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---
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t2.5_a0.7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t2.5_a0.7
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8012
- Accuracy: 0.7
- Brier Loss: 0.4467
- Nll: 2.5682
- F1 Micro: 0.7
- F1 Macro: 0.6313
- Ece: 0.2684
- Aurc: 0.1170
## 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 | 13 | 1.8024 | 0.16 | 0.8966 | 8.5001 | 0.16 | 0.1073 | 0.2079 | 0.8334 |
| No log | 2.0 | 26 | 1.7941 | 0.145 | 0.8957 | 8.3207 | 0.145 | 0.0843 | 0.2022 | 0.8435 |
| No log | 3.0 | 39 | 1.7486 | 0.2 | 0.8868 | 6.2015 | 0.2000 | 0.1007 | 0.2209 | 0.7900 |
| No log | 4.0 | 52 | 1.6854 | 0.205 | 0.8738 | 6.0142 | 0.205 | 0.0707 | 0.2453 | 0.7584 |
| No log | 5.0 | 65 | 1.6162 | 0.2 | 0.8594 | 6.2364 | 0.2000 | 0.0552 | 0.2466 | 0.7717 |
| No log | 6.0 | 78 | 1.5412 | 0.235 | 0.8416 | 6.0423 | 0.235 | 0.0902 | 0.2589 | 0.7006 |
| No log | 7.0 | 91 | 1.5011 | 0.295 | 0.8304 | 6.1420 | 0.295 | 0.1272 | 0.2803 | 0.6124 |
| No log | 8.0 | 104 | 1.4415 | 0.3 | 0.8114 | 6.0440 | 0.3 | 0.1296 | 0.2870 | 0.5641 |
| No log | 9.0 | 117 | 1.3257 | 0.38 | 0.7625 | 5.6923 | 0.38 | 0.2198 | 0.3136 | 0.3675 |
| No log | 10.0 | 130 | 1.3748 | 0.33 | 0.7905 | 5.5276 | 0.33 | 0.1870 | 0.2947 | 0.5985 |
| No log | 11.0 | 143 | 1.3294 | 0.39 | 0.7683 | 4.9632 | 0.39 | 0.2573 | 0.2940 | 0.4639 |
| No log | 12.0 | 156 | 1.2444 | 0.385 | 0.7297 | 4.8431 | 0.3850 | 0.2330 | 0.2849 | 0.4173 |
| No log | 13.0 | 169 | 1.2212 | 0.45 | 0.7153 | 4.5819 | 0.45 | 0.3051 | 0.3143 | 0.3379 |
| No log | 14.0 | 182 | 1.1835 | 0.495 | 0.6888 | 3.6108 | 0.495 | 0.3412 | 0.3316 | 0.2873 |
| No log | 15.0 | 195 | 1.1203 | 0.47 | 0.6559 | 3.6500 | 0.47 | 0.3348 | 0.2935 | 0.3061 |
| No log | 16.0 | 208 | 1.1520 | 0.495 | 0.6707 | 3.8106 | 0.495 | 0.3632 | 0.2938 | 0.3604 |
| No log | 17.0 | 221 | 1.0261 | 0.565 | 0.6021 | 3.3382 | 0.565 | 0.4214 | 0.2840 | 0.2047 |
| No log | 18.0 | 234 | 1.0080 | 0.61 | 0.5914 | 3.2936 | 0.61 | 0.4748 | 0.3240 | 0.1806 |
| No log | 19.0 | 247 | 1.0696 | 0.58 | 0.6253 | 3.2354 | 0.58 | 0.4686 | 0.3152 | 0.2626 |
| No log | 20.0 | 260 | 0.9733 | 0.615 | 0.5722 | 3.1019 | 0.615 | 0.4968 | 0.3259 | 0.2066 |
| No log | 21.0 | 273 | 0.9266 | 0.625 | 0.5423 | 3.0239 | 0.625 | 0.5202 | 0.2834 | 0.1782 |
| No log | 22.0 | 286 | 0.9364 | 0.66 | 0.5461 | 2.9031 | 0.66 | 0.5461 | 0.3128 | 0.1601 |
| No log | 23.0 | 299 | 0.9181 | 0.675 | 0.5307 | 2.8416 | 0.675 | 0.5584 | 0.3106 | 0.1462 |
