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# resnet-50-cocoa

This model is a fine-tuned version of [google/efficientnet-b0](https://huggingface.co/google/efficientnet-b0) on the SemilleroCV/Cocoa-dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2657
- Accuracy: 0.9097

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0627        | 1.0   | 196   | 1.5223          | 0.5596   |
| 0.591         | 2.0   | 392   | 0.8975          | 0.8303   |
| 0.6623        | 3.0   | 588   | 0.6564          | 0.8773   |
| 0.4874        | 4.0   | 784   | 0.6842          | 0.8339   |
| 0.4671        | 5.0   | 980   | 0.4894          | 0.8809   |
| 0.5623        | 6.0   | 1176  | 0.4160          | 0.8736   |
| 0.3917        | 7.0   | 1372  | 0.4022          | 0.8845   |
| 0.3153        | 8.0   | 1568  | 0.4939          | 0.8412   |
| 0.5814        | 9.0   | 1764  | 0.3540          | 0.8773   |
| 0.5883        | 10.0  | 1960  | 0.3493          | 0.8953   |
| 0.4616        | 11.0  | 2156  | 0.7928          | 0.7762   |
| 0.499         | 12.0  | 2352  | 2.0659          | 0.2960   |
| 0.2236        | 13.0  | 2548  | 0.4444          | 0.8520   |
| 0.2083        | 14.0  | 2744  | 0.4640          | 0.8736   |
| 0.3408        | 15.0  | 2940  | 0.3775          | 0.8773   |
| 0.3529        | 16.0  | 3136  | 0.3519          | 0.8881   |
| 0.3859        | 17.0  | 3332  | 0.3310          | 0.9061   |
| 0.3557        | 18.0  | 3528  | 0.3475          | 0.8917   |
| 0.4979        | 19.0  | 3724  | 0.3839          | 0.8592   |
| 0.7133        | 20.0  | 3920  | 0.3032          | 0.9134   |
| 0.4489        | 21.0  | 4116  | 0.4246          | 0.8520   |
| 0.2605        | 22.0  | 4312  | 0.2951          | 0.8989   |
| 0.3787        | 23.0  | 4508  | 0.4357          | 0.8520   |
| 0.3015        | 24.0  | 4704  | 0.3990          | 0.8917   |
| 0.1965        | 25.0  | 4900  | 0.3536          | 0.9097   |
| 0.3903        | 26.0  | 5096  | 0.4166          | 0.8592   |
| 0.1902        | 27.0  | 5292  | 0.4354          | 0.8520   |
| 0.2089        | 28.0  | 5488  | 0.4089          | 0.8592   |
| 0.3574        | 29.0  | 5684  | 0.4787          | 0.8231   |
| 0.3532        | 30.0  | 5880  | 0.3165          | 0.9097   |
| 0.2967        | 31.0  | 6076  | 0.3105          | 0.9134   |
| 0.2364        | 32.0  | 6272  | 0.3560          | 0.9061   |
| 0.3136        | 33.0  | 6468  | 0.2657          | 0.9097   |
| 0.4061        | 34.0  | 6664  | 0.2680          | 0.9134   |
| 0.3296        | 35.0  | 6860  | 0.3798          | 0.9061   |
| 0.2905        | 36.0  | 7056  | 0.5098          | 0.8556   |
| 0.2763        | 37.0  | 7252  | 0.4219          | 0.8809   |
| 0.2454        | 38.0  | 7448  | 0.2852          | 0.9134   |
| 0.6077        | 39.0  | 7644  | 0.3603          | 0.8989   |
| 0.1966        | 40.0  | 7840  | 0.3519          | 0.8736   |
| 0.2473        | 41.0  | 8036  | 0.3343          | 0.9025   |
| 0.2795        | 42.0  | 8232  | 0.3384          | 0.9170   |
| 0.1249        | 43.0  | 8428  | 0.4046          | 0.8773   |
| 0.2943        | 44.0  | 8624  | 0.3953          | 0.8917   |
| 0.3002        | 45.0  | 8820  | 0.5003          | 0.8592   |
| 0.1525        | 46.0  | 9016  | 0.3232          | 0.9170   |
| 0.4022        | 47.0  | 9212  | 0.3113          | 0.9170   |
| 0.4994        | 48.0  | 9408  | 0.4494          | 0.8556   |
