metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- f1
model-index:
- name: 14-classifier-finetuned-padchest
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: F1
type: f1
value: 0.6619832088435766
14-classifier-finetuned-padchest
This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.9826
- F1: 0.6620
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 2.0848 | 1.0 | 18 | 2.0904 | 0.0451 |
| 2.0822 | 2.0 | 36 | 2.0842 | 0.0848 |
| 2.0681 | 3.0 | 54 | 2.0742 | 0.1092 |
| 2.0568 | 4.0 | 72 | 2.0624 | 0.1443 |
| 2.0363 | 5.0 | 90 | 2.0420 | 0.1747 |
| 2.0235 | 6.0 | 108 | 2.0288 | 0.1533 |
| 1.9984 | 7.0 | 126 | 2.0008 | 0.1816 |
| 1.9647 | 8.0 | 144 | 1.9752 | 0.1849 |
| 1.9257 | 9.0 | 162 | 1.9481 | 0.2075 |
| 1.8791 | 10.0 | 180 | 1.9060 | 0.2219 |
| 1.8561 | 11.0 | 198 | 1.8678 | 0.2734 |
| 1.803 | 12.0 | 216 | 1.8322 | 0.2039 |
| 1.7461 | 13.0 | 234 | 1.8045 | 0.2939 |
| 1.7169 | 14.0 | 252 | 1.8216 | 0.3012 |
| 1.6773 | 15.0 | 270 | 1.7588 | 0.3166 |
| 1.6724 | 16.0 | 288 | 1.7379 | 0.2726 |
| 1.6286 | 17.0 | 306 | 1.7109 | 0.3248 |
| 1.5533 | 18.0 | 324 | 1.6492 | 0.3294 |
| 1.5075 | 19.0 | 342 | 1.5951 | 0.3394 |
| 1.4789 | 20.0 | 360 | 1.5657 | 0.3643 |
| 1.4077 | 21.0 | 378 | 1.5287 | 0.3665 |
| 1.4146 | 22.0 | 396 | 1.4897 | 0.4099 |
| 1.3583 | 23.0 | 414 | 1.4704 | 0.3765 |
| 1.3486 | 24.0 | 432 | 1.4469 | 0.3813 |
| 1.2947 | 25.0 | 450 | 1.4228 | 0.4049 |
| 1.3272 | 26.0 | 468 | 1.4035 | 0.4203 |
| 1.3048 | 27.0 | 486 | 1.3907 | 0.4316 |
| 1.2898 | 28.0 | 504 | 1.3992 | 0.4520 |
| 1.2204 | 29.0 | 522 | 1.3751 | 0.4952 |
| 1.2298 | 30.0 | 540 | 1.3658 | 0.4771 |
| 1.2036 | 31.0 | 558 | 1.3464 | 0.4723 |
| 1.2314 | 32.0 | 576 | 1.3276 | 0.5061 |
| 1.2201 | 33.0 | 594 | 1.3068 | 0.5027 |
| 1.1737 | 34.0 | 612 | 1.2978 | 0.5161 |
| 1.2102 | 35.0 | 630 | 1.2962 | 0.4961 |
| 1.156 | 36.0 | 648 | 1.2793 | 0.5172 |
| 1.1707 | 37.0 | 666 | 1.2715 | 0.5125 |
| 1.149 | 38.0 | 684 | 1.2728 | 0.4986 |
| 1.1685 | 39.0 | 702 | 1.2525 | 0.5101 |
| 1.1212 | 40.0 | 720 | 1.2446 | 0.5100 |
| 1.095 | 41.0 | 738 | 1.2365 | 0.5119 |
| 1.1166 | 42.0 | 756 | 1.2241 | 0.5294 |
| 1.0775 | 43.0 | 774 | 1.2175 | 0.5234 |
| 1.0768 | 44.0 | 792 | 1.2041 | 0.5165 |
| 1.0395 | 45.0 | 810 | 1.1995 | 0.5284 |
| 1.0857 | 46.0 | 828 | 1.2031 | 0.5316 |
| 1.0447 | 47.0 | 846 | 1.1954 | 0.5096 |
