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# mobilenetv2-cocoa

This model is a fine-tuned version of [google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobilenet_v2_1.0_224) on the SemilleroCV/Cocoa-dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3226
- Accuracy: 0.8953

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.569         | 1.0   | 196   | 0.5072          | 0.8628   |
| 0.3973        | 2.0   | 392   | 0.4278          | 0.8700   |
| 0.5873        | 3.0   | 588   | 0.4138          | 0.8773   |
| 0.4781        | 4.0   | 784   | 0.4718          | 0.8736   |
| 0.4483        | 5.0   | 980   | 0.4506          | 0.8773   |
| 0.655         | 6.0   | 1176  | 0.3685          | 0.8953   |
| 0.3441        | 7.0   | 1372  | 0.4751          | 0.8773   |
| 0.3166        | 8.0   | 1568  | 0.3796          | 0.8809   |
| 0.5114        | 9.0   | 1764  | 0.4087          | 0.8917   |
| 0.6452        | 10.0  | 1960  | 0.3760          | 0.8989   |
| 0.4747        | 11.0  | 2156  | 0.4223          | 0.8773   |
| 0.5145        | 12.0  | 2352  | 1.1704          | 0.5957   |
| 0.1991        | 13.0  | 2548  | 0.3454          | 0.9097   |
| 0.2396        | 14.0  | 2744  | 0.3913          | 0.8700   |
| 0.3259        | 15.0  | 2940  | 0.3689          | 0.8881   |
| 0.3434        | 16.0  | 3136  | 0.3743          | 0.8736   |
| 0.389         | 17.0  | 3332  | 0.3657          | 0.9025   |
| 0.302         | 18.0  | 3528  | 0.4218          | 0.8917   |
| 0.4693        | 19.0  | 3724  | 0.3226          | 0.8953   |
| 0.6346        | 20.0  | 3920  | 0.3277          | 0.8881   |
| 0.481         | 21.0  | 4116  | 0.3484          | 0.8700   |
| 0.2628        | 22.0  | 4312  | 0.3942          | 0.9025   |
| 0.3653        | 23.0  | 4508  | 0.3537          | 0.8989   |
| 0.344         | 24.0  | 4704  | 0.4758          | 0.8809   |
| 0.2819        | 25.0  | 4900  | 0.4318          | 0.8989   |
| 0.513         | 26.0  | 5096  | 0.4277          | 0.8412   |
| 0.201         | 27.0  | 5292  | 0.3915          | 0.8953   |
| 0.2696        | 28.0  | 5488  | 0.4401          | 0.8809   |
| 0.4204        | 29.0  | 5684  | 0.3856          | 0.8953   |
| 0.316         | 30.0  | 5880  | 0.3576          | 0.8845   |
| 0.3102        | 31.0  | 6076  | 0.4155          | 0.8809   |
| 0.1489        | 32.0  | 6272  | 0.4147          | 0.8953   |
| 0.3302        | 33.0  | 6468  | 0.4217          | 0.8953   |
| 0.3271        | 34.0  | 6664  | 0.3321          | 0.9097   |
| 0.3481        | 35.0  | 6860  | 0.3828          | 0.8809   |
| 0.3329        | 36.0  | 7056  | 0.4045          | 0.8700   |
| 0.2471        | 37.0  | 7252  | 0.5536          | 0.8664   |
| 0.2007        | 38.0  | 7448  | 0.3503          | 0.8881   |
| 0.7535        | 39.0  | 7644  | 0.4819          | 0.8809   |
| 0.1851        | 40.0  | 7840  | 0.3762          | 0.8773   |
| 0.2329        | 41.0  | 8036  | 0.4465          | 0.8845   |
| 0.2889        | 42.0  | 8232  | 0.4696          | 0.9061   |
| 0.1409        | 43.0  | 8428  | 0.4876          | 0.8809   |
| 0.2683        | 44.0  | 8624  | 0.6134          | 0.8809   |
| 0.3535        | 45.0  | 8820  | 0.4364          | 0.8809   |
| 0.1683        | 46.0  | 9016  | 0.4059          | 0.8881   |
| 0.43          | 47.0  | 9212  | 0.3955          | 0.8881   |
| 0.5702        | 48.0  | 9408  | 0.3898          | 0.8809   |
