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# mobileViTV2-64

This model is a fine-tuned version of [apple/mobilevitv2-1.0-imagenet1k-256](https://huggingface.co/apple/mobilevitv2-1.0-imagenet1k-256) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3307
- Accuracy: 0.9106
- F1: 0.9093
- Precision: 0.9118
- Recall: 0.9106

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.6065        | 1.0   | 364   | 1.6103          | 0.1860   | 0.1866 | 0.1893    | 0.1860 |
| 1.5721        | 2.0   | 728   | 1.5804          | 0.2851   | 0.2857 | 0.2889    | 0.2851 |
| 1.4793        | 3.0   | 1092  | 1.5111          | 0.4229   | 0.4057 | 0.4006    | 0.4229 |
| 1.316         | 4.0   | 1456  | 1.2841          | 0.5028   | 0.4652 | 0.5157    | 0.5028 |
| 1.1397        | 5.0   | 1820  | 1.0520          | 0.5909   | 0.5474 | 0.6502    | 0.5909 |
| 0.8639        | 6.0   | 2184  | 0.8194          | 0.7163   | 0.6970 | 0.7325    | 0.7163 |
| 0.7371        | 7.0   | 2548  | 0.6773          | 0.7796   | 0.7711 | 0.7927    | 0.7796 |
| 0.6451        | 8.0   | 2912  | 0.5546          | 0.8292   | 0.8258 | 0.8334    | 0.8292 |
| 0.5299        | 9.0   | 3276  | 0.4800          | 0.8485   | 0.8461 | 0.8477    | 0.8485 |
| 0.387         | 10.0  | 3640  | 0.4091          | 0.8760   | 0.8737 | 0.8759    | 0.8760 |
| 0.3903        | 11.0  | 4004  | 0.3547          | 0.8884   | 0.8867 | 0.8887    | 0.8884 |
| 0.3513        | 12.0  | 4368  | 0.3207          | 0.9008   | 0.8983 | 0.9038    | 0.9008 |
| 0.3145        | 13.0  | 4732  | 0.3213          | 0.8967   | 0.8939 | 0.9009    | 0.8967 |
| 0.1838        | 14.0  | 5096  | 0.3013          | 0.8939   | 0.8924 | 0.8941    | 0.8939 |
| 0.3438        | 15.0  | 5460  | 0.3229          | 0.8857   | 0.8843 | 0.8850    | 0.8857 |
| 0.1913        | 16.0  | 5824  | 0.2568          | 0.9174   | 0.9159 | 0.9191    | 0.9174 |
| 0.2078        | 17.0  | 6188  | 0.2609          | 0.9187   | 0.9169 | 0.9206    | 0.9187 |
| 0.2061        | 18.0  | 6552  | 0.2811          | 0.9077   | 0.9061 | 0.9076    | 0.9077 |
| 0.2806        | 19.0  | 6916  | 0.2536          | 0.9242   | 0.9230 | 0.9260    | 0.9242 |
| 0.2495        | 20.0  | 7280  | 0.2881          | 0.9091   | 0.9076 | 0.9094    | 0.9091 |
| 0.0361        | 21.0  | 7644  | 0.2875          | 0.9311   | 0.9301 | 0.9331    | 0.9311 |
| 0.1811        | 22.0  | 8008  | 0.3067          | 0.9063   | 0.9050 | 0.9056    | 0.9063 |
| 0.1129        | 23.0  | 8372  | 0.2996          | 0.9050   | 0.9047 | 0.9053    | 0.9050 |
| 0.1138        | 24.0  | 8736  | 0.2970          | 0.9063   | 0.9060 | 0.9066    | 0.9063 |
| 0.3135        | 25.0  | 9100  | 0.3723          | 0.8967   | 0.8968 | 0.8972    | 0.8967 |
| 0.0828        | 26.0  | 9464  | 0.3574          | 0.9063   | 0.9060 | 0.9059    | 0.9063 |
| 0.0783        | 27.0  | 9828  | 0.4087          | 0.8939   | 0.8926 | 0.8926    | 0.8939 |
| 0.051         | 28.0  | 10192 | 0.3713          | 0.9063   | 0.9060 | 0.9068    | 0.9063 |
| 0.0744        | 29.0  | 10556 | 0.4470          | 0.8953   | 0.8951 | 0.8958    | 0.8953 |
| 0.0814        | 30.0  | 10920 | 0.4289          | 0.9077   | 0.9085 | 0.9099    | 0.9077 |
| 0.131         | 31.0  | 11284 | 0.4600          | 0.9008   | 0.8996 | 0.8997    | 0.9008 |