| No log | 24.0 | 312 | 0.9739 | 0.665 | 0.5539 | 2.8798 | 0.665 | 0.5634 | 0.3325 | 0.1610 |
| No log | 25.0 | 325 | 0.8851 | 0.69 | 0.5099 | 2.7336 | 0.69 | 0.6013 | 0.3064 | 0.1437 |
| No log | 26.0 | 338 | 0.8755 | 0.71 | 0.4979 | 2.7400 | 0.7100 | 0.6032 | 0.3162 | 0.1211 |
| No log | 27.0 | 351 | 0.8653 | 0.675 | 0.4964 | 2.8339 | 0.675 | 0.5705 | 0.2977 | 0.1386 |
| No log | 28.0 | 364 | 0.8838 | 0.675 | 0.5055 | 2.7456 | 0.675 | 0.5816 | 0.2969 | 0.1524 |
| No log | 29.0 | 377 | 0.8805 | 0.68 | 0.5025 | 2.6942 | 0.68 | 0.5855 | 0.3099 | 0.1380 |
| No log | 30.0 | 390 | 0.8585 | 0.665 | 0.4891 | 2.7511 | 0.665 | 0.5737 | 0.2627 | 0.1370 |
| No log | 31.0 | 403 | 0.8410 | 0.675 | 0.4736 | 2.6431 | 0.675 | 0.5985 | 0.2670 | 0.1335 |
| No log | 32.0 | 416 | 0.8378 | 0.71 | 0.4724 | 2.7320 | 0.7100 | 0.6236 | 0.2885 | 0.1153 |
| No log | 33.0 | 429 | 0.8421 | 0.705 | 0.4718 | 2.6331 | 0.705 | 0.6326 | 0.2644 | 0.1147 |
| No log | 34.0 | 442 | 0.8350 | 0.685 | 0.4697 | 2.8035 | 0.685 | 0.6062 | 0.2831 | 0.1291 |
| No log | 35.0 | 455 | 0.8377 | 0.7 | 0.4708 | 2.4611 | 0.7 | 0.6376 | 0.3173 | 0.1195 |
| No log | 36.0 | 468 | 0.8126 | 0.69 | 0.4562 | 2.3909 | 0.69 | 0.6154 | 0.2433 | 0.1177 |
| No log | 37.0 | 481 | 0.8299 | 0.685 | 0.4673 | 2.5695 | 0.685 | 0.6080 | 0.2802 | 0.1261 |
| No log | 38.0 | 494 | 0.8197 | 0.685 | 0.4597 | 2.6388 | 0.685 | 0.6187 | 0.2690 | 0.1229 |
| 0.9314 | 39.0 | 507 | 0.8137 | 0.695 | 0.4547 | 2.7263 | 0.695 | 0.6332 | 0.2581 | 0.1207 |
| 0.9314 | 40.0 | 520 | 0.8168 | 0.69 | 0.4583 | 2.6230 | 0.69 | 0.6267 | 0.2696 | 0.1161 |
| 0.9314 | 41.0 | 533 | 0.8090 | 0.7 | 0.4529 | 2.6449 | 0.7 | 0.6236 | 0.2445 | 0.1187 |
| 0.9314 | 42.0 | 546 | 0.8168 | 0.68 | 0.4586 | 2.5516 | 0.68 | 0.6162 | 0.2722 | 0.1275 |
| 0.9314 | 43.0 | 559 | 0.8100 | 0.7 | 0.4523 | 2.5565 | 0.7 | 0.6347 | 0.2869 | 0.1192 |
| 0.9314 | 44.0 | 572 | 0.8078 | 0.7 | 0.4514 | 2.5734 | 0.7 | 0.6344 | 0.2583 | 0.1172 |
| 0.9314 | 45.0 | 585 | 0.8022 | 0.715 | 0.4472 | 2.4971 | 0.715 | 0.6534 | 0.2890 | 0.1165 |
| 0.9314 | 46.0 | 598 | 0.8049 | 0.695 | 0.4484 | 2.4891 | 0.695 | 0.6423 | 0.2722 | 0.1189 |
| 0.9314 | 47.0 | 611 | 0.8025 | 0.705 | 0.4481 | 2.4929 | 0.705 | 0.6393 | 0.2650 | 0.1124 |
| 0.9314 | 48.0 | 624 | 0.7973 | 0.7 | 0.4439 | 2.5000 | 0.7 | 0.6292 | 0.2718 | 0.1142 |
| 0.9314 | 49.0 | 637 | 0.8011 | 0.7 | 0.4464 | 2.5713 | 0.7 | 0.6303 | 0.2400 | 0.1183 |
| 0.9314 | 50.0 | 650 | 0.8012 | 0.7 | 0.4467 | 2.5682 | 0.7 | 0.6313 | 0.2684 | 0.1170 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
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