Hugging Face's logo

bdpc
/
resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_hint

Image Classification
Transformers
PyTorch
resnet
Generated from Trainer
Inference Endpoints
Model card Files Community
resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_hint / README.md
bdpc's picture
bdpc
Saving best model to hub
57794d5
preview code
|
raw
history blame
7.87 kB
metadata
license: apache-2.0
base_model: microsoft/resnet-50
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_hint
    results: []

resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_hint

This model is a fine-tuned version of microsoft/resnet-50 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 20.4893
  • Accuracy: 0.7622
  • Brier Loss: 0.3995
  • Nll: 2.6673
  • F1 Micro: 0.7622
  • F1 Macro: 0.7619
  • Ece: 0.1742
  • Aurc: 0.0853

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: 0.0001
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy Brier Loss Nll F1 Micro F1 Macro Ece Aurc
No log 1.0 250 27.0152 0.144 0.9329 8.3774 0.144 0.1293 0.0760 0.8496
26.9201 2.0 500 25.8022 0.4547 0.8625 4.1098 0.4547 0.4194 0.3292 0.3673
26.9201 3.0 750 24.5485 0.5617 0.6135 3.0722 0.5617 0.5439 0.1557 0.2257
24.565 4.0 1000 23.9825 0.6388 0.5062 2.7343 0.6388 0.6354 0.1084 0.1537
24.565 5.0 1250 23.8483 0.6747 0.4518 2.5930 0.6747 0.6686 0.0597 0.1289
23.3904 6.0 1500 23.2280 0.7137 0.3953 2.4736 0.7138 0.7117 0.0486 0.0997
23.3904 7.0 1750 23.0275 0.725 0.3781 2.3823 0.7250 0.7238 0.0414 0.0911
22.6462 8.0 2000 22.8213 0.7358 0.3699 2.3745 0.7358 0.7351 0.0539 0.0881
22.6462 9.0 2250 22.6219 0.7468 0.3629 2.3056 0.7468 0.7465 0.0617 0.0852
22.0944 10.0 2500 22.4746 0.751 0.3593 2.3500 0.751 0.7523 0.0637 0.0846
22.0944 11.0 2750 22.3503 0.752 0.3624 2.4245 0.752 0.7533 0.0810 0.0834
21.6411 12.0 3000 22.2263 0.7545 0.3693 2.4277 0.7545 0.7547 0.0972 0.0885
21.6411 13.0 3250 22.1353 0.7522 0.3740 2.4647 0.7522 0.7532 0.1141 0.0862
21.2742 14.0 3500 22.1122 0.7475 0.3868 2.5369 0.7475 0.7495 0.1250 0.0922
21.2742 15.0 3750 22.0040 0.7508 0.3842 2.5364 0.7508 0.7501 0.1304 0.0911
20.9515 16.0 4000 21.8795 0.758 0.3772 2.5474 0.7580 0.7578 0.1324 0.0846
20.9515 17.0 4250 21.7554 0.754 0.3892 2.5498 0.754 0.7543 0.1420 0.0923
20.6695 18.0 4500 21.6863 0.749 0.3981 2.6337 0.749 0.7507 0.1510 0.0922
20.6695 19.0 4750 21.6123 0.7498 0.4007 2.5993 0.7498 0.7499 0.1551 0.0921
20.4239 20.0 5000 21.5128 0.7595 0.3845 2.5510 0.7595 0.7590 0.1498 0.0870
20.4239 21.0 5250 21.4770 0.7542 0.4005 2.6396 0.7542 0.7547 0.1623 0.0932
20.2131 22.0 5500 21.3497 0.7612 0.3892 2.5117 0.7612 0.7609 0.1539 0.0891
20.2131 23.0 5750 21.3489 0.7572 0.3956 2.5227 0.7572 0.7570 0.1608 0.0883
20.0332 24.0 6000 21.2609 0.7585 0.3939 2.5487 0.7585 0.7595 0.1629 0.0860
20.0332 25.0 6250 21.2046 0.7552 0.3982 2.6283 0.7552 0.7559 0.1663 0.0878
19.8699 26.0 6500 21.1515 0.7528 0.4038 2.6730 0.7528 0.7536 0.1721 0.0858
19.8699 27.0 6750 21.0789 0.7562 0.4003 2.6027 0.7562 0.7575 0.1683 0.0876
19.7228 28.0 7000 21.0357 0.7565 0.3996 2.6490 0.7565 0.7561 0.1707 0.0844
19.7228 29.0 7250 20.9975 0.758 0.3971 2.6300 0.7580 0.7574 0.1704 0.0835
19.589 30.0 7500 20.9221 0.7568 0.4007 2.5841 0.7568 0.7567 0.1714 0.0860
19.589 31.0 7750 20.8725 0.7562 0.3996 2.5775 0.7562 0.7562 0.1752 0.0847
19.4738 32.0 8000 20.8438 0.7572 0.3999 2.6441 0.7572 0.7570 0.1693 0.0877
19.4738 33.0 8250 20.8337 0.755 0.4052 2.6660 0.755 0.7555 0.1743 0.0868
19.3704 34.0 8500 20.7635 0.7575 0.4022 2.6885 0.7575 0.7583 0.1764 0.0868
19.3704 35.0 8750 20.7705 0.7608 0.4001 2.6415 0.7608 0.7601 0.1735 0.0856
19.2791 36.0 9000 20.7221 0.7632 0.3984 2.7139 0.7632 0.7640 0.1706 0.0857
19.2791 37.0 9250 20.6873 0.7622 0.3986 2.6743 0.7622 0.7625 0.1715 0.0838
19.2036 38.0 9500 20.6757 0.7618 0.3990 2.6225 0.7618 0.7620 0.1735 0.0852
19.2036 39.0 9750 20.6421 0.7588 0.4018 2.6342 0.7588 0.7579 0.1761 0.0870
19.1398 40.0 10000 20.6432 0.761 0.4057 2.6595 0.761 0.7610 0.1760 0.0868
19.1398 41.0 10250 20.5778 0.7672 0.3981 2.6180 0.7672 0.7674 0.1680 0.0850
19.0835 42.0 10500 20.5628 0.764 0.3981 2.6309 0.764 0.7625 0.1726 0.0851
19.0835 43.0 10750 20.5530 0.7632 0.3995 2.6470 0.7632 0.7628 0.1733 0.0868
19.0398 44.0 11000 20.5625 0.761 0.4029 2.6650 0.761 0.7608 0.1764 0.0864
19.0398 45.0 11250 20.5637 0.7628 0.4010 2.6709 0.7628 0.7623 0.1760 0.0850
19.0073 46.0 11500 20.5378 0.7628 0.3998 2.6522 0.7628 0.7631 0.1749 0.0859
19.0073 47.0 11750 20.5199 0.7615 0.4010 2.6406 0.7615 0.7619 0.1748 0.0867
18.9818 48.0 12000 20.5378 0.761 0.4031 2.6434 0.761 0.7616 0.1767 0.0856
18.9818 49.0 12250 20.4962 0.7652 0.3962 2.6250 0.7652 0.7653 0.1720 0.0853
18.9734 50.0 12500 20.4893 0.7622 0.3995 2.6673 0.7622 0.7619 0.1742 0.0853

Framework versions

  • Transformers 4.33.3
  • Pytorch 2.2.0.dev20231002
  • Datasets 2.7.1
  • Tokenizers 0.13.3