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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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- task: |
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type: image-classification |
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name: Image Classification |
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dataset: |
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type: custom |
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name: custom |
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split: test |
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metrics: |
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- type: f1 |
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value: 0.8580847578266328 |
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name: F1 |
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- type: precision |
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value: 0.8587893412503379 |
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name: Precision |
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- type: recall |
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value: 0.8593508500772797 |
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name: Recall |
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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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