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# tomato-leaf-disease-classification-resnet50

This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the wellCh4n/tomato-leaf-disease-image dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0197
- Accuracy: 0.9956

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.6891        | 1.0   | 965   | 1.6572          | 0.3488   |
| 1.1351        | 2.0   | 1930  | 1.1593          | 0.7126   |
| 0.7767        | 3.0   | 2895  | 0.6135          | 0.8168   |
| 0.7963        | 4.0   | 3860  | 0.3818          | 0.8796   |
| 0.547         | 5.0   | 4825  | 0.2581          | 0.9302   |
| 0.5104        | 6.0   | 5790  | 0.2106          | 0.9438   |
| 0.3997        | 7.0   | 6755  | 0.1579          | 0.9563   |
| 0.2527        | 8.0   | 7720  | 0.1292          | 0.9604   |
| 0.3268        | 9.0   | 8685  | 0.1154          | 0.9659   |
| 0.2595        | 10.0  | 9650  | 0.1018          | 0.9699   |
| 0.2269        | 11.0  | 10615 | 0.0869          | 0.9743   |
| 0.2515        | 12.0  | 11580 | 0.0783          | 0.9747   |
| 0.2604        | 13.0  | 12545 | 0.0710          | 0.9794   |
| 0.2583        | 14.0  | 13510 | 0.0704          | 0.9783   |
| 0.2004        | 15.0  | 14475 | 0.0603          | 0.9824   |
| 0.2552        | 16.0  | 15440 | 0.0565          | 0.9835   |
| 0.2192        | 17.0  | 16405 | 0.0553          | 0.9846   |
| 0.3443        | 18.0  | 17370 | 0.0508          | 0.9831   |
| 0.1954        | 19.0  | 18335 | 0.0530          | 0.9846   |
| 0.2685        | 20.0  | 19300 | 0.0430          | 0.9864   |
| 0.1277        | 21.0  | 20265 | 0.0406          | 0.9864   |
| 0.1388        | 22.0  | 21230 | 0.0404          | 0.9872   |
| 0.2379        | 23.0  | 22195 | 0.0399          | 0.9875   |
| 0.1018        | 24.0  | 23160 | 0.0441          | 0.9879   |
| 0.2155        | 25.0  | 24125 | 0.0364          | 0.9905   |
| 0.1699        | 26.0  | 25090 | 0.0398          | 0.9875   |
| 0.2772        | 27.0  | 26055 | 0.0364          | 0.9872   |
| 0.1669        | 28.0  | 27020 | 0.0369          | 0.9894   |
| 0.0867        | 29.0  | 27985 | 0.0339          | 0.9901   |
| 0.1314        | 30.0  | 28950 | 0.0322          | 0.9905   |
| 0.082         | 31.0  | 29915 | 0.0362          | 0.9879   |
| 0.0393        | 32.0  | 30880 | 0.0332          | 0.9908   |
| 0.0812        | 33.0  | 31845 | 0.0329          | 0.9905   |
| 0.2634        | 34.0  | 32810 | 0.0333          | 0.9897   |
| 0.1581        | 35.0  | 33775 | 0.0337          | 0.9901   |
| 0.168         | 36.0  | 34740 | 0.0298          | 0.9890   |
| 0.0653        | 37.0  | 35705 | 0.0311          | 0.9905   |
| 0.0998        | 38.0  | 36670 | 0.0326          | 0.9901   |
| 0.0947        | 39.0  | 37635 | 0.0288          | 0.9919   |
| 0.1126        | 40.0  | 38600 | 0.0272          | 0.9916   |
| 0.1319        | 41.0  | 39565 | 0.0272          | 0.9919   |
| 0.0446        | 42.0  | 40530 | 0.0283          | 0.9916   |
| 0.2453        | 43.0  | 41495 | 0.0281          | 0.9919   |
| 0.0708        | 44.0  | 42460 | 0.0263          | 0.9923   |
| 0.0441        | 45.0  | 43425 | 0.0262          | 0.9916   |
| 0.0936        | 46.0  | 44390 | 0.0252          | 0.9919   |
| 0.1565        | 47.0  | 45355 | 0.0284          | 0.9923   |
| 0.0404        | 48.0  | 46320 | 0.0263          | 0.9930   |
| 0.0357        | 49.0  | 47285 | 0.0240          | 0.9930   |
| 0.0971        | 50.0  | 48250 | 0.0285          | 0.9916   |
| 0.0582        | 51.0  | 49215 | 0.0251          | 0.9923   |
