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resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5

Image Classification
Transformers
PyTorch
resnet
Generated from Trainer
Inference Endpoints
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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_CEKD_t2.5_a0.5
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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_CEKD_t2.5_a0.5
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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: 0.5837
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+ - Accuracy: 0.7867
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+ - Brier Loss: 0.3013
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+ - Nll: 1.9882
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+ - F1 Micro: 0.7868
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+ - F1 Macro: 0.7860
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+ - Ece: 0.0529
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+ - Aurc: 0.0581
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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 | 4.1958 | 0.1035 | 0.9350 | 9.1004 | 0.1035 | 0.0792 | 0.0472 | 0.9013 |
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+ | 4.2322 | 2.0 | 500 | 4.0778 | 0.173 | 0.9251 | 6.5742 | 0.173 | 0.1393 | 0.0993 | 0.7501 |
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+ | 4.2322 | 3.0 | 750 | 3.6484 | 0.339 | 0.8778 | 4.9108 | 0.339 | 0.2957 | 0.2172 | 0.5305 |
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+ | 3.5256 | 4.0 | 1000 | 2.5967 | 0.4592 | 0.6991 | 3.3640 | 0.4592 | 0.4220 | 0.1274 | 0.3285 |
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+ | 3.5256 | 5.0 | 1250 | 2.0345 | 0.5417 | 0.6078 | 3.0118 | 0.5417 | 0.5180 | 0.0976 | 0.2447 |
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+ | 1.9172 | 6.0 | 1500 | 1.4417 | 0.625 | 0.5029 | 2.7890 | 0.625 | 0.6123 | 0.0549 | 0.1623 |
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+ | 1.9172 | 7.0 | 1750 | 1.3298 | 0.639 | 0.4852 | 2.6110 | 0.639 | 0.6320 | 0.0558 | 0.1501 |
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+ | 1.1801 | 8.0 | 2000 | 1.1697 | 0.674 | 0.4473 | 2.4787 | 0.674 | 0.6712 | 0.0466 | 0.1283 |
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+ | 1.1801 | 9.0 | 2250 | 0.9625 | 0.7093 | 0.4020 | 2.3242 | 0.7093 | 0.7085 | 0.0526 | 0.1017 |
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+ | 0.8029 | 10.0 | 2500 | 0.9477 | 0.7215 | 0.3893 | 2.3193 | 0.7215 | 0.7228 | 0.0515 | 0.0971 |
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+ | 0.8029 | 11.0 | 2750 | 0.8527 | 0.7375 | 0.3692 | 2.2785 | 0.7375 | 0.7377 | 0.0490 | 0.0870 |
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+ | 0.5717 | 12.0 | 3000 | 0.7377 | 0.7515 | 0.3470 | 2.1475 | 0.7515 | 0.7529 | 0.0552 | 0.0757 |
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+ | 0.5717 | 13.0 | 3250 | 0.7309 | 0.7498 | 0.3469 | 2.1250 | 0.7498 | 0.7494 | 0.0589 | 0.0758 |
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+ | 0.4414 | 14.0 | 3500 | 0.7165 | 0.7558 | 0.3427 | 2.1045 | 0.7558 | 0.7576 | 0.0582 | 0.0721 |
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+ | 0.4414 | 15.0 | 3750 | 0.6865 | 0.7678 | 0.3319 | 2.0457 | 0.7678 | 0.7688 | 0.0551 | 0.0697 |
