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resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_MSE

Image Classification
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PyTorch
resnet
Generated from Trainer
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  1. README.md +166 -0
  2. config.json +76 -0
  3. pytorch_model.bin +3 -0
  4. test-logits.npz +3 -0
  5. test-references.npz +3 -0
  6. training_args.bin +3 -0
  7. validation-logits.npz +3 -0
  8. validation-references.npz +3 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: microsoft/resnet-50
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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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+ model-index:
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+ - name: resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_MSE
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+ results: []
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+ ---
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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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+
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+ # resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_MSE
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+
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+ This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.7429
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+ - Accuracy: 0.7853
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+ - Brier Loss: 0.3044
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+ - Nll: 2.0364
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+ - F1 Micro: 0.7853
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+ - F1 Macro: 0.7862
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+ - Ece: 0.0430
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+ - Aurc: 0.0599
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0001
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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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+ - 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: 100
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
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+ | No log | 1.0 | 250 | 9.5443 | 0.0765 | 0.9365 | 3.7373 | 0.0765 | 0.0522 | 0.0360 | 0.9336 |
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+ | 9.4095 | 2.0 | 500 | 7.4542 | 0.0757 | 0.9312 | 2.8468 | 0.0757 | 0.0316 | 0.0425 | 0.8840 |
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+ | 9.4095 | 3.0 | 750 | 5.8933 | 0.0975 | 0.9356 | 3.2058 | 0.0975 | 0.0408 | 0.0798 | 0.8593 |
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+ | 5.9994 | 4.0 | 1000 | 4.3665 | 0.2125 | 0.8700 | 5.3759 | 0.2125 | 0.1290 | 0.0743 | 0.7029 |
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+ | 5.9994 | 5.0 | 1250 | 3.0367 | 0.4415 | 0.6924 | 4.9073 | 0.4415 | 0.4283 | 0.0806 | 0.3570 |
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+ | 3.2184 | 6.0 | 1500 | 2.1589 | 0.579 | 0.5587 | 3.7412 | 0.579 | 0.5771 | 0.0572 | 0.2172 |
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+ | 3.2184 | 7.0 | 1750 | 1.5582 | 0.652 | 0.4673 | 3.0701 | 0.652 | 0.6456 | 0.0517 | 0.1478 |
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+ | 1.6737 | 8.0 | 2000 | 1.3502 | 0.6893 | 0.4266 | 2.8575 | 0.6893 | 0.6860 | 0.0544 | 0.1175 |
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+ | 1.6737 | 9.0 | 2250 | 1.1389 | 0.7188 | 0.3914 | 2.5937 | 0.7188 | 0.7195 | 0.0544 | 0.1006 |
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+ | 1.0789 | 10.0 | 2500 | 1.0563 | 0.7302 | 0.3742 | 2.5043 | 0.7302 | 0.7305 | 0.0618 | 0.0912 |
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+ | 1.0789 | 11.0 | 2750 | 1.0035 | 0.7428 | 0.3604 | 2.4375 | 0.7428 | 0.7441 | 0.0587 | 0.0823 |
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+ | 0.7934 | 12.0 | 3000 | 0.9169 | 0.7548 | 0.3472 | 2.2921 | 0.7548 | 0.7555 | 0.0547 | 0.0762 |
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+ | 0.7934 | 13.0 | 3250 | 0.8628 | 0.7598 | 0.3386 | 2.2849 | 0.7598 | 0.7600 | 0.0550 | 0.0739 |
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+ | 0.6268 | 14.0 | 3500 | 0.8773 | 0.7675 | 0.3362 | 2.2170 | 0.7675 | 0.7692 | 0.0490 | 0.0718 |
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+ | 0.6268 | 15.0 | 3750 | 0.8263 | 0.7682 | 0.3306 | 2.1617 | 0.7682 | 0.7702 | 0.0534 | 0.0704 |