| 0.6512        | 49.0  | 9604  | 0.3722          | 0.9206   |
| 0.3152        | 50.0  | 9800  | 0.2852          | 0.9097   |
| 0.1165        | 51.0  | 9996  | 0.4138          | 0.8628   |
| 0.216         | 52.0  | 10192 | 0.3413          | 0.8953   |
| 0.1455        | 53.0  | 10388 | 0.3046          | 0.9170   |
| 0.554         | 54.0  | 10584 | 0.2849          | 0.8989   |
| 0.3586        | 55.0  | 10780 | 0.3517          | 0.9134   |
| 0.2239        | 56.0  | 10976 | 0.4538          | 0.9025   |
| 0.1725        | 57.0  | 11172 | 0.4492          | 0.8592   |
| 0.4689        | 58.0  | 11368 | 0.4739          | 0.8628   |
| 0.3565        | 59.0  | 11564 | 0.2831          | 0.9206   |
| 0.2259        | 60.0  | 11760 | 0.3465          | 0.9206   |
| 0.2212        | 61.0  | 11956 | 0.2884          | 0.9314   |
| 0.2648        | 62.0  | 12152 | 0.4875          | 0.8448   |
| 0.3438        | 63.0  | 12348 | 0.3989          | 0.9061   |
| 0.4785        | 64.0  | 12544 | 0.5953          | 0.8520   |
| 0.06          | 65.0  | 12740 | 0.2954          | 0.9278   |
| 0.1965        | 66.0  | 12936 | 0.5033          | 0.8520   |
| 0.3548        | 67.0  | 13132 | 0.4132          | 0.8809   |
| 0.1279        | 68.0  | 13328 | 0.3743          | 0.9170   |
| 0.2879        | 69.0  | 13524 | 0.6423          | 0.7762   |
| 0.1757        | 70.0  | 13720 | 0.5979          | 0.8014   |
| 0.3338        | 71.0  | 13916 | 0.4398          | 0.8989   |
| 0.1604        | 72.0  | 14112 | 0.5634          | 0.8231   |
| 0.1078        | 73.0  | 14308 | 0.6204          | 0.7762   |
| 0.258         | 74.0  | 14504 | 0.3685          | 0.8953   |
| 0.1227        | 75.0  | 14700 | 0.7026          | 0.8159   |
| 0.2257        | 76.0  | 14896 | 0.4048          | 0.9170   |
| 0.1786        | 77.0  | 15092 | 0.4891          | 0.8845   |
| 0.2006        | 78.0  | 15288 | 0.4216          | 0.8773   |
| 0.3144        | 79.0  | 15484 | 0.2721          | 0.8953   |
| 0.1969        | 80.0  | 15680 | 0.4270          | 0.8484   |
| 0.1405        | 81.0  | 15876 | 0.7632          | 0.7834   |
| 0.1427        | 82.0  | 16072 | 0.3249          | 0.9025   |
| 0.2493        | 83.0  | 16268 | 0.3838          | 0.8989   |
| 0.331         | 84.0  | 16464 | 0.3330          | 0.9206   |
| 0.1231        | 85.0  | 16660 | 0.3246          | 0.8700   |
| 0.2781        | 86.0  | 16856 | 0.3710          | 0.8736   |
| 0.7193        | 87.0  | 17052 | 0.3384          | 0.9061   |
| 0.1149        | 88.0  | 17248 | 0.3703          | 0.9097   |
| 0.0269        | 89.0  | 17444 | 0.5013          | 0.8592   |
| 0.0967        | 90.0  | 17640 | 0.3456          | 0.8989   |
| 0.177         | 91.0  | 17836 | 0.3799          | 0.8881   |
| 0.1917        | 92.0  | 18032 | 0.3239          | 0.9061   |
| 0.2082        | 93.0  | 18228 | 0.4861          | 0.8989   |
| 0.3836        | 94.0  | 18424 | 0.4444          | 0.8736   |
| 0.1           | 95.0  | 18620 | 0.3713          | 0.8845   |
| 0.1785        | 96.0  | 18816 | 0.4279          | 0.8303   |
| 0.19          | 97.0  | 19012 | 0.6588          | 0.8412   |
| 0.099         | 98.0  | 19208 | 0.6632          | 0.8267   |
| 0.1467        | 99.0  | 19404 | 0.4642          | 0.8809   |
| 0.2617        | 100.0 | 19600 | 0.3624          | 0.8809   |


### Framework versions

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

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