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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224 |
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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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model-index: |
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- name: vit-base-patch16-224 |
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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: validation |
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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: 0.79 |
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- name: Precision |
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type: precision |
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value: 0.7955164222268126 |
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- name: Recall |
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type: recall |
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value: 0.79 |
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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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# vit-base-patch16-224 |
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co./google/vit-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: 0.6740 |
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- Accuracy: 0.79 |
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- Precision: 0.7955 |
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- Recall: 0.79 |
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- F1 Score: 0.7923 |
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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: 5e-05 |
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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: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:| |
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| No log | 1.0 | 4 | 0.5895 | 0.725 | 0.5256 | 0.725 | 0.6094 | |
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| No log | 2.0 | 8 | 0.5737 | 0.725 | 0.5256 | 0.725 | 0.6094 | |
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| No log | 3.0 | 12 | 0.5746 | 0.7333 | 0.6978 | 0.7333 | 0.6589 | |
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| No log | 4.0 | 16 | 0.5449 | 0.7292 | 0.7126 | 0.7292 | 0.6263 | |
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| No log | 5.0 | 20 | 0.5943 | 0.7208 | 0.7362 | 0.7208 | 0.7270 | |
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| No log | 6.0 | 24 | 0.5124 | 0.75 | 0.7360 | 0.75 | 0.6895 | |
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| No log | 7.0 | 28 | 0.6057 | 0.6625 | 0.7301 | 0.6625 | 0.6797 | |
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| No log | 8.0 | 32 | 0.5059 | 0.7583 | 0.7376 | 0.7583 | 0.7214 | |
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| No log | 9.0 | 36 | 0.5734 | 0.7125 | 0.7474 | 0.7125 | 0.7237 | |
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| No log | 10.0 | 40 | 0.5069 | 0.7458 | 0.7182 | 0.7458 | 0.7116 | |
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| No log | 11.0 | 44 | 0.5135 | 0.775 | 0.7659 | 0.775 | 0.7689 | |
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| No log | 12.0 | 48 | 0.4943 | 0.775 | 0.7601 | 0.775 | 0.7610 | |
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| 0.5275 | 13.0 | 52 | 0.5654 | 0.7458 | 0.7790 | 0.7458 | 0.7557 | |
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| 0.5275 | 14.0 | 56 | 0.5257 | 0.7625 | 0.7636 | 0.7625 | 0.7631 | |
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| 0.5275 | 15.0 | 60 | 0.5107 | 0.7875 | 0.7813 | 0.7875 | 0.7836 | |
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| 0.5275 | 16.0 | 64 | 0.5514 | 0.7333 | 0.7655 | 0.7333 | 0.7434 | |
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| 0.5275 | 17.0 | 68 | 0.5004 | 0.7833 | 0.7698 | 0.7833 | 0.7699 | |
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| 0.5275 | 18.0 | 72 | 0.5999 | 0.7125 | 0.7738 | 0.7125 | 0.7269 | |
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| 0.5275 | 19.0 | 76 | 0.4975 | 0.7667 | 0.7554 | 0.7667 | 0.7589 | |
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| 0.5275 | 20.0 | 80 | 0.5120 | 0.7917 | 0.7981 | 0.7917 | 0.7944 | |
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| 0.5275 | 21.0 | 84 | 0.5203 | 0.7833 | 0.7876 | 0.7833 | 0.7853 | |
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| 0.5275 | 22.0 | 88 | 0.5304 | 0.8042 | 0.8051 | 0.8042 | 0.8046 | |
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| 0.5275 | 23.0 | 92 | 0.5475 | 0.825 | 0.825 | 0.825 | 0.8250 | |
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| 0.5275 | 24.0 | 96 | 0.5757 | 0.7458 | 0.7661 | 0.7458 | 0.7531 | |
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| 0.2422 | 25.0 | 100 | 0.5669 | 0.7875 | 0.7829 | 0.7875 | 0.7848 | |
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| 0.2422 | 26.0 | 104 | 0.5489 | 0.7958 | 0.7931 | 0.7958 | 0.7943 | |
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| 0.2422 | 27.0 | 108 | 0.5372 | 0.8 | 0.7982 | 0.8 | 0.7990 | |
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| 0.2422 | 28.0 | 112 | 0.5500 | 0.8208 | 0.8160 | 0.8208 | 0.8176 | |
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| 0.2422 | 29.0 | 116 | 0.5682 | 0.8042 | 0.8033 | 0.8042 | 0.8037 | |
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| 0.2422 | 30.0 | 120 | 0.5899 | 0.8083 | 0.8050 | 0.8083 | 0.8064 | |
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| 0.2422 | 31.0 | 124 | 0.6217 | 0.8 | 0.8063 | 0.8 | 0.8026 | |
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| 0.2422 | 32.0 | 128 | 0.6063 | 0.8125 | 0.8053 | 0.8125 | 0.8068 | |
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| 0.2422 | 33.0 | 132 | 0.5843 | 0.8042 | 0.8033 | 0.8042 | 0.8037 | |
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| 0.2422 | 34.0 | 136 | 0.6020 | 0.8125 | 0.8073 | 0.8125 | 0.8091 | |
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| 0.2422 | 35.0 | 140 | 0.6180 | 0.8042 | 0.8092 | 0.8042 | 0.8063 | |
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| 0.2422 | 36.0 | 144 | 0.6287 | 0.8208 | 0.8171 | 0.8208 | 0.8186 | |
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| 0.2422 | 37.0 | 148 | 0.6231 | 0.825 | 0.8234 | 0.825 | 0.8242 | |
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| 0.0631 | 38.0 | 152 | 0.6260 | 0.8292 | 0.8300 | 0.8292 | 0.8296 | |
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| 0.0631 | 39.0 | 156 | 0.6278 | 0.8333 | 0.8294 | 0.8333 | 0.8308 | |
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| 0.0631 | 40.0 | 160 | 0.6325 | 0.8208 | 0.8200 | 0.8208 | 0.8204 | |
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| 0.0631 | 41.0 | 164 | 0.6370 | 0.8083 | 0.8013 | 0.8083 | 0.8032 | |
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| 0.0631 | 42.0 | 168 | 0.6371 | 0.8125 | 0.8100 | 0.8125 | 0.8111 | |
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| 0.0631 | 43.0 | 172 | 0.6404 | 0.8042 | 0.8016 | 0.8042 | 0.8027 | |
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| 0.0631 | 44.0 | 176 | 0.6640 | 0.8292 | 0.8227 | 0.8292 | 0.8229 | |
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| 0.0631 | 45.0 | 180 | 0.6636 | 0.8208 | 0.8185 | 0.8208 | 0.8195 | |
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| 0.0631 | 46.0 | 184 | 0.6826 | 0.8083 | 0.8122 | 0.8083 | 0.8100 | |
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| 0.0631 | 47.0 | 188 | 0.6756 | 0.8208 | 0.8185 | 0.8208 | 0.8195 | |
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| 0.0631 | 48.0 | 192 | 0.6695 | 0.8292 | 0.8246 | 0.8292 | 0.8261 | |
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| 0.0631 | 49.0 | 196 | 0.6669 | 0.825 | 0.8198 | 0.825 | 0.8213 | |
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| 0.0264 | 50.0 | 200 | 0.6658 | 0.825 | 0.8198 | 0.825 | 0.8213 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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