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# resnet50-cocoa

This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the SemilleroCV/Cocoa-dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3381
- Accuracy: 0.8917

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 100.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3793        | 1.0   | 196   | 1.4452          | 0.8628   |
| 0.9417        | 2.0   | 392   | 1.0832          | 0.8628   |
| 0.8546        | 3.0   | 588   | 0.7324          | 0.8628   |
| 0.6067        | 4.0   | 784   | 0.5761          | 0.8628   |
| 0.5583        | 5.0   | 980   | 0.5221          | 0.8628   |
| 0.6819        | 6.0   | 1176  | 0.4618          | 0.8628   |
| 0.4154        | 7.0   | 1372  | 0.4545          | 0.8628   |
| 0.4997        | 8.0   | 1568  | 0.4556          | 0.8628   |
| 0.6623        | 9.0   | 1764  | 0.4483          | 0.8628   |
| 0.8141        | 10.0  | 1960  | 0.4494          | 0.8628   |
| 0.5514        | 11.0  | 2156  | 0.4437          | 0.8628   |
| 0.6831        | 12.0  | 2352  | 0.4407          | 0.8664   |
| 0.2799        | 13.0  | 2548  | 0.4459          | 0.8700   |
| 0.451         | 14.0  | 2744  | 0.4313          | 0.8809   |
| 0.3901        | 15.0  | 2940  | 0.4340          | 0.8845   |
| 0.4778        | 16.0  | 3136  | 0.4219          | 0.8845   |
| 0.5531        | 17.0  | 3332  | 0.4304          | 0.8845   |
| 0.4904        | 18.0  | 3528  | 0.4429          | 0.8845   |
| 0.5398        | 19.0  | 3724  | 0.4144          | 0.8917   |
| 0.8024        | 20.0  | 3920  | 0.4253          | 0.8881   |
| 0.7022        | 21.0  | 4116  | 0.4232          | 0.8917   |
| 0.3868        | 22.0  | 4312  | 0.4167          | 0.8917   |
| 0.4075        | 23.0  | 4508  | 0.3917          | 0.8917   |
| 0.3873        | 24.0  | 4704  | 0.4269          | 0.8881   |
| 0.2382        | 25.0  | 4900  | 0.3913          | 0.8845   |
| 0.6525        | 26.0  | 5096  | 0.3949          | 0.8881   |
| 0.3207        | 27.0  | 5292  | 0.3967          | 0.8881   |
| 0.4569        | 28.0  | 5488  | 0.3901          | 0.8845   |
| 0.6184        | 29.0  | 5684  | 0.4114          | 0.8917   |
| 0.6055        | 30.0  | 5880  | 0.4342          | 0.8881   |
| 0.47          | 31.0  | 6076  | 0.4071          | 0.8917   |
| 0.3507        | 32.0  | 6272  | 0.3838          | 0.8881   |
| 0.4888        | 33.0  | 6468  | 0.4006          | 0.8881   |
| 0.4276        | 34.0  | 6664  | 0.3909          | 0.8881   |
| 0.5371        | 35.0  | 6860  | 0.4238          | 0.8917   |
| 0.4826        | 36.0  | 7056  | 0.3843          | 0.8917   |
| 0.5119        | 37.0  | 7252  | 0.3747          | 0.8845   |
| 0.4192        | 38.0  | 7448  | 0.4232          | 0.8881   |
| 1.1545        | 39.0  | 7644  | 0.4415          | 0.8881   |
| 0.3206        | 40.0  | 7840  | 0.3937          | 0.8881   |
| 0.3464        | 41.0  | 8036  | 0.3678          | 0.8881   |
| 0.4016        | 42.0  | 8232  | 0.3849          | 0.8881   |
| 0.2037        | 43.0  | 8428  | 0.3487          | 0.8881   |
| 0.3795        | 44.0  | 8624  | 0.4298          | 0.8881   |
| 0.403         | 45.0  | 8820  | 0.3966          | 0.8881   |
| 0.2754        | 46.0  | 9016  | 0.3785          | 0.8845   |
| 0.5228        | 47.0  | 9212  | 0.4117          | 0.8881   |
| 0.7263        | 48.0  | 9408  | 0.3726          | 0.8845   |
| 0.8995        | 49.0  | 9604  | 0.4559          | 0.8917   |