| 0.0245        | 32.0  | 11648 | 0.4818          | 0.8981   | 0.8978 | 0.8977    | 0.8981 |
| 0.0541        | 33.0  | 12012 | 0.4678          | 0.9050   | 0.9043 | 0.9040    | 0.9050 |
| 0.1011        | 34.0  | 12376 | 0.5298          | 0.8994   | 0.8985 | 0.8991    | 0.8994 |
| 0.17          | 35.0  | 12740 | 0.5093          | 0.9036   | 0.9026 | 0.9024    | 0.9036 |
| 0.0892        | 36.0  | 13104 | 0.5018          | 0.9063   | 0.9050 | 0.9050    | 0.9063 |
| 0.0246        | 37.0  | 13468 | 0.5520          | 0.9077   | 0.9058 | 0.9061    | 0.9077 |
| 0.0564        | 38.0  | 13832 | 0.5493          | 0.9077   | 0.9075 | 0.9077    | 0.9077 |
| 0.0817        | 39.0  | 14196 | 0.5607          | 0.9091   | 0.9084 | 0.9081    | 0.9091 |
| 0.0056        | 40.0  | 14560 | 0.5990          | 0.8939   | 0.8947 | 0.8961    | 0.8939 |
| 0.0653        | 41.0  | 14924 | 0.5870          | 0.9146   | 0.9136 | 0.9136    | 0.9146 |
| 0.1649        | 42.0  | 15288 | 0.5882          | 0.9050   | 0.9040 | 0.9039    | 0.9050 |
| 0.1057        | 43.0  | 15652 | 0.5924          | 0.9008   | 0.8999 | 0.9002    | 0.9008 |
| 0.0859        | 44.0  | 16016 | 0.5830          | 0.8994   | 0.8994 | 0.8999    | 0.8994 |
| 0.1809        | 45.0  | 16380 | 0.6357          | 0.8953   | 0.8939 | 0.8936    | 0.8953 |
| 0.1285        | 46.0  | 16744 | 0.6617          | 0.8967   | 0.8965 | 0.8975    | 0.8967 |
| 0.1018        | 47.0  | 17108 | 0.6006          | 0.9050   | 0.9044 | 0.9042    | 0.9050 |
| 0.0091        | 48.0  | 17472 | 0.5762          | 0.9091   | 0.9090 | 0.9094    | 0.9091 |
| 0.0368        | 49.0  | 17836 | 0.6097          | 0.9077   | 0.9067 | 0.9071    | 0.9077 |
| 0.0585        | 50.0  | 18200 | 0.6059          | 0.9063   | 0.9059 | 0.9061    | 0.9063 |
| 0.0373        | 51.0  | 18564 | 0.6621          | 0.8953   | 0.8953 | 0.8963    | 0.8953 |
| 0.1672        | 52.0  | 18928 | 0.6081          | 0.9022   | 0.9020 | 0.9019    | 0.9022 |
| 0.0344        | 53.0  | 19292 | 0.6145          | 0.8994   | 0.9002 | 0.9011    | 0.8994 |
| 0.0727        | 54.0  | 19656 | 0.6106          | 0.9036   | 0.9034 | 0.9034    | 0.9036 |
| 0.1997        | 55.0  | 20020 | 0.6037          | 0.9091   | 0.9082 | 0.9090    | 0.9091 |
| 0.0437        | 56.0  | 20384 | 0.5835          | 0.9105   | 0.9100 | 0.9105    | 0.9105 |
| 0.0263        | 57.0  | 20748 | 0.6032          | 0.9063   | 0.9062 | 0.9064    | 0.9063 |
| 0.056         | 58.0  | 21112 | 0.5828          | 0.9105   | 0.9101 | 0.9102    | 0.9105 |
| 0.0422        | 59.0  | 21476 | 0.6179          | 0.9105   | 0.9111 | 0.9129    | 0.9105 |
| 0.0377        | 60.0  | 21840 | 0.6400          | 0.8981   | 0.8997 | 0.9027    | 0.8981 |
| 0.1162        | 61.0  | 22204 | 0.5841          | 0.9105   | 0.9106 | 0.9108    | 0.9105 |
| 0.0407        | 62.0  | 22568 | 0.6017          | 0.9063   | 0.9064 | 0.9067    | 0.9063 |
| 0.0443        | 63.0  | 22932 | 0.6064          | 0.9036   | 0.9031 | 0.9029    | 0.9036 |
| 0.089         | 64.0  | 23296 | 0.6250          | 0.9008   | 0.9011 | 0.9018    | 0.9008 |
| 0.0971        | 65.0  | 23660 | 0.6729          | 0.9022   | 0.9011 | 0.9018    | 0.9022 |
| 0.046         | 66.0  | 24024 | 0.6445          | 0.9063   | 0.9060 | 0.9062    | 0.9063 |
