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--- |
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license: apache-2.0 |
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base_model: microsoft/resnet-101 |
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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 |
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results: [] |
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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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# resnet101_rvl-cdip |
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This model is a fine-tuned version of [microsoft/resnet-101](https://huggingface.co/microsoft/resnet-101) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6158 |
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- Accuracy: 0.8210 |
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- Brier Loss: 0.2556 |
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- Nll: 1.7696 |
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- F1 Micro: 0.8210 |
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- F1 Macro: 0.8209 |
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- Ece: 0.0176 |
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- Aurc: 0.0418 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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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: 10 |
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### Training results |
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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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| 1.3521 | 1.0 | 5000 | 1.2626 | 0.6133 | 0.5108 | 2.7262 | 0.6133 | 0.6042 | 0.0455 | 0.1644 | |
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| 0.942 | 2.0 | 10000 | 0.9005 | 0.7318 | 0.3723 | 2.2139 | 0.7318 | 0.7293 | 0.0174 | 0.0862 | |
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| 0.7983 | 3.0 | 15000 | 0.7691 | 0.7723 | 0.3198 | 2.0444 | 0.7723 | 0.7714 | 0.0139 | 0.0641 | |
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| 0.7167 | 4.0 | 20000 | 0.7048 | 0.7924 | 0.2931 | 1.9414 | 0.7924 | 0.7931 | 0.0135 | 0.0541 | |
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| 0.6656 | 5.0 | 25000 | 0.6658 | 0.8052 | 0.2770 | 1.8581 | 0.8052 | 0.8056 | 0.0108 | 0.0486 | |
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| 0.6252 | 6.0 | 30000 | 0.6415 | 0.8117 | 0.2670 | 1.8157 | 0.8117 | 0.8112 | 0.0128 | 0.0455 | |
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| 0.6038 | 7.0 | 35000 | 0.6269 | 0.8176 | 0.2607 | 1.7833 | 0.8176 | 0.8180 | 0.0144 | 0.0432 | |
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| 0.5784 | 8.0 | 40000 | 0.6217 | 0.8195 | 0.2583 | 1.7723 | 0.8195 | 0.8195 | 0.0151 | 0.0425 | |
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| 0.5583 | 9.0 | 45000 | 0.6150 | 0.8214 | 0.2553 | 1.7719 | 0.8214 | 0.8214 | 0.0164 | 0.0415 | |
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| 0.5519 | 10.0 | 50000 | 0.6158 | 0.8210 | 0.2556 | 1.7696 | 0.8210 | 0.8209 | 0.0176 | 0.0418 | |
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### Framework versions |
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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 |
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