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--- |
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license: apache-2.0 |
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base_model: facebook/levit-128 |
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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: levit-128-finetuned-flower |
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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: 0.9506352087114338 |
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- name: Precision |
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type: precision |
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value: 0.950988634564862 |
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- name: Recall |
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type: recall |
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value: 0.9506352087114338 |
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- name: F1 |
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type: f1 |
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value: 0.9505680872971296 |
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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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# levit-128-finetuned-flower |
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This model is a fine-tuned version of [facebook/levit-128](https://huggingface.co/facebook/levit-128) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1807 |
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- Accuracy: 0.9506 |
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- Precision: 0.9510 |
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- Recall: 0.9506 |
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- F1: 0.9506 |
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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: 0.005 |
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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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| 0.6679 | 1.0 | 40 | 0.6957 | 0.8076 | 0.8492 | 0.8076 | 0.8060 | |
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| 0.7188 | 2.0 | 80 | 0.7094 | 0.7822 | 0.7997 | 0.7822 | 0.7789 | |
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| 0.7277 | 3.0 | 120 | 0.7803 | 0.7477 | 0.7912 | 0.7477 | 0.7480 | |
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| 0.561 | 4.0 | 160 | 0.5489 | 0.8352 | 0.8462 | 0.8352 | 0.8292 | |
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| 0.4958 | 5.0 | 200 | 0.4067 | 0.8770 | 0.8852 | 0.8770 | 0.8766 | |
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| 0.4681 | 6.0 | 240 | 0.4801 | 0.8457 | 0.8570 | 0.8457 | 0.8423 | |
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| 0.368 | 7.0 | 280 | 0.4348 | 0.8617 | 0.8697 | 0.8617 | 0.8618 | |
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| 0.355 | 8.0 | 320 | 0.3401 | 0.8926 | 0.8971 | 0.8926 | 0.8924 | |
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| 0.3164 | 9.0 | 360 | 0.3510 | 0.8871 | 0.8935 | 0.8871 | 0.8871 | |
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| 0.2972 | 10.0 | 400 | 0.2877 | 0.9140 | 0.9159 | 0.9140 | 0.9133 | |
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| 0.2639 | 11.0 | 440 | 0.2588 | 0.9245 | 0.9246 | 0.9245 | 0.9233 | |
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| 0.264 | 12.0 | 480 | 0.2811 | 0.9096 | 0.9155 | 0.9096 | 0.9097 | |
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| 0.2082 | 13.0 | 520 | 0.2368 | 0.9238 | 0.9244 | 0.9238 | 0.9225 | |
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| 0.1506 | 14.0 | 560 | 0.2552 | 0.9205 | 0.9244 | 0.9205 | 0.9200 | |
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| 0.179 | 15.0 | 600 | 0.2133 | 0.9401 | 0.9421 | 0.9401 | 0.9399 | |
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| 0.1388 | 16.0 | 640 | 0.2170 | 0.9376 | 0.9388 | 0.9376 | 0.9377 | |
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| 0.116 | 17.0 | 680 | 0.1817 | 0.9466 | 0.9468 | 0.9466 | 0.9464 | |
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| 0.0976 | 18.0 | 720 | 0.1915 | 0.9470 | 0.9477 | 0.9470 | 0.9473 | |
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| 0.0806 | 19.0 | 760 | 0.1876 | 0.9492 | 0.9501 | 0.9492 | 0.9493 | |
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| 0.0911 | 20.0 | 800 | 0.1807 | 0.9506 | 0.9510 | 0.9506 | 0.9506 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.0.1 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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