| 0.8043        | 49.0  | 9604  | 0.5963          | 0.8953   |
| 0.3742        | 50.0  | 9800  | 0.5273          | 0.8989   |
| 0.1026        | 51.0  | 9996  | 0.3999          | 0.8989   |
| 0.2357        | 52.0  | 10192 | 0.4724          | 0.8592   |
| 0.2612        | 53.0  | 10388 | 0.4169          | 0.8845   |
| 0.4747        | 54.0  | 10584 | 0.3973          | 0.8917   |
| 0.4943        | 55.0  | 10780 | 0.5156          | 0.9061   |
| 0.2296        | 56.0  | 10976 | 0.6397          | 0.8917   |
| 0.1789        | 57.0  | 11172 | 0.5098          | 0.8267   |
| 0.4355        | 58.0  | 11368 | 0.5032          | 0.8917   |
| 0.3957        | 59.0  | 11564 | 0.4205          | 0.9025   |
| 0.4806        | 60.0  | 11760 | 0.7011          | 0.8917   |
| 0.2356        | 61.0  | 11956 | 0.7832          | 0.8881   |
| 0.3865        | 62.0  | 12152 | 0.4622          | 0.8917   |
| 0.3504        | 63.0  | 12348 | 0.5889          | 0.8773   |
| 0.3766        | 64.0  | 12544 | 0.5246          | 0.8592   |
| 0.1336        | 65.0  | 12740 | 0.6462          | 0.8773   |
| 0.3275        | 66.0  | 12936 | 0.5013          | 0.8628   |
| 0.3765        | 67.0  | 13132 | 0.4857          | 0.8953   |
| 0.1622        | 68.0  | 13328 | 0.4918          | 0.8845   |
| 0.2291        | 69.0  | 13524 | 0.5734          | 0.8736   |
| 0.1786        | 70.0  | 13720 | 0.6691          | 0.8231   |
| 0.3451        | 71.0  | 13916 | 0.7318          | 0.8773   |
| 0.2313        | 72.0  | 14112 | 0.5041          | 0.8700   |
| 0.1984        | 73.0  | 14308 | 0.6518          | 0.7690   |
| 0.2345        | 74.0  | 14504 | 0.5280          | 0.8845   |
| 0.0851        | 75.0  | 14700 | 0.6302          | 0.8917   |
| 0.2234        | 76.0  | 14896 | 0.4843          | 0.8809   |
| 0.2266        | 77.0  | 15092 | 0.4900          | 0.8628   |
| 0.2735        | 78.0  | 15288 | 0.5249          | 0.8736   |
| 0.2442        | 79.0  | 15484 | 0.5061          | 0.8917   |
| 0.2246        | 80.0  | 15680 | 0.4810          | 0.8664   |
| 0.3557        | 81.0  | 15876 | 0.6420          | 0.8123   |
| 0.2017        | 82.0  | 16072 | 0.5158          | 0.8845   |
| 0.249         | 83.0  | 16268 | 0.4364          | 0.9025   |
| 0.2566        | 84.0  | 16464 | 0.5507          | 0.8736   |
| 0.1012        | 85.0  | 16660 | 0.4728          | 0.8845   |
| 0.1972        | 86.0  | 16856 | 0.5746          | 0.8809   |
| 0.7922        | 87.0  | 17052 | 0.5262          | 0.8628   |
| 0.1229        | 88.0  | 17248 | 0.6293          | 0.8845   |
| 0.0248        | 89.0  | 17444 | 0.6193          | 0.8881   |
| 0.0925        | 90.0  | 17640 | 0.4755          | 0.8700   |
| 0.1968        | 91.0  | 17836 | 0.5528          | 0.8700   |
| 0.1694        | 92.0  | 18032 | 0.4338          | 0.8953   |
| 0.2083        | 93.0  | 18228 | 1.1286          | 0.8809   |
| 0.3666        | 94.0  | 18424 | 0.6879          | 0.8267   |
| 0.1358        | 95.0  | 18620 | 0.5071          | 0.8881   |
| 0.2247        | 96.0  | 18816 | 0.5941          | 0.8520   |
| 0.2682        | 97.0  | 19012 | 0.5219          | 0.8592   |
| 0.1762        | 98.0  | 19208 | 0.6929          | 0.8520   |
| 0.2368        | 99.0  | 19404 | 0.5324          | 0.8845   |
| 0.1268        | 100.0 | 19600 | 0.6160          | 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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