| 0.6844        | 50.0  | 9800  | 0.4164          | 0.8881   |
| 0.2734        | 51.0  | 9996  | 0.3862          | 0.8881   |
| 0.4179        | 52.0  | 10192 | 0.4386          | 0.8917   |
| 0.3354        | 53.0  | 10388 | 0.3949          | 0.8881   |
| 0.7031        | 54.0  | 10584 | 0.3910          | 0.8881   |
| 0.586         | 55.0  | 10780 | 0.4216          | 0.8881   |
| 0.3601        | 56.0  | 10976 | 0.4545          | 0.8881   |
| 0.362         | 57.0  | 11172 | 0.3760          | 0.8845   |
| 0.6132        | 58.0  | 11368 | 0.4258          | 0.8881   |
| 0.5605        | 59.0  | 11564 | 0.3972          | 0.8881   |
| 0.5071        | 60.0  | 11760 | 0.3873          | 0.8917   |
| 0.458         | 61.0  | 11956 | 0.4098          | 0.8881   |
| 0.4401        | 62.0  | 12152 | 0.3859          | 0.8845   |
| 0.5439        | 63.0  | 12348 | 0.4142          | 0.8917   |
| 0.6099        | 64.0  | 12544 | 0.3970          | 0.8881   |
| 0.2749        | 65.0  | 12740 | 0.3656          | 0.8809   |
| 0.581         | 66.0  | 12936 | 0.4203          | 0.8881   |
| 0.6009        | 67.0  | 13132 | 0.4074          | 0.8917   |
| 0.2388        | 68.0  | 13328 | 0.3594          | 0.8845   |
| 0.6006        | 69.0  | 13524 | 0.4045          | 0.8845   |
| 0.388         | 70.0  | 13720 | 0.3717          | 0.8881   |
| 0.552         | 71.0  | 13916 | 0.4239          | 0.8881   |
| 0.3875        | 72.0  | 14112 | 0.3731          | 0.8881   |
| 0.3105        | 73.0  | 14308 | 0.3434          | 0.8845   |
| 0.4627        | 74.0  | 14504 | 0.3946          | 0.8881   |
| 0.2931        | 75.0  | 14700 | 0.3950          | 0.8845   |
| 0.4639        | 76.0  | 14896 | 0.3875          | 0.8881   |
| 0.3534        | 77.0  | 15092 | 0.4009          | 0.8881   |
| 0.3175        | 78.0  | 15288 | 0.4109          | 0.8881   |
| 0.5334        | 79.0  | 15484 | 0.3918          | 0.8881   |
| 0.4827        | 80.0  | 15680 | 0.3807          | 0.8881   |
| 0.5162        | 81.0  | 15876 | 0.3624          | 0.8845   |
| 0.4377        | 82.0  | 16072 | 0.3729          | 0.8881   |
| 0.4487        | 83.0  | 16268 | 0.3981          | 0.8917   |
| 0.5057        | 84.0  | 16464 | 0.3995          | 0.8917   |
| 0.3421        | 85.0  | 16660 | 0.3554          | 0.8881   |
| 0.4083        | 86.0  | 16856 | 0.3634          | 0.8845   |
| 0.7634        | 87.0  | 17052 | 0.3970          | 0.8881   |
| 0.2588        | 88.0  | 17248 | 0.4121          | 0.8917   |
| 0.1584        | 89.0  | 17444 | 0.3711          | 0.8881   |
| 0.2643        | 90.0  | 17640 | 0.3743          | 0.8881   |
| 0.2771        | 91.0  | 17836 | 0.3726          | 0.8881   |
| 0.336         | 92.0  | 18032 | 0.3758          | 0.8845   |
| 0.3283        | 93.0  | 18228 | 0.4397          | 0.8917   |
| 0.7224        | 94.0  | 18424 | 0.3869          | 0.8917   |
| 0.1575        | 95.0  | 18620 | 0.3381          | 0.8917   |
| 0.4062        | 96.0  | 18816 | 0.3684          | 0.8845   |
| 0.3849        | 97.0  | 19012 | 0.3887          | 0.8881   |
| 0.2755        | 98.0  | 19208 | 0.3725          | 0.8881   |
| 0.4952        | 99.0  | 19404 | 0.4137          | 0.8917   |
| 0.3807        | 100.0 | 19600 | 0.3923          | 0.8881   |


### Framework versions

- Transformers 4.48.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0

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