| 0.0387        | 67.0  | 24388 | 0.6070          | 0.9036   | 0.9039 | 0.9046    | 0.9036 |
| 0.0709        | 68.0  | 24752 | 0.5890          | 0.9132   | 0.9131 | 0.9132    | 0.9132 |
| 0.0273        | 69.0  | 25116 | 0.6484          | 0.9008   | 0.9001 | 0.9007    | 0.9008 |
| 0.1951        | 70.0  | 25480 | 0.6336          | 0.9077   | 0.9075 | 0.9075    | 0.9077 |
| 0.0569        | 71.0  | 25844 | 0.6546          | 0.9105   | 0.9104 | 0.9105    | 0.9105 |
| 0.1145        | 72.0  | 26208 | 0.6964          | 0.9036   | 0.9026 | 0.9027    | 0.9036 |
| 0.0352        | 73.0  | 26572 | 0.6657          | 0.9118   | 0.9114 | 0.9115    | 0.9118 |
| 0.0375        | 74.0  | 26936 | 0.6417          | 0.9050   | 0.9054 | 0.9059    | 0.9050 |
| 0.0351        | 75.0  | 27300 | 0.6812          | 0.9091   | 0.9077 | 0.9081    | 0.9091 |
| 0.0675        | 76.0  | 27664 | 0.6445          | 0.9105   | 0.9100 | 0.9103    | 0.9105 |
| 0.0418        | 77.0  | 28028 | 0.7359          | 0.9091   | 0.9073 | 0.9084    | 0.9091 |
| 0.0353        | 78.0  | 28392 | 0.6701          | 0.9022   | 0.9021 | 0.9022    | 0.9022 |
| 0.0221        | 79.0  | 28756 | 0.6607          | 0.9036   | 0.9039 | 0.9051    | 0.9036 |
| 0.2435        | 80.0  | 29120 | 0.6487          | 0.9118   | 0.9115 | 0.9114    | 0.9118 |
| 0.0362        | 81.0  | 29484 | 0.7711          | 0.9077   | 0.9060 | 0.9064    | 0.9077 |
| 0.0116        | 82.0  | 29848 | 0.6276          | 0.9063   | 0.9067 | 0.9073    | 0.9063 |
| 0.001         | 83.0  | 30212 | 0.6564          | 0.9022   | 0.9020 | 0.9022    | 0.9022 |
| 0.013         | 84.0  | 30576 | 0.6576          | 0.9077   | 0.9071 | 0.9072    | 0.9077 |
| 0.0183        | 85.0  | 30940 | 0.7075          | 0.9036   | 0.9033 | 0.9038    | 0.9036 |
| 0.0367        | 86.0  | 31304 | 0.7168          | 0.9118   | 0.9100 | 0.9108    | 0.9118 |
| 0.027         | 87.0  | 31668 | 0.6892          | 0.9132   | 0.9133 | 0.9143    | 0.9132 |
| 0.015         | 88.0  | 32032 | 0.6886          | 0.9077   | 0.9069 | 0.9069    | 0.9077 |
| 0.0435        | 89.0  | 32396 | 0.6863          | 0.9008   | 0.9009 | 0.9012    | 0.9008 |
| 0.0049        | 90.0  | 32760 | 0.6883          | 0.9077   | 0.9072 | 0.9069    | 0.9077 |
| 0.1041        | 91.0  | 33124 | 0.7216          | 0.9008   | 0.9000 | 0.9002    | 0.9008 |
| 0.0465        | 92.0  | 33488 | 0.7032          | 0.9022   | 0.9021 | 0.9026    | 0.9022 |
| 0.0221        | 93.0  | 33852 | 0.7131          | 0.9036   | 0.9025 | 0.9023    | 0.9036 |
| 0.0091        | 94.0  | 34216 | 0.6886          | 0.8953   | 0.8963 | 0.8976    | 0.8953 |
| 0.0322        | 95.0  | 34580 | 0.7213          | 0.9022   | 0.9020 | 0.9024    | 0.9022 |
| 0.0348        | 96.0  | 34944 | 0.7005          | 0.9022   | 0.9016 | 0.9014    | 0.9022 |
| 0.0357        | 97.0  | 35308 | 0.7131          | 0.8967   | 0.8971 | 0.8980    | 0.8967 |
| 0.0363        | 98.0  | 35672 | 0.6947          | 0.9118   | 0.9112 | 0.9114    | 0.9118 |
| 0.0249        | 99.0  | 36036 | 0.6783          | 0.9132   | 0.9126 | 0.9126    | 0.9132 |
| 0.0179        | 100.0 | 36400 | 0.6614          | 0.9036   | 0.9037 | 0.9039    | 0.9036 |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.4.0
- Tokenizers 0.21.0

durariadorio_64x64 mole_64x64 quebrado_64x64 riadorio_64x64 riofechado_64x64