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+ | 0.3691 | 16.0 | 4000 | 0.7002 | 0.7662 | 0.3348 | 2.1280 | 0.7663 | 0.7664 | 0.0567 | 0.0698 |
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+ | 0.3691 | 17.0 | 4250 | 0.6896 | 0.7628 | 0.3326 | 2.0750 | 0.7628 | 0.7631 | 0.0608 | 0.0691 |
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+ | 0.3214 | 18.0 | 4500 | 0.6666 | 0.7715 | 0.3258 | 2.0468 | 0.7715 | 0.7707 | 0.0544 | 0.0680 |
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+ | 0.3214 | 19.0 | 4750 | 0.6735 | 0.7702 | 0.3277 | 2.0544 | 0.7702 | 0.7700 | 0.0571 | 0.0681 |
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+ | 0.2914 | 20.0 | 5000 | 0.6607 | 0.772 | 0.3241 | 2.0364 | 0.772 | 0.7729 | 0.0525 | 0.0659 |
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+ | 0.2914 | 21.0 | 5250 | 0.6625 | 0.7688 | 0.3217 | 2.0387 | 0.7688 | 0.7703 | 0.0455 | 0.0664 |
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+ | 0.2653 | 22.0 | 5500 | 0.6543 | 0.775 | 0.3200 | 2.0560 | 0.775 | 0.7752 | 0.0507 | 0.0647 |
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+ | 0.2653 | 23.0 | 5750 | 0.6409 | 0.7725 | 0.3188 | 2.0091 | 0.7725 | 0.7733 | 0.0554 | 0.0647 |
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+ | 0.2482 | 24.0 | 6000 | 0.6452 | 0.7758 | 0.3191 | 2.0256 | 0.7758 | 0.7756 | 0.0502 | 0.0655 |
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+ | 0.2482 | 25.0 | 6250 | 0.6401 | 0.7742 | 0.3196 | 2.0668 | 0.7742 | 0.7745 | 0.0528 | 0.0648 |
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+ | 0.2354 | 26.0 | 6500 | 0.6316 | 0.775 | 0.3171 | 2.0150 | 0.775 | 0.7755 | 0.0555 | 0.0634 |
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+ | 0.2354 | 27.0 | 6750 | 0.6257 | 0.7808 | 0.3147 | 2.0129 | 0.7808 | 0.7808 | 0.0503 | 0.0624 |
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+ | 0.2229 | 28.0 | 7000 | 0.6343 | 0.7778 | 0.3144 | 2.0910 | 0.7778 | 0.7776 | 0.0510 | 0.0624 |
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+ | 0.2229 | 29.0 | 7250 | 0.6206 | 0.781 | 0.3115 | 2.0399 | 0.7810 | 0.7798 | 0.0555 | 0.0606 |
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+ | 0.2147 | 30.0 | 7500 | 0.6262 | 0.777 | 0.3124 | 2.0603 | 0.777 | 0.7772 | 0.0539 | 0.0616 |
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+ | 0.2147 | 31.0 | 7750 | 0.6265 | 0.7788 | 0.3137 | 2.0833 | 0.7788 | 0.7777 | 0.0532 | 0.0614 |
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+ | 0.2058 | 32.0 | 8000 | 0.6134 | 0.7815 | 0.3119 | 2.0369 | 0.7815 | 0.7815 | 0.0514 | 0.0615 |
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+ | 0.2058 | 33.0 | 8250 | 0.6153 | 0.7772 | 0.3133 | 2.0513 | 0.7773 | 0.7772 | 0.0534 | 0.0623 |
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+ | 0.1994 | 34.0 | 8500 | 0.6143 | 0.7853 | 0.3098 | 2.0188 | 0.7853 | 0.7857 | 0.0508 | 0.0611 |
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+ | 0.1994 | 35.0 | 8750 | 0.6096 | 0.7827 | 0.3086 | 2.0134 | 0.7828 | 0.7828 | 0.0512 | 0.0606 |
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+ | 0.1932 | 36.0 | 9000 | 0.6094 | 0.784 | 0.3067 | 2.0151 | 0.7840 | 0.7847 | 0.0471 | 0.0602 |
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+ | 0.1932 | 37.0 | 9250 | 0.6142 | 0.7833 | 0.3111 | 2.0213 | 0.7833 | 0.7829 | 0.0542 | 0.0608 |
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+ | 0.1895 | 38.0 | 9500 | 0.6103 | 0.7812 | 0.3094 | 2.0594 | 0.7812 | 0.7799 | 0.0529 | 0.0603 |