| 1.0504 | 48.0 | 864 | 1.1708 | 0.5349 |
| 1.0229 | 49.0 | 882 | 1.1656 | 0.5468 |
| 1.0715 | 50.0 | 900 | 1.1625 | 0.5505 |
| 1.0401 | 51.0 | 918 | 1.1619 | 0.5458 |
| 1.0477 | 52.0 | 936 | 1.1373 | 0.5608 |
| 1.009 | 53.0 | 954 | 1.1425 | 0.5740 |
| 1.0078 | 54.0 | 972 | 1.1397 | 0.5622 |
| 0.9709 | 55.0 | 990 | 1.1503 | 0.5813 |
| 0.9989 | 56.0 | 1008 | 1.1271 | 0.5761 |
| 0.9704 | 57.0 | 1026 | 1.1332 | 0.5691 |
| 0.9537 | 58.0 | 1044 | 1.1113 | 0.5910 |
| 0.9722 | 59.0 | 1062 | 1.1047 | 0.5832 |
| 0.9889 | 60.0 | 1080 | 1.1005 | 0.5815 |
| 0.9682 | 61.0 | 1098 | 1.0862 | 0.6137 |
| 0.9609 | 62.0 | 1116 | 1.0737 | 0.6148 |
| 0.9688 | 63.0 | 1134 | 1.0580 | 0.6238 |
| 0.9488 | 64.0 | 1152 | 1.0645 | 0.6253 |
| 0.926 | 65.0 | 1170 | 1.0576 | 0.6188 |
| 0.9689 | 66.0 | 1188 | 1.0438 | 0.6210 |
| 0.9445 | 67.0 | 1206 | 1.0409 | 0.6319 |
| 0.938 | 68.0 | 1224 | 1.0302 | 0.6397 |
| 0.9134 | 69.0 | 1242 | 1.0346 | 0.6337 |
| 0.9125 | 70.0 | 1260 | 1.0221 | 0.6575 |
| 0.8879 | 71.0 | 1278 | 1.0146 | 0.6633 |
| 0.9212 | 72.0 | 1296 | 1.0206 | 0.6384 |
| 0.9259 | 73.0 | 1314 | 1.0255 | 0.6213 |
| 0.9224 | 74.0 | 1332 | 1.0190 | 0.6417 |
| 0.9249 | 75.0 | 1350 | 1.0063 | 0.6371 |
| 0.8888 | 76.0 | 1368 | 0.9951 | 0.6458 |
| 0.8799 | 77.0 | 1386 | 1.0045 | 0.6436 |
| 0.9186 | 78.0 | 1404 | 0.9871 | 0.6449 |
| 0.9087 | 79.0 | 1422 | 1.0031 | 0.6611 |
| 0.914 | 80.0 | 1440 | 0.9893 | 0.6501 |
| 0.9012 | 81.0 | 1458 | 0.9876 | 0.6441 |
| 0.8748 | 82.0 | 1476 | 0.9873 | 0.6533 |
| 0.8736 | 83.0 | 1494 | 0.9951 | 0.6524 |
| 0.892 | 84.0 | 1512 | 1.0012 | 0.6563 |
| 0.8746 | 85.0 | 1530 | 0.9944 | 0.6684 |
| 0.8769 | 86.0 | 1548 | 0.9841 | 0.6558 |
| 0.8816 | 87.0 | 1566 | 0.9930 | 0.6551 |
| 0.8889 | 88.0 | 1584 | 0.9880 | 0.6497 |
| 0.8705 | 89.0 | 1602 | 0.9874 | 0.6564 |
| 0.8607 | 90.0 | 1620 | 0.9850 | 0.6471 |
| 0.86 | 91.0 | 1638 | 0.9851 | 0.6572 |
| 0.878 | 92.0 | 1656 | 0.9835 | 0.6553 |
| 0.8592 | 93.0 | 1674 | 0.9784 | 0.6577 |
| 0.8699 | 94.0 | 1692 | 0.9783 | 0.6568 |
| 0.8413 | 95.0 | 1710 | 0.9909 | 0.6519 |
| 0.8944 | 96.0 | 1728 | 0.9759 | 0.6581 |
| 0.8404 | 97.0 | 1746 | 0.9834 | 0.6640 |
| 0.8954 | 98.0 | 1764 | 0.9785 | 0.6582 |
| 0.8539 | 99.0 | 1782 | 0.9746 | 0.6528 |
| 0.8732 | 100.0 | 1800 | 0.9826 | 0.6620 |
Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.19.0
- Tokenizers 0.12.1