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+ | 0.5269 | 16.0 | 4000 | 0.8422 | 0.7708 | 0.3289 | 2.1907 | 0.7707 | 0.7717 | 0.0524 | 0.0687 |
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+ | 0.5269 | 17.0 | 4250 | 0.8100 | 0.7745 | 0.3241 | 2.1664 | 0.7745 | 0.7761 | 0.0509 | 0.0667 |
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+ | 0.4516 | 18.0 | 4500 | 0.8013 | 0.7778 | 0.3215 | 2.1216 | 0.7778 | 0.7790 | 0.0473 | 0.0669 |
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+ | 0.4516 | 19.0 | 4750 | 0.7911 | 0.7802 | 0.3183 | 2.1224 | 0.7802 | 0.7812 | 0.0476 | 0.0648 |
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+ | 0.4039 | 20.0 | 5000 | 0.7900 | 0.7775 | 0.3197 | 2.0969 | 0.7775 | 0.7797 | 0.0473 | 0.0647 |
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+ | 0.4039 | 21.0 | 5250 | 0.7919 | 0.7792 | 0.3191 | 2.1445 | 0.7792 | 0.7810 | 0.0531 | 0.0652 |
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+ | 0.3563 | 22.0 | 5500 | 0.7960 | 0.7802 | 0.3166 | 2.0849 | 0.7802 | 0.7818 | 0.0478 | 0.0649 |
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+ | 0.3563 | 23.0 | 5750 | 0.7615 | 0.7825 | 0.3128 | 2.0834 | 0.7825 | 0.7833 | 0.0478 | 0.0638 |
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+ | 0.3251 | 24.0 | 6000 | 0.7840 | 0.7792 | 0.3151 | 2.0841 | 0.7792 | 0.7800 | 0.0513 | 0.0648 |
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+ | 0.3251 | 25.0 | 6250 | 0.7837 | 0.7792 | 0.3159 | 2.0889 | 0.7792 | 0.7808 | 0.0485 | 0.0643 |
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+ | 0.2949 | 26.0 | 6500 | 0.7827 | 0.7802 | 0.3158 | 2.0416 | 0.7802 | 0.7819 | 0.0548 | 0.0648 |
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+ | 0.2949 | 27.0 | 6750 | 0.7650 | 0.78 | 0.3130 | 2.0411 | 0.78 | 0.7807 | 0.0506 | 0.0629 |
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+ | 0.2669 | 28.0 | 7000 | 0.7787 | 0.7802 | 0.3133 | 2.0843 | 0.7802 | 0.7810 | 0.0454 | 0.0627 |
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+ | 0.2669 | 29.0 | 7250 | 0.7892 | 0.782 | 0.3163 | 2.0953 | 0.782 | 0.7826 | 0.0508 | 0.0635 |
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+ | 0.2512 | 30.0 | 7500 | 0.7775 | 0.7825 | 0.3126 | 2.0904 | 0.7825 | 0.7837 | 0.0451 | 0.0633 |
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+ | 0.2512 | 31.0 | 7750 | 0.7601 | 0.7817 | 0.3124 | 2.0251 | 0.7817 | 0.7827 | 0.0485 | 0.0627 |
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+ | 0.231 | 32.0 | 8000 | 0.7669 | 0.7833 | 0.3120 | 2.0685 | 0.7833 | 0.7842 | 0.0472 | 0.0629 |
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+ | 0.231 | 33.0 | 8250 | 0.7652 | 0.7847 | 0.3116 | 2.0661 | 0.7847 | 0.7858 | 0.0519 | 0.0625 |
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+ | 0.2172 | 34.0 | 8500 | 0.7637 | 0.7837 | 0.3107 | 2.0264 | 0.7837 | 0.7852 | 0.0487 | 0.0628 |
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+ | 0.2172 | 35.0 | 8750 | 0.7691 | 0.783 | 0.3120 | 2.0535 | 0.7830 | 0.7844 | 0.0438 | 0.0634 |
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+ | 0.2032 | 36.0 | 9000 | 0.7647 | 0.7845 | 0.3093 | 2.0480 | 0.7845 | 0.7852 | 0.0471 | 0.0620 |
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+ | 0.2032 | 37.0 | 9250 | 0.7727 | 0.782 | 0.3122 | 2.0610 | 0.782 | 0.7830 | 0.0493 | 0.0628 |
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+ | 0.1925 | 38.0 | 9500 | 0.7563 | 0.7843 | 0.3085 | 2.0267 | 0.7843 | 0.7849 | 0.0459 | 0.0608 |
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+ | 0.1925 | 39.0 | 9750 | 0.7597 | 0.7835 | 0.3087 | 2.0062 | 0.7835 | 0.7845 | 0.0485 | 0.0614 |
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+ | 0.1823 | 40.0 | 10000 | 0.7611 | 0.7833 | 0.3107 | 2.0007 | 0.7833 | 0.7853 | 0.0479 | 0.0625 |
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+ | 0.1823 | 41.0 | 10250 | 0.7608 | 0.7843 | 0.3076 | 2.0335 | 0.7843 | 0.7854 | 0.0486 | 0.0602 |
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+ | 0.17 | 42.0 | 10500 | 0.7535 | 0.7833 | 0.3096 | 2.0121 | 0.7833 | 0.7844 | 0.0505 | 0.0613 |
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+ | 0.17 | 43.0 | 10750 | 0.7524 | 0.7845 | 0.3066 | 2.0425 | 0.7845 | 0.7856 | 0.0476 | 0.0605 |
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+ | 0.1639 | 44.0 | 11000 | 0.7608 | 0.7808 | 0.3108 | 2.0739 | 0.7808 | 0.7816 | 0.0503 | 0.0618 |
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+ | 0.1639 | 45.0 | 11250 | 0.7560 | 0.786 | 0.3063 | 1.9876 | 0.786 | 0.7868 | 0.0496 | 0.0607 |
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+ | 0.1575 | 46.0 | 11500 | 0.7494 | 0.784 | 0.3063 | 2.0311 | 0.7840 | 0.7846 | 0.0416 | 0.0601 |