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+ | 0.1895 | 39.0 | 9750 | 0.6059 | 0.781 | 0.3078 | 2.0386 | 0.7810 | 0.7806 | 0.0545 | 0.0607 |
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+ | 0.1848 | 40.0 | 10000 | 0.6042 | 0.782 | 0.3072 | 2.0133 | 0.782 | 0.7824 | 0.0527 | 0.0603 |
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+ | 0.1848 | 41.0 | 10250 | 0.5991 | 0.785 | 0.3043 | 2.0124 | 0.785 | 0.7853 | 0.0496 | 0.0594 |
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+ | 0.1793 | 42.0 | 10500 | 0.6034 | 0.784 | 0.3058 | 2.0607 | 0.7840 | 0.7838 | 0.0490 | 0.0599 |
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+ | 0.1793 | 43.0 | 10750 | 0.6047 | 0.7827 | 0.3068 | 2.0139 | 0.7828 | 0.7819 | 0.0492 | 0.0595 |
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+ | 0.1768 | 44.0 | 11000 | 0.5982 | 0.785 | 0.3057 | 2.0303 | 0.785 | 0.7843 | 0.0473 | 0.0596 |
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+ | 0.1768 | 45.0 | 11250 | 0.6036 | 0.7795 | 0.3087 | 2.0173 | 0.7795 | 0.7788 | 0.0549 | 0.0607 |
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+ | 0.1743 | 46.0 | 11500 | 0.5974 | 0.785 | 0.3060 | 2.0026 | 0.785 | 0.7839 | 0.0478 | 0.0596 |
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+ | 0.1743 | 47.0 | 11750 | 0.5996 | 0.782 | 0.3068 | 2.0144 | 0.782 | 0.7825 | 0.0480 | 0.0598 |
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+ | 0.1707 | 48.0 | 12000 | 0.5958 | 0.7833 | 0.3079 | 2.0344 | 0.7833 | 0.7827 | 0.0500 | 0.0598 |
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+ | 0.1707 | 49.0 | 12250 | 0.5969 | 0.782 | 0.3060 | 2.0162 | 0.782 | 0.7820 | 0.0482 | 0.0597 |
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+ | 0.1683 | 50.0 | 12500 | 0.5933 | 0.784 | 0.3043 | 1.9897 | 0.7840 | 0.7836 | 0.0496 | 0.0589 |
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+ | 0.1683 | 51.0 | 12750 | 0.5935 | 0.7833 | 0.3042 | 2.0142 | 0.7833 | 0.7829 | 0.0501 | 0.0586 |
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+ | 0.1649 | 52.0 | 13000 | 0.5950 | 0.7847 | 0.3050 | 2.0125 | 0.7847 | 0.7851 | 0.0475 | 0.0591 |
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+ | 0.1649 | 53.0 | 13250 | 0.5904 | 0.7837 | 0.3020 | 1.9830 | 0.7837 | 0.7837 | 0.0504 | 0.0584 |
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+ | 0.1636 | 54.0 | 13500 | 0.5926 | 0.785 | 0.3042 | 2.0006 | 0.785 | 0.7845 | 0.0493 | 0.0588 |
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+ | 0.1636 | 55.0 | 13750 | 0.5885 | 0.7847 | 0.3029 | 2.0025 | 0.7847 | 0.7843 | 0.0505 | 0.0585 |
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+ | 0.1616 | 56.0 | 14000 | 0.5920 | 0.788 | 0.3041 | 2.0174 | 0.788 | 0.7878 | 0.0520 | 0.0591 |
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+ | 0.1616 | 57.0 | 14250 | 0.5927 | 0.7863 | 0.3033 | 2.0321 | 0.7863 | 0.7858 | 0.0521 | 0.0588 |
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+ | 0.1592 | 58.0 | 14500 | 0.5878 | 0.787 | 0.3017 | 1.9751 | 0.787 | 0.7874 | 0.0461 | 0.0584 |
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+ | 0.1592 | 59.0 | 14750 | 0.5888 | 0.7867 | 0.3030 | 1.9996 | 0.7868 | 0.7864 | 0.0494 | 0.0582 |
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+ | 0.1585 | 60.0 | 15000 | 0.5929 | 0.786 | 0.3052 | 2.0237 | 0.786 | 0.7857 | 0.0512 | 0.0584 |
