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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: finetuned-affecthq |
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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: 0.7179302910528207 |
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- name: Precision |
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type: precision |
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value: 0.7173911115103917 |
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- name: Recall |
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type: recall |
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value: 0.7179302910528207 |
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- name: F1 |
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type: f1 |
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value: 0.7166821507529032 |
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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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# finetuned-affecthq |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8116 |
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- Accuracy: 0.7179 |
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- Precision: 0.7174 |
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- Recall: 0.7179 |
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- F1: 0.7167 |
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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: 1e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 17 |
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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 | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 1.5413 | 1.0 | 174 | 1.4810 | 0.4898 | 0.4867 | 0.4898 | 0.4409 | |
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| 1.0367 | 2.0 | 348 | 1.0571 | 0.6155 | 0.6172 | 0.6155 | 0.6041 | |
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| 0.9534 | 3.0 | 522 | 0.9673 | 0.6475 | 0.6476 | 0.6475 | 0.6375 | |
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| 0.8532 | 4.0 | 696 | 0.9056 | 0.6748 | 0.6710 | 0.6748 | 0.6704 | |
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| 0.8211 | 5.0 | 870 | 0.8707 | 0.6903 | 0.6912 | 0.6903 | 0.6836 | |
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| 0.7797 | 6.0 | 1044 | 0.8472 | 0.7050 | 0.7050 | 0.7050 | 0.7019 | |
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| 0.7816 | 7.0 | 1218 | 0.8298 | 0.7111 | 0.7099 | 0.7111 | 0.7096 | |
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| 0.7135 | 8.0 | 1392 | 0.8186 | 0.7111 | 0.7116 | 0.7111 | 0.7105 | |
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| 0.6697 | 9.0 | 1566 | 0.8143 | 0.7140 | 0.7124 | 0.7140 | 0.7126 | |
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| 0.6765 | 10.0 | 1740 | 0.8116 | 0.7179 | 0.7174 | 0.7179 | 0.7167 | |
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### Framework versions |
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- Transformers 4.27.0.dev0 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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