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# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.5_a0.9

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: 0.8831
- Accuracy: 0.695
- Brier Loss: 0.4126
- Nll: 2.4628
- F1 Micro: 0.695
- F1 Macro: 0.6387
- Ece: 0.2426
- Aurc: 0.1068

## 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   | 2.1233          | 0.16     | 0.8967     | 8.5697 | 0.16     | 0.1066   | 0.2078 | 0.8316 |
| No log        | 2.0   | 26   | 2.1188          | 0.14     | 0.8961     | 8.2960 | 0.14     | 0.0886   | 0.1947 | 0.8419 |
| No log        | 3.0   | 39   | 2.0764          | 0.195    | 0.8873     | 6.4713 | 0.195    | 0.1159   | 0.2335 | 0.7665 |
| No log        | 4.0   | 52   | 2.0365          | 0.21     | 0.8787     | 5.7752 | 0.2100   | 0.0930   | 0.2376 | 0.7548 |
| No log        | 5.0   | 65   | 1.9888          | 0.2      | 0.8682     | 5.8737 | 0.2000   | 0.0775   | 0.2417 | 0.7314 |
| No log        | 6.0   | 78   | 1.8998          | 0.215    | 0.8465     | 5.8553 | 0.2150   | 0.0970   | 0.2586 | 0.7063 |
| No log        | 7.0   | 91   | 1.8351          | 0.33     | 0.8289     | 5.7781 | 0.33     | 0.1904   | 0.3089 | 0.6103 |
| No log        | 8.0   | 104  | 1.7342          | 0.4      | 0.7968     | 5.5366 | 0.4000   | 0.2476   | 0.3457 | 0.4276 |
| No log        | 9.0   | 117  | 1.6787          | 0.36     | 0.7757     | 5.7414 | 0.36     | 0.2148   | 0.3062 | 0.4324 |
| No log        | 10.0  | 130  | 1.6942          | 0.4      | 0.7870     | 5.2615 | 0.4000   | 0.2831   | 0.3168 | 0.5227 |
| No log        | 11.0  | 143  | 1.5992          | 0.4      | 0.7489     | 4.7833 | 0.4000   | 0.2649   | 0.3053 | 0.4679 |
| No log        | 12.0  | 156  | 1.6071          | 0.425    | 0.7532     | 4.2803 | 0.425    | 0.2906   | 0.3196 | 0.4646 |
| No log        | 13.0  | 169  | 1.4727          | 0.48     | 0.6925     | 4.1911 | 0.48     | 0.3239   | 0.2957 | 0.3081 |
| No log        | 14.0  | 182  | 1.4275          | 0.515    | 0.6705     | 3.7980 | 0.515    | 0.3569   | 0.3211 | 0.2626 |
| No log        | 15.0  | 195  | 1.3282          | 0.56     | 0.6200     | 3.6359 | 0.56     | 0.4163   | 0.2990 | 0.2213 |
| No log        | 16.0  | 208  | 1.3280          | 0.565    | 0.6263     | 3.4960 | 0.565    | 0.4177   | 0.3217 | 0.2346 |
| No log        | 17.0  | 221  | 1.3220          | 0.595    | 0.6196     | 3.2202 | 0.595    | 0.4639   | 0.3322 | 0.1992 |
| No log        | 18.0  | 234  | 1.2359          | 0.595    | 0.5840     | 3.3332 | 0.595    | 0.4780   | 0.3042 | 0.2011 |
| No log        | 19.0  | 247  | 1.1690          | 0.625    | 0.5531     | 3.2423 | 0.625    | 0.5233   | 0.2940 | 0.1807 |
| No log        | 20.0  | 260  | 1.1644          | 0.64     | 0.5532     | 3.0542 | 0.64     | 0.5429   | 0.3019 | 0.1821 |
| No log        | 21.0  | 273  | 1.1611          | 0.62     | 0.5516     | 2.9412 | 0.62     | 0.5193   | 0.2865 | 0.2160 |
| No log        | 22.0  | 286  | 1.3427          | 0.585    | 0.6361     | 3.0936 | 0.585    | 0.5089   | 0.3442 | 0.2922 |
| No log        | 23.0  | 299  | 1.1238          | 0.62     | 0.5440     | 2.7924 | 0.62     | 0.5458   | 0.2654 | 0.2088 |
| No log        | 24.0  | 312  | 1.2008          | 0.685    | 0.5615     | 2.5918 | 0.685    | 0.5890   | 0.3907 | 0.1516 |
| No log        | 25.0  | 325  | 1.0764          | 0.695    | 0.5000     | 2.6354 | 0.695    | 0.6107   | 0.3126 | 0.1397 |