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+ | 0.1585 | 61.0 | 15250 | 0.5894 | 0.7865 | 0.3026 | 1.9895 | 0.7865 | 0.7864 | 0.0548 | 0.0585 |
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+ | 0.1562 | 62.0 | 15500 | 0.5903 | 0.7873 | 0.3033 | 1.9670 | 0.7873 | 0.7870 | 0.0481 | 0.0584 |
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+ | 0.1562 | 63.0 | 15750 | 0.5896 | 0.7853 | 0.3023 | 1.9681 | 0.7853 | 0.7850 | 0.0520 | 0.0587 |
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+ | 0.1548 | 64.0 | 16000 | 0.5903 | 0.7847 | 0.3027 | 1.9865 | 0.7847 | 0.7846 | 0.0506 | 0.0587 |
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+ | 0.1548 | 65.0 | 16250 | 0.5910 | 0.7853 | 0.3039 | 2.0009 | 0.7853 | 0.7849 | 0.0515 | 0.0593 |
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+ | 0.1537 | 66.0 | 16500 | 0.5866 | 0.7883 | 0.3012 | 1.9561 | 0.7883 | 0.7881 | 0.0447 | 0.0581 |
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+ | 0.1537 | 67.0 | 16750 | 0.5858 | 0.7867 | 0.3009 | 1.9868 | 0.7868 | 0.7861 | 0.0486 | 0.0577 |
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+ | 0.1526 | 68.0 | 17000 | 0.5886 | 0.7867 | 0.3024 | 2.0009 | 0.7868 | 0.7862 | 0.0530 | 0.0587 |
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+ | 0.1526 | 69.0 | 17250 | 0.5850 | 0.7863 | 0.3010 | 2.0095 | 0.7863 | 0.7860 | 0.0510 | 0.0581 |
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+ | 0.1508 | 70.0 | 17500 | 0.5867 | 0.7865 | 0.3019 | 2.0304 | 0.7865 | 0.7861 | 0.0525 | 0.0583 |
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+ | 0.1508 | 71.0 | 17750 | 0.5895 | 0.7857 | 0.3038 | 2.0013 | 0.7857 | 0.7853 | 0.0478 | 0.0586 |
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+ | 0.15 | 72.0 | 18000 | 0.5894 | 0.7847 | 0.3025 | 2.0051 | 0.7847 | 0.7845 | 0.0500 | 0.0586 |
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+ | 0.15 | 73.0 | 18250 | 0.5867 | 0.7865 | 0.3022 | 1.9634 | 0.7865 | 0.7860 | 0.0489 | 0.0582 |
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+ | 0.149 | 74.0 | 18500 | 0.5888 | 0.7857 | 0.3026 | 1.9817 | 0.7857 | 0.7851 | 0.0497 | 0.0584 |
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+ | 0.149 | 75.0 | 18750 | 0.5823 | 0.7885 | 0.2994 | 1.9873 | 0.7885 | 0.7880 | 0.0476 | 0.0577 |
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+ | 0.1483 | 76.0 | 19000 | 0.5866 | 0.7853 | 0.3025 | 1.9870 | 0.7853 | 0.7849 | 0.0531 | 0.0583 |
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+ | 0.1483 | 77.0 | 19250 | 0.5866 | 0.7867 | 0.3013 | 1.9933 | 0.7868 | 0.7862 | 0.0498 | 0.0577 |
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+ | 0.1478 | 78.0 | 19500 | 0.5844 | 0.787 | 0.3010 | 1.9793 | 0.787 | 0.7868 | 0.0465 | 0.0579 |
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+ | 0.1478 | 79.0 | 19750 | 0.5850 | 0.7857 | 0.3005 | 1.9856 | 0.7857 | 0.7855 | 0.0489 | 0.0580 |
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+ | 0.1463 | 80.0 | 20000 | 0.5829 | 0.7893 | 0.2999 | 2.0003 | 0.7893 | 0.7890 | 0.0543 | 0.0578 |
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+ | 0.1463 | 81.0 | 20250 | 0.5845 | 0.7867 | 0.3011 | 2.0178 | 0.7868 | 0.7864 | 0.0494 | 0.0580 |
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+ | 0.1457 | 82.0 | 20500 | 0.5878 | 0.7865 | 0.3022 | 2.0108 | 0.7865 | 0.7861 | 0.0507 | 0.0583 |
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+ | 0.1457 | 83.0 | 20750 | 0.5862 | 0.7865 | 0.3016 | 1.9996 | 0.7865 | 0.7865 | 0.0505 | 0.0585 |