| 0.048         | 52.0  | 50180 | 0.0257          | 0.9919   |
| 0.1218        | 53.0  | 51145 | 0.0252          | 0.9930   |
| 0.0576        | 54.0  | 52110 | 0.0227          | 0.9930   |
| 0.0723        | 55.0  | 53075 | 0.0227          | 0.9930   |
| 0.1347        | 56.0  | 54040 | 0.0242          | 0.9941   |
| 0.1684        | 57.0  | 55005 | 0.0255          | 0.9927   |
| 0.0525        | 58.0  | 55970 | 0.0250          | 0.9938   |
| 0.1031        | 59.0  | 56935 | 0.0265          | 0.9923   |
| 0.0768        | 60.0  | 57900 | 0.0244          | 0.9941   |
| 0.0416        | 61.0  | 58865 | 0.0207          | 0.9934   |
| 0.1783        | 62.0  | 59830 | 0.0237          | 0.9941   |
| 0.1253        | 63.0  | 60795 | 0.0269          | 0.9912   |
| 0.0448        | 64.0  | 61760 | 0.0236          | 0.9941   |
| 0.0967        | 65.0  | 62725 | 0.0230          | 0.9934   |
| 0.0486        | 66.0  | 63690 | 0.0229          | 0.9941   |
| 0.0442        | 67.0  | 64655 | 0.0256          | 0.9934   |
| 0.0526        | 68.0  | 65620 | 0.0210          | 0.9945   |
| 0.0949        | 69.0  | 66585 | 0.0250          | 0.9938   |
| 0.0674        | 70.0  | 67550 | 0.0228          | 0.9938   |
| 0.1554        | 71.0  | 68515 | 0.0240          | 0.9941   |
| 0.0598        | 72.0  | 69480 | 0.0233          | 0.9945   |
| 0.0632        | 73.0  | 70445 | 0.0218          | 0.9949   |
| 0.0951        | 74.0  | 71410 | 0.0234          | 0.9945   |
| 0.1634        | 75.0  | 72375 | 0.0245          | 0.9945   |
| 0.2039        | 76.0  | 73340 | 0.0222          | 0.9938   |
| 0.0741        | 77.0  | 74305 | 0.0226          | 0.9949   |
| 0.0923        | 78.0  | 75270 | 0.0218          | 0.9949   |
| 0.0351        | 79.0  | 76235 | 0.0230          | 0.9945   |
| 0.1234        | 80.0  | 77200 | 0.0244          | 0.9934   |
| 0.0659        | 81.0  | 78165 | 0.0232          | 0.9945   |
| 0.0393        | 82.0  | 79130 | 0.0210          | 0.9949   |
| 0.053         | 83.0  | 80095 | 0.0205          | 0.9945   |
| 0.0575        | 84.0  | 81060 | 0.0210          | 0.9945   |
| 0.0651        | 85.0  | 82025 | 0.0198          | 0.9949   |
| 0.0875        | 86.0  | 82990 | 0.0210          | 0.9945   |
| 0.1006        | 87.0  | 83955 | 0.0214          | 0.9949   |
| 0.0466        | 88.0  | 84920 | 0.0211          | 0.9941   |
| 0.088         | 89.0  | 85885 | 0.0233          | 0.9923   |
| 0.1162        | 90.0  | 86850 | 0.0197          | 0.9956   |
| 0.0641        | 91.0  | 87815 | 0.0213          | 0.9949   |
| 0.0867        | 92.0  | 88780 | 0.0203          | 0.9952   |
| 0.0305        | 93.0  | 89745 | 0.0212          | 0.9941   |
| 0.1009        | 94.0  | 90710 | 0.0200          | 0.9956   |
| 0.084         | 95.0  | 91675 | 0.0200          | 0.9960   |
| 0.0409        | 96.0  | 92640 | 0.0213          | 0.9949   |
| 0.107         | 97.0  | 93605 | 0.0210          | 0.9934   |
| 0.0558        | 98.0  | 94570 | 0.0206          | 0.9952   |
| 0.0644        | 99.0  | 95535 | 0.0219          | 0.9949   |
| 0.0617        | 100.0 | 96500 | 0.0205          | 0.9941   |


### Framework versions

- Transformers 4.48.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0

a healthy tomato leaf a tomato leaf with leaf mold a tomato leaf with target spot a tomato leaf with late blight a tomato leaf with early blight a tomato leaf with bacterial spot a tomato leaf with septoria leaf spot a tomato leaf with tomato mosaic virus a tomato leaf with tomato yellow leaf curl virus a tomato leaf with spider mites two-spotted spider mite