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+ | 0.1575 | 47.0 | 11750 | 0.7515 | 0.7857 | 0.3069 | 2.0539 | 0.7857 | 0.7866 | 0.0456 | 0.0609 |
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+ | 0.1493 | 48.0 | 12000 | 0.7511 | 0.7843 | 0.3086 | 2.0325 | 0.7843 | 0.7852 | 0.0552 | 0.0612 |
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+ | 0.1493 | 49.0 | 12250 | 0.7495 | 0.787 | 0.3067 | 2.0231 | 0.787 | 0.7880 | 0.0475 | 0.0605 |
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+ | 0.1425 | 50.0 | 12500 | 0.7538 | 0.7867 | 0.3052 | 2.0267 | 0.7868 | 0.7870 | 0.0507 | 0.0603 |
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+ | 0.1425 | 51.0 | 12750 | 0.7529 | 0.7847 | 0.3081 | 2.0592 | 0.7847 | 0.7859 | 0.0467 | 0.0604 |
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+ | 0.1356 | 52.0 | 13000 | 0.7527 | 0.7808 | 0.3071 | 2.0349 | 0.7808 | 0.7818 | 0.0473 | 0.0607 |
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+ | 0.1356 | 53.0 | 13250 | 0.7451 | 0.7865 | 0.3049 | 2.0368 | 0.7865 | 0.7879 | 0.0484 | 0.0595 |
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+ | 0.1325 | 54.0 | 13500 | 0.7481 | 0.7857 | 0.3056 | 2.0223 | 0.7857 | 0.7869 | 0.0468 | 0.0603 |
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+ | 0.1325 | 55.0 | 13750 | 0.7470 | 0.7835 | 0.3057 | 2.0306 | 0.7835 | 0.7844 | 0.0492 | 0.0601 |
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+ | 0.1264 | 56.0 | 14000 | 0.7471 | 0.7873 | 0.3053 | 2.0336 | 0.7873 | 0.7880 | 0.0519 | 0.0601 |
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+ | 0.1264 | 57.0 | 14250 | 0.7429 | 0.7895 | 0.3032 | 2.0149 | 0.7895 | 0.7903 | 0.0468 | 0.0595 |
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+ | 0.1208 | 58.0 | 14500 | 0.7399 | 0.7885 | 0.3035 | 2.0147 | 0.7885 | 0.7895 | 0.0433 | 0.0596 |
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+ | 0.1208 | 59.0 | 14750 | 0.7518 | 0.786 | 0.3076 | 2.0481 | 0.786 | 0.7873 | 0.0403 | 0.0607 |
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+ | 0.119 | 60.0 | 15000 | 0.7483 | 0.7903 | 0.3058 | 2.0138 | 0.7903 | 0.7914 | 0.0471 | 0.0601 |
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+ | 0.119 | 61.0 | 15250 | 0.7463 | 0.7845 | 0.3043 | 2.0617 | 0.7845 | 0.7855 | 0.0458 | 0.0599 |
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+ | 0.1128 | 62.0 | 15500 | 0.7478 | 0.7875 | 0.3056 | 2.0187 | 0.7875 | 0.7888 | 0.0452 | 0.0604 |
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+ | 0.1128 | 63.0 | 15750 | 0.7510 | 0.784 | 0.3061 | 2.0204 | 0.7840 | 0.7850 | 0.0495 | 0.0605 |
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+ | 0.1109 | 64.0 | 16000 | 0.7424 | 0.786 | 0.3053 | 2.0167 | 0.786 | 0.7871 | 0.0449 | 0.0603 |
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+ | 0.1109 | 65.0 | 16250 | 0.7473 | 0.7885 | 0.3054 | 2.0200 | 0.7885 | 0.7893 | 0.0471 | 0.0600 |
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+ | 0.1078 | 66.0 | 16500 | 0.7467 | 0.7873 | 0.3054 | 2.0224 | 0.7873 | 0.7883 | 0.0482 | 0.0599 |
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+ | 0.1078 | 67.0 | 16750 | 0.7445 | 0.7893 | 0.3039 | 2.0082 | 0.7893 | 0.7895 | 0.0456 | 0.0593 |
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+ | 0.1051 | 68.0 | 17000 | 0.7490 | 0.7873 | 0.3063 | 2.0152 | 0.7873 | 0.7883 | 0.0505 | 0.0602 |
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+ | 0.1051 | 69.0 | 17250 | 0.7490 | 0.785 | 0.3061 | 2.0103 | 0.785 | 0.7861 | 0.0465 | 0.0602 |
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+ | 0.1009 | 70.0 | 17500 | 0.7445 | 0.7875 | 0.3049 | 2.0308 | 0.7875 | 0.7884 | 0.0483 | 0.0598 |
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+ | 0.1009 | 71.0 | 17750 | 0.7490 | 0.7863 | 0.3068 | 2.0260 | 0.7863 | 0.7875 | 0.0495 | 0.0604 |
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+ | 0.0984 | 72.0 | 18000 | 0.7465 | 0.7893 | 0.3059 | 2.0161 | 0.7893 | 0.7906 | 0.0427 | 0.0601 |
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+ | 0.0984 | 73.0 | 18250 | 0.7451 | 0.7873 | 0.3058 | 2.0204 | 0.7873 | 0.7882 | 0.0511 | 0.0605 |
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+ | 0.0966 | 74.0 | 18500 | 0.7445 | 0.7875 | 0.3042 | 2.0227 | 0.7875 | 0.7886 | 0.0495 | 0.0599 |
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+ | 0.0966 | 75.0 | 18750 | 0.7443 | 0.7863 | 0.3040 | 2.0138 | 0.7863 | 0.7872 | 0.0442 | 0.0598 |
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+ | 0.0947 | 76.0 | 19000 | 0.7448 | 0.7865 | 0.3054 | 2.0234 | 0.7865 | 0.7873 | 0.0457 | 0.0598 |
