.99 | 320 | 0.4141 | 0.8539 |
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| 0.08 | 8.99 | 360 | 0.4049 | 0.8594 |
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| 0.0639 | 9.99 | 400 | 0.4137 | 0.8524 |
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### Framework versions
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- Transformers 4.22.1
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- Pytorch 1.12.1+cu113
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- Datasets 2.4.0
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- Tokenizers 0.12.1
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