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+ | 0.1452 | 84.0 | 21000 | 0.5851 | 0.7863 | 0.3011 | 2.0002 | 0.7863 | 0.7859 | 0.0481 | 0.0582 |
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+ | 0.1452 | 85.0 | 21250 | 0.5850 | 0.787 | 0.3013 | 1.9659 | 0.787 | 0.7867 | 0.0524 | 0.0582 |
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+ | 0.1449 | 86.0 | 21500 | 0.5878 | 0.7867 | 0.3023 | 1.9837 | 0.7868 | 0.7866 | 0.0526 | 0.0581 |
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+ | 0.1449 | 87.0 | 21750 | 0.5844 | 0.7873 | 0.3010 | 1.9807 | 0.7873 | 0.7865 | 0.0522 | 0.0577 |
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+ | 0.1437 | 88.0 | 22000 | 0.5846 | 0.7877 | 0.3012 | 1.9947 | 0.7877 | 0.7869 | 0.0464 | 0.0580 |
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+ | 0.1437 | 89.0 | 22250 | 0.5859 | 0.787 | 0.3016 | 2.0002 | 0.787 | 0.7867 | 0.0503 | 0.0581 |
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+ | 0.143 | 90.0 | 22500 | 0.5838 | 0.7865 | 0.3010 | 1.9996 | 0.7865 | 0.7859 | 0.0496 | 0.0576 |
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+ | 0.143 | 91.0 | 22750 | 0.5843 | 0.7837 | 0.3011 | 1.9683 | 0.7837 | 0.7834 | 0.0501 | 0.0583 |
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+ | 0.1426 | 92.0 | 23000 | 0.5843 | 0.7873 | 0.3010 | 1.9960 | 0.7873 | 0.7870 | 0.0524 | 0.0578 |
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+ | 0.1426 | 93.0 | 23250 | 0.5827 | 0.7847 | 0.3005 | 1.9719 | 0.7847 | 0.7844 | 0.0506 | 0.0579 |
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+ | 0.1428 | 94.0 | 23500 | 0.5831 | 0.7865 | 0.3009 | 1.9781 | 0.7865 | 0.7862 | 0.0517 | 0.0579 |
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+ | 0.1428 | 95.0 | 23750 | 0.5821 | 0.784 | 0.3001 | 1.9641 | 0.7840 | 0.7838 | 0.0505 | 0.0579 |
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+ | 0.1424 | 96.0 | 24000 | 0.5850 | 0.7845 | 0.3020 | 1.9667 | 0.7845 | 0.7842 | 0.0526 | 0.0584 |
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+ | 0.1424 | 97.0 | 24250 | 0.5850 | 0.7847 | 0.3012 | 1.9776 | 0.7847 | 0.7844 | 0.0508 | 0.0579 |
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+ | 0.142 | 98.0 | 24500 | 0.5845 | 0.7877 | 0.3011 | 1.9745 | 0.7877 | 0.7870 | 0.0491 | 0.0579 |
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+ | 0.142 | 99.0 | 24750 | 0.5834 | 0.7853 | 0.3010 | 1.9679 | 0.7853 | 0.7852 | 0.0506 | 0.0581 |
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+ | 0.1416 | 100.0 | 25000 | 0.5837 | 0.7867 | 0.3013 | 1.9882 | 0.7868 | 0.7860 | 0.0529 | 0.0581 |
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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,
17
+ 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"
38
+ },
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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,
45
+ "handwritten": 3,
46
+ "invoice": 11,
47
+ "letter": 0,
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+ "memo": 15,
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+ "news_article": 9,
50
+ "presentation": 12,
51
+ "questionnaire": 13,
52
+ "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"
62
+ ],
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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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