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Text2Image_PyData_23

Image Classification
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PyTorch
swin
Generated from Trainer
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Text2Image_PyData_23 / README.md
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metadata
base_model: juliensimon/autotrain-chest-xray-demo-1677859324
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: Text2Image_PyData_23
    results: []

Text2Image_PyData_23

This model is a fine-tuned version of juliensimon/autotrain-chest-xray-demo-1677859324 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.3421
  • Accuracy: 0.8333
  • F1: [0.71584699 0.88208617]
  • Precision: [0.99242424 0.79065041]
  • Recall: [0.55982906 0.9974359 ]

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 16
  • eval_batch_size: 64
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 15

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
0.0451 0.98 40 0.6974 0.7933 [0.62170088 0.85777288] [0.99065421 0.75241779] [0.45299145 0.9974359 ]
0.036 1.99 81 0.3557 0.8958 [0.84107579 0.92252682] [0.98285714 0.86191537] [0.73504274 0.99230769]
0.043 2.99 122 0.4253 0.9006 [0.84803922 0.92619048] [0.99425287 0.86444444] [0.73931624 0.9974359 ]
0.0225 4.0 163 0.8776 0.8349 [0.71934605 0.8830874 ] [0.9924812 0.79226069] [0.56410256 0.9974359 ]
0.0153 4.98 203 0.7095 0.8670 [0.78552972 0.90360046] [0.99346405 0.82590234] [0.64957265 0.9974359 ]
0.0107 5.99 244 0.8537 0.8446 [0.73994638 0.88914286] [0.99280576 0.80206186] [0.58974359 0.9974359 ]
0.0052 6.99 285 1.0167 0.8462 [0.74331551 0.89016018] [0.99285714 0.80371901] [0.59401709 0.9974359 ]
0.0049 8.0 326 1.3230 0.8045 [0.64942529 0.86444444] [0.99122807 0.7627451 ] [0.48290598 0.9974359 ]
0.0061 8.98 366 1.2652 0.8269 [0.70165746 0.87810384] [0.9921875 0.78427419] [0.54273504 0.9974359 ]
0.004 9.99 407 1.4846 0.8157 [0.67605634 0.8712206 ] [0.99173554 0.77335984] [0.51282051 0.9974359 ]
0.0005 10.99 448 1.5685 0.8109 [0.66477273 0.86830357] [0.99152542 0.7687747 ] [0.5 0.9974359]
0.0029 12.0 489 1.2547 0.8397 [0.72972973 0.88610478] [0.99264706 0.79713115] [0.57692308 0.9974359 ]
0.0015 12.98 529 1.4026 0.8285 [0.70523416 0.87909605] [0.99224806 0.78585859] [0.54700855 0.9974359 ]
0.0012 13.99 570 1.4444 0.8237 [0.69444444 0.87612613] [0.99206349 0.7811245 ] [0.53418803 0.9974359 ]
0.0039 14.72 600 1.3421 0.8333 [0.71584699 0.88208617] [0.99242424 0.79065041] [0.55982906 0.9974359 ]

Framework versions

  • Transformers 4.34.1
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1