| No log        | 26.0  | 338  | 1.0268          | 0.675    | 0.4822     | 2.4798 | 0.675    | 0.5992   | 0.2775 | 0.1229 |
| No log        | 27.0  | 351  | 1.0340          | 0.67     | 0.4893     | 2.4316 | 0.67     | 0.5997   | 0.2763 | 0.1638 |
| No log        | 28.0  | 364  | 1.0154          | 0.665    | 0.4769     | 2.6487 | 0.665    | 0.6034   | 0.2590 | 0.1487 |
| No log        | 29.0  | 377  | 1.0013          | 0.64     | 0.4814     | 2.5899 | 0.64     | 0.5771   | 0.2429 | 0.1593 |
| No log        | 30.0  | 390  | 1.0173          | 0.685    | 0.4714     | 2.6922 | 0.685    | 0.6178   | 0.2898 | 0.1423 |
| No log        | 31.0  | 403  | 0.9630          | 0.695    | 0.4509     | 2.6349 | 0.695    | 0.6206   | 0.2746 | 0.1248 |
| No log        | 32.0  | 416  | 0.9950          | 0.68     | 0.4648     | 2.4144 | 0.68     | 0.6362   | 0.3020 | 0.1725 |
| No log        | 33.0  | 429  | 0.9711          | 0.72     | 0.4502     | 2.6651 | 0.72     | 0.6571   | 0.2892 | 0.1268 |
| No log        | 34.0  | 442  | 0.9491          | 0.705    | 0.4425     | 2.7169 | 0.705    | 0.6425   | 0.2541 | 0.1145 |
| No log        | 35.0  | 455  | 0.9213          | 0.685    | 0.4309     | 2.5736 | 0.685    | 0.6174   | 0.2380 | 0.1161 |
| No log        | 36.0  | 468  | 0.9144          | 0.695    | 0.4297     | 2.4141 | 0.695    | 0.6308   | 0.2502 | 0.1154 |
| No log        | 37.0  | 481  | 0.9242          | 0.715    | 0.4264     | 2.7191 | 0.715    | 0.6429   | 0.2386 | 0.1030 |
| No log        | 38.0  | 494  | 0.9290          | 0.695    | 0.4346     | 2.6515 | 0.695    | 0.6367   | 0.2432 | 0.1189 |
| 1.0953        | 39.0  | 507  | 0.9110          | 0.69     | 0.4262     | 2.6615 | 0.69     | 0.6328   | 0.2368 | 0.1112 |
| 1.0953        | 40.0  | 520  | 0.9000          | 0.695    | 0.4186     | 2.4590 | 0.695    | 0.6417   | 0.2453 | 0.1070 |
| 1.0953        | 41.0  | 533  | 0.8961          | 0.69     | 0.4189     | 2.4170 | 0.69     | 0.6368   | 0.2349 | 0.1090 |
| 1.0953        | 42.0  | 546  | 0.9103          | 0.675    | 0.4286     | 2.6129 | 0.675    | 0.6193   | 0.2318 | 0.1190 |
| 1.0953        | 43.0  | 559  | 0.8858          | 0.715    | 0.4131     | 2.5243 | 0.715    | 0.6517   | 0.2462 | 0.1053 |
| 1.0953        | 44.0  | 572  | 0.8872          | 0.705    | 0.4135     | 2.3272 | 0.705    | 0.6542   | 0.2596 | 0.1051 |
| 1.0953        | 45.0  | 585  | 0.8897          | 0.715    | 0.4136     | 2.3788 | 0.715    | 0.6532   | 0.2560 | 0.1035 |
| 1.0953        | 46.0  | 598  | 0.8842          | 0.7      | 0.4117     | 2.5325 | 0.7      | 0.6446   | 0.2327 | 0.1075 |
| 1.0953        | 47.0  | 611  | 0.8857          | 0.675    | 0.4141     | 2.5451 | 0.675    | 0.6203   | 0.2473 | 0.1125 |
| 1.0953        | 48.0  | 624  | 0.8875          | 0.69     | 0.4164     | 2.4696 | 0.69     | 0.6352   | 0.2542 | 0.1109 |
| 1.0953        | 49.0  | 637  | 0.8842          | 0.69     | 0.4153     | 2.5338 | 0.69     | 0.6358   | 0.2302 | 0.1112 |
| 1.0953        | 50.0  | 650  | 0.8831          | 0.695    | 0.4126     | 2.4628 | 0.695    | 0.6387   | 0.2426 | 0.1068 |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "microsoft/resnet-50", "model-index": [{"name": "resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.5_a0.9", "results": []}]}
transformers pytorch resnet image-classification generated_from_trainer base_model:microsoft/resnet-50 license:apache-2.0 autotrain_compatible endpoints_compatible region:us