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+ | 0.0947 | 77.0 | 19250 | 0.7448 | 0.7865 | 0.3041 | 2.0110 | 0.7865 | 0.7875 | 0.0508 | 0.0596 |
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+ | 0.0931 | 78.0 | 19500 | 0.7460 | 0.7883 | 0.3040 | 2.0125 | 0.7883 | 0.7895 | 0.0467 | 0.0595 |
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+ | 0.0931 | 79.0 | 19750 | 0.7456 | 0.7883 | 0.3038 | 2.0302 | 0.7883 | 0.7894 | 0.0455 | 0.0596 |
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+ | 0.0899 | 80.0 | 20000 | 0.7469 | 0.788 | 0.3040 | 2.0188 | 0.788 | 0.7892 | 0.0487 | 0.0597 |
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+ | 0.0899 | 81.0 | 20250 | 0.7421 | 0.788 | 0.3041 | 2.0359 | 0.788 | 0.7888 | 0.0427 | 0.0595 |
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+ | 0.0882 | 82.0 | 20500 | 0.7444 | 0.7865 | 0.3051 | 2.0219 | 0.7865 | 0.7875 | 0.0479 | 0.0600 |
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+ | 0.0882 | 83.0 | 20750 | 0.7439 | 0.788 | 0.3039 | 2.0197 | 0.788 | 0.7894 | 0.0439 | 0.0597 |
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+ | 0.0871 | 84.0 | 21000 | 0.7421 | 0.7865 | 0.3040 | 1.9910 | 0.7865 | 0.7876 | 0.0445 | 0.0598 |
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+ | 0.0871 | 85.0 | 21250 | 0.7429 | 0.7887 | 0.3043 | 2.0253 | 0.7887 | 0.7898 | 0.0426 | 0.0597 |
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+ | 0.0869 | 86.0 | 21500 | 0.7442 | 0.7873 | 0.3041 | 2.0156 | 0.7873 | 0.7885 | 0.0488 | 0.0596 |
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+ | 0.0869 | 87.0 | 21750 | 0.7439 | 0.7857 | 0.3051 | 2.0099 | 0.7857 | 0.7867 | 0.0465 | 0.0599 |
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+ | 0.084 | 88.0 | 22000 | 0.7434 | 0.786 | 0.3040 | 1.9926 | 0.786 | 0.7869 | 0.0469 | 0.0598 |
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+ | 0.084 | 89.0 | 22250 | 0.7431 | 0.7873 | 0.3048 | 2.0028 | 0.7873 | 0.7880 | 0.0442 | 0.0599 |
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+ | 0.0821 | 90.0 | 22500 | 0.7447 | 0.7867 | 0.3040 | 2.0349 | 0.7868 | 0.7876 | 0.0477 | 0.0596 |
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+ | 0.0821 | 91.0 | 22750 | 0.7443 | 0.7877 | 0.3051 | 2.0356 | 0.7877 | 0.7887 | 0.0486 | 0.0601 |
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+ | 0.0813 | 92.0 | 23000 | 0.7500 | 0.7873 | 0.3053 | 2.0465 | 0.7873 | 0.7880 | 0.0484 | 0.0601 |
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+ | 0.0813 | 93.0 | 23250 | 0.7449 | 0.788 | 0.3037 | 1.9966 | 0.788 | 0.7890 | 0.0441 | 0.0594 |
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+ | 0.0811 | 94.0 | 23500 | 0.7466 | 0.7897 | 0.3048 | 2.0297 | 0.7897 | 0.7907 | 0.0429 | 0.0600 |
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+ | 0.0811 | 95.0 | 23750 | 0.7482 | 0.7875 | 0.3058 | 2.0319 | 0.7875 | 0.7885 | 0.0464 | 0.0601 |
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+ | 0.0808 | 96.0 | 24000 | 0.7473 | 0.7863 | 0.3055 | 2.0219 | 0.7863 | 0.7874 | 0.0477 | 0.0603 |
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+ | 0.0808 | 97.0 | 24250 | 0.7451 | 0.7855 | 0.3044 | 2.0356 | 0.7855 | 0.7865 | 0.0481 | 0.0594 |
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+ | 0.08 | 98.0 | 24500 | 0.7442 | 0.7857 | 0.3042 | 2.0213 | 0.7857 | 0.7868 | 0.0475 | 0.0595 |
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+ | 0.08 | 99.0 | 24750 | 0.7462 | 0.7863 | 0.3053 | 2.0354 | 0.7863 | 0.7874 | 0.0425 | 0.0599 |
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+ | 0.079 | 100.0 | 25000 | 0.7429 | 0.7853 | 0.3044 | 2.0364 | 0.7853 | 0.7862 | 0.0430 | 0.0599 |
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+
160
+
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+ ### Framework versions
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+
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+ - Transformers 4.33.3
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+ - Pytorch 2.2.0.dev20231002
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+ - Datasets 2.7.1
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+ - Tokenizers 0.13.3
config.json ADDED
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+ {
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+ "_name_or_path": "microsoft/resnet-50",
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+ "architectures": [
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+ "ResNetForImageClassification"
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+ ],
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+ "depths": [
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+ 3,
