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--- |
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license: apache-2.0 |
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base_model: facebook/levit-256 |
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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-256-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: 0.9520871143375681 |
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- name: Precision |
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type: precision |
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value: 0.9522871286223231 |
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- name: Recall |
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type: recall |
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value: 0.9520871143375681 |
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- name: F1 |
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type: f1 |
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value: 0.9518251458019376 |
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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-256-finetuned-flower |
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This model is a fine-tuned version of [facebook/levit-256](https://huggingface.co/facebook/levit-256) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1677 |
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- Accuracy: 0.9521 |
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- Precision: 0.9523 |
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- Recall: 0.9521 |
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- F1: 0.9518 |
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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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| 0.599 | 1.0 | 40 | 0.5907 | 0.8207 | 0.8515 | 0.8207 | 0.8219 | |
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| 0.7842 | 2.0 | 80 | 1.4800 | 0.6693 | 0.7271 | 0.6693 | 0.6607 | |
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| 0.7716 | 3.0 | 120 | 0.8614 | 0.7554 | 0.7853 | 0.7554 | 0.7544 | |
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| 0.5976 | 4.0 | 160 | 0.5576 | 0.8243 | 0.8470 | 0.8243 | 0.8260 | |
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| 0.488 | 5.0 | 200 | 0.4656 | 0.8555 | 0.8724 | 0.8555 | 0.8546 | |
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| 0.4871 | 6.0 | 240 | 0.4387 | 0.8672 | 0.8823 | 0.8672 | 0.8672 | |
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| 0.3606 | 7.0 | 280 | 0.3041 | 0.9045 | 0.9053 | 0.9045 | 0.9034 | |
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| 0.3159 | 8.0 | 320 | 0.3283 | 0.8976 | 0.9022 | 0.8976 | 0.8961 | |
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| 0.3078 | 9.0 | 360 | 0.2848 | 0.9125 | 0.9156 | 0.9125 | 0.9124 | |
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| 0.2922 | 10.0 | 400 | 0.2526 | 0.9180 | 0.9212 | 0.9180 | 0.9184 | |
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| 0.2412 | 11.0 | 440 | 0.2367 | 0.9281 | 0.9306 | 0.9281 | 0.9280 | |
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| 0.2095 | 12.0 | 480 | 0.2283 | 0.9314 | 0.9323 | 0.9314 | 0.9305 | |
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| 0.1786 | 13.0 | 520 | 0.1890 | 0.9408 | 0.9412 | 0.9408 | 0.9408 | |
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| 0.123 | 14.0 | 560 | 0.2071 | 0.9383 | 0.9398 | 0.9383 | 0.9382 | |
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| 0.1481 | 15.0 | 600 | 0.1854 | 0.9426 | 0.9433 | 0.9426 | 0.9426 | |
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| 0.125 | 16.0 | 640 | 0.2051 | 0.9376 | 0.9400 | 0.9376 | 0.9373 | |
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| 0.1135 | 17.0 | 680 | 0.1785 | 0.9495 | 0.9496 | 0.9495 | 0.9495 | |
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| 0.0815 | 18.0 | 720 | 0.1655 | 0.9539 | 0.9542 | 0.9539 | 0.9538 | |
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| 0.0784 | 19.0 | 760 | 0.1707 | 0.9525 | 0.9527 | 0.9525 | 0.9521 | |
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| 0.0905 | 20.0 | 800 | 0.1677 | 0.9521 | 0.9523 | 0.9521 | 0.9518 | |
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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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