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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: deit-base-patch16-224-FV-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: 0.848531684698609 |
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- name: Precision |
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type: precision |
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value: 0.8458069264500935 |
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- name: Recall |
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type: recall |
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value: 0.848531684698609 |
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- name: F1 |
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type: f1 |
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value: 0.8463625265241504 |
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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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# deit-base-patch16-224-FV-finetuned-memes |
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This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-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: 0.6769 |
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- Accuracy: 0.8485 |
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- Precision: 0.8458 |
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- Recall: 0.8485 |
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- F1: 0.8464 |
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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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| 1.2733 | 0.99 | 20 | 1.0893 | 0.5811 | 0.5790 | 0.5811 | 0.5293 | |
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| 0.7284 | 1.99 | 40 | 0.7351 | 0.7210 | 0.7642 | 0.7210 | 0.7271 | |
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| 0.4267 | 2.99 | 60 | 0.5202 | 0.7991 | 0.8104 | 0.7991 | 0.8033 | |
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| 0.2181 | 3.99 | 80 | 0.4605 | 0.8346 | 0.8351 | 0.8346 | 0.8334 | |
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| 0.1504 | 4.99 | 100 | 0.5281 | 0.8253 | 0.8281 | 0.8253 | 0.8266 | |
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| 0.1001 | 5.99 | 120 | 0.4945 | 0.8369 | 0.8336 | 0.8369 | 0.8347 | |
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| 0.0874 | 6.99 | 140 | 0.5902 | 0.8338 | 0.8370 | 0.8338 | 0.8348 | |
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| 0.0634 | 7.99 | 160 | 0.6088 | 0.8253 | 0.8221 | 0.8253 | 0.8234 | |
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| 0.0699 | 8.99 | 180 | 0.6210 | 0.8207 | 0.8202 | 0.8207 | 0.8186 | |
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| 0.0661 | 9.99 | 200 | 0.5675 | 0.8385 | 0.8417 | 0.8385 | 0.8393 | |
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| 0.0592 | 10.99 | 220 | 0.6550 | 0.8253 | 0.8324 | 0.8253 | 0.8275 | |
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| 0.0559 | 11.99 | 240 | 0.6400 | 0.8416 | 0.8370 | 0.8416 | 0.8387 | |
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| 0.0501 | 12.99 | 260 | 0.6726 | 0.8393 | 0.8353 | 0.8393 | 0.8350 | |
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| 0.0529 | 13.99 | 280 | 0.6285 | 0.8408 | 0.8399 | 0.8408 | 0.8401 | |
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| 0.0478 | 14.99 | 300 | 0.6423 | 0.8400 | 0.8380 | 0.8400 | 0.8384 | |
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| 0.0458 | 15.99 | 320 | 0.6632 | 0.8369 | 0.8337 | 0.8369 | 0.8348 | |
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| 0.048 | 16.99 | 340 | 0.6719 | 0.8423 | 0.8401 | 0.8423 | 0.8404 | |
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| 0.0417 | 17.99 | 360 | 0.6807 | 0.8423 | 0.8415 | 0.8423 | 0.8408 | |
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| 0.0461 | 18.99 | 380 | 0.6732 | 0.8454 | 0.8440 | 0.8454 | 0.8438 | |
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| 0.044 | 19.99 | 400 | 0.6769 | 0.8485 | 0.8458 | 0.8485 | 0.8464 | |
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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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