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+ 4,
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+ 6,
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+ 3
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+ ],
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+ "downsample_in_first_stage": false,
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+ "embedding_size": 64,
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+ "hidden_act": "relu",
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+ "hidden_sizes": [
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+ 256,
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+ 512,
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+ 1024,
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+ 2048
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+ ],
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+ "id2label": {
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+ "0": "letter",
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+ "1": "form",
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+ "2": "email",
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+ "3": "handwritten",
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+ "4": "advertisement",
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+ "5": "scientific_report",
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+ "6": "scientific_publication",
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+ "7": "specification",
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+ "8": "file_folder",
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+ "9": "news_article",
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+ "10": "budget",
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+ "11": "invoice",
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+ "12": "presentation",
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+ "13": "questionnaire",
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+ "14": "resume",
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+ "15": "memo"
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+ },
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+ "label2id": {
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+ "advertisement": 4,
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+ "budget": 10,
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+ "email": 2,
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+ "file_folder": 8,
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+ "form": 1,
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+ "handwritten": 3,
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+ "invoice": 11,
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+ "letter": 0,
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+ "memo": 15,
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+ "news_article": 9,
50
+ "presentation": 12,
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+ "questionnaire": 13,
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+ "resume": 14,
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+ "scientific_publication": 6,
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+ "scientific_report": 5,
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+ "specification": 7
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+ },
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+ "layer_type": "bottleneck",
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+ "model_type": "resnet",
59
+ "num_channels": 3,
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+ "out_features": [
61
+ "stage4"
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+ ],
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+ "out_indices": [
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+ 4
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+ ],
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+ "problem_type": "single_label_classification",
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+ "stage_names": [
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+ "stem",
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+ "stage1",
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+ "stage2",
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+ "stage3",
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+ "stage4"
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+ ],
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.33.3"
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+ }
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