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--- |
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base_model: juliensimon/autotrain-chest-xray-demo-1677859324 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: Text2Image_PyData_23 |
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results: [] |
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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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# Text2Image_PyData_23 |
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This model is a fine-tuned version of [juliensimon/autotrain-chest-xray-demo-1677859324](https://huggingface.co/juliensimon/autotrain-chest-xray-demo-1677859324) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3421 |
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- Accuracy: 0.8333 |
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- F1: [0.71584699 0.88208617] |
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- Precision: [0.99242424 0.79065041] |
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- Recall: [0.55982906 0.9974359 ] |
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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: 16 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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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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- num_epochs: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------------:|:-----------------------:|:-----------------------:| |
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| 0.0451 | 0.98 | 40 | 0.6974 | 0.7933 | [0.62170088 0.85777288] | [0.99065421 0.75241779] | [0.45299145 0.9974359 ] | |
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| 0.036 | 1.99 | 81 | 0.3557 | 0.8958 | [0.84107579 0.92252682] | [0.98285714 0.86191537] | [0.73504274 0.99230769] | |
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| 0.043 | 2.99 | 122 | 0.4253 | 0.9006 | [0.84803922 0.92619048] | [0.99425287 0.86444444] | [0.73931624 0.9974359 ] | |
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| 0.0225 | 4.0 | 163 | 0.8776 | 0.8349 | [0.71934605 0.8830874 ] | [0.9924812 0.79226069] | [0.56410256 0.9974359 ] | |
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| 0.0153 | 4.98 | 203 | 0.7095 | 0.8670 | [0.78552972 0.90360046] | [0.99346405 0.82590234] | [0.64957265 0.9974359 ] | |
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| 0.0107 | 5.99 | 244 | 0.8537 | 0.8446 | [0.73994638 0.88914286] | [0.99280576 0.80206186] | [0.58974359 0.9974359 ] | |
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| 0.0052 | 6.99 | 285 | 1.0167 | 0.8462 | [0.74331551 0.89016018] | [0.99285714 0.80371901] | [0.59401709 0.9974359 ] | |
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| 0.0049 | 8.0 | 326 | 1.3230 | 0.8045 | [0.64942529 0.86444444] | [0.99122807 0.7627451 ] | [0.48290598 0.9974359 ] | |
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| 0.0061 | 8.98 | 366 | 1.2652 | 0.8269 | [0.70165746 0.87810384] | [0.9921875 0.78427419] | [0.54273504 0.9974359 ] | |
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| 0.004 | 9.99 | 407 | 1.4846 | 0.8157 | [0.67605634 0.8712206 ] | [0.99173554 0.77335984] | [0.51282051 0.9974359 ] | |
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| 0.0005 | 10.99 | 448 | 1.5685 | 0.8109 | [0.66477273 0.86830357] | [0.99152542 0.7687747 ] | [0.5 0.9974359] | |
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| 0.0029 | 12.0 | 489 | 1.2547 | 0.8397 | [0.72972973 0.88610478] | [0.99264706 0.79713115] | [0.57692308 0.9974359 ] | |
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| 0.0015 | 12.98 | 529 | 1.4026 | 0.8285 | [0.70523416 0.87909605] | [0.99224806 0.78585859] | [0.54700855 0.9974359 ] | |
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| 0.0012 | 13.99 | 570 | 1.4444 | 0.8237 | [0.69444444 0.87612613] | [0.99206349 0.7811245 ] | [0.53418803 0.9974359 ] | |
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| 0.0039 | 14.72 | 600 | 1.3421 | 0.8333 | [0.71584699 0.88208617] | [0.99242424 0.79065041] | [0.55982906 0.9974359 ] | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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