
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# faces_clasification_alcss

This model is a fine-tuned version of [lokeshk/Face-Recognition-NM](https://huggingface.co/lokeshk/Face-Recognition-NM) on the private_faces_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2189
- Accuracy: 0.5481

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 4.6929        | 0.0962 | 10   | 4.5349          | 0.0913   |
| 4.4502        | 0.1923 | 20   | 4.3568          | 0.1010   |
| 4.3178        | 0.2885 | 30   | 4.2414          | 0.0865   |
| 4.2083        | 0.3846 | 40   | 4.1396          | 0.1731   |
| 4.1225        | 0.4808 | 50   | 4.0550          | 0.1538   |
| 3.908         | 0.5769 | 60   | 3.9737          | 0.1683   |
| 3.9502        | 0.6731 | 70   | 3.9053          | 0.1683   |
| 3.8725        | 0.7692 | 80   | 3.8432          | 0.1731   |
| 3.7944        | 0.8654 | 90   | 3.7840          | 0.2115   |
| 3.7648        | 0.9615 | 100  | 3.7644          | 0.1971   |
| 3.7211        | 1.0577 | 110  | 3.7172          | 0.1971   |
| 3.5759        | 1.1538 | 120  | 3.6622          | 0.25     |
| 3.6082        | 1.25   | 130  | 3.6080          | 0.2644   |
| 3.4272        | 1.3462 | 140  | 3.5738          | 0.2837   |
| 3.3349        | 1.4423 | 150  | 3.5526          | 0.2740   |
| 3.189         | 1.5385 | 160  | 3.5162          | 0.2837   |
| 3.2959        | 1.6346 | 170  | 3.4639          | 0.2933   |
| 3.1273        | 1.7308 | 180  | 3.4308          | 0.3365   |
| 3.1621        | 1.8269 | 190  | 3.3436          | 0.3221   |
| 3.1053        | 1.9231 | 200  | 3.2973          | 0.3702   |
| 3.0641        | 2.0192 | 210  | 3.2637          | 0.3462   |
| 2.6593        | 2.1154 | 220  | 3.2149          | 0.3798   |
| 2.5709        | 2.2115 | 230  | 3.1658          | 0.3846   |
| 2.5412        | 2.3077 | 240  | 3.1359          | 0.3702   |
| 2.5775        | 2.4038 | 250  | 3.1104          | 0.3654   |
| 2.6372        | 2.5    | 260  | 3.1027          | 0.3798   |
| 2.6732        | 2.5962 | 270  | 3.0252          | 0.4231   |
| 2.6384        | 2.6923 | 280  | 3.0202          | 0.3942   |
| 2.4607        | 2.7885 | 290  | 2.9785          | 0.4135   |
| 2.5519        | 2.8846 | 300  | 2.9470          | 0.4327   |
| 2.2381        | 2.9808 | 310  | 2.9402          | 0.4231   |
| 2.1999        | 3.0769 | 320  | 2.9074          | 0.4519   |
| 2.179         | 3.1731 | 330  | 2.8780          | 0.4567   |
| 2.1427        | 3.2692 | 340  | 2.8331          | 0.4423   |
| 2.1335        | 3.3654 | 350  | 2.8051          | 0.4760   |
| 1.7641        | 3.4615 | 360  | 2.7798          | 0.4712   |
| 1.9687        | 3.5577 | 370  | 2.7607          | 0.4808   |
| 1.8046        | 3.6538 | 380  | 2.7381          | 0.4760   |
| 1.944         | 3.75   | 390  | 2.7244          | 0.4856   |
| 1.7403        | 3.8462 | 400  | 2.6899          | 0.4567   |
| 1.7732        | 3.9423 | 410  | 2.6656          | 0.4808   |
| 1.4105        | 4.0385 | 420  | 2.6526          | 0.4760   |
| 1.377         | 4.1346 | 430  | 2.6448          | 0.4904   |
| 1.5767        | 4.2308 | 440  | 2.5933          | 0.4663   |
| 1.3826        | 4.3269 | 450  | 2.5832          | 0.5      |
| 1.6504        | 4.4231 | 460  | 2.5573          | 0.5192   |
| 1.5579        | 4.5192 | 470  | 2.5666          | 0.5048   |
| 1.2466        | 4.6154 | 480  | 2.5197          | 0.5144   |
| 1.32          | 4.7115 | 490  | 2.5145          | 0.5240   |
| 1.5286        | 4.8077 | 500  | 2.4909          | 0.5192   |
| 1.394         | 4.9038 | 510  | 2.4882          | 0.5192   |
| 1.3982        | 5.0    | 520  | 2.4616          | 0.5192   |
| 1.1167        | 5.0962 | 530  | 2.4569          | 0.5096   |
| 1.3562        | 5.1923 | 540  | 2.4559          | 0.5192   |
| 1.0018        | 5.2885 | 550  | 2.4438          | 0.5192   |
| 1.2367        | 5.3846 | 560  | 2.4204          | 0.5192   |
| 1.1748        | 5.4808 | 570  | 2.4112          | 0.5337   |
| 1.022         | 5.5769 | 580  | 2.4130          | 0.5385   |
| 1.0954        | 5.6731 | 590  | 2.3992          | 0.5192   |
| 0.9759        | 5.7692 | 600  | 2.3683          | 0.5240   |
| 1.0327        | 5.8654 | 610  | 2.3577          | 0.5144   |
| 1.1167        | 5.9615 | 620  | 2.3547          | 0.5144   |
| 0.8077        | 6.0577 | 630  | 2.3452          | 0.5385   |
| 0.95          | 6.1538 | 640  | 2.3486          | 0.5433   |
| 0.7993        | 6.25   | 650  | 2.3428          | 0.5529   |
| 0.923         | 6.3462 | 660  | 2.3279          | 0.5337   |
| 0.7566        | 6.4423 | 670  | 2.3176          | 0.5385   |
| 0.8834        | 6.5385 | 680  | 2.3201          | 0.5288   |
| 0.9337        | 6.6346 | 690  | 2.3064          | 0.5529   |
| 0.7596        | 6.7308 | 700  | 2.3063          | 0.5288   |
| 0.973         | 6.8269 | 710  | 2.2847          | 0.5337   |
| 1.0212        | 6.9231 | 720  | 2.3006          | 0.5433   |
| 0.8315        | 7.0192 | 730  | 2.2813          | 0.5385   |
| 0.814         | 7.1154 | 740  | 2.2751          | 0.5481   |
| 0.7658        | 7.2115 | 750  | 2.2754          | 0.5433   |
| 0.5956        | 7.3077 | 760  | 2.2781          | 0.5433   |
| 0.8864        | 7.4038 | 770  | 2.2602          | 0.5529   |
| 0.7181        | 7.5    | 780  | 2.2568          | 0.5721   |
| 0.643         | 7.5962 | 790  | 2.2586          | 0.5481   |
| 0.7107        | 7.6923 | 800  | 2.2520          | 0.5337   |
| 0.6078        | 7.7885 | 810  | 2.2423          | 0.5385   |
| 0.873         | 7.8846 | 820  | 2.2478          | 0.5337   |
| 0.7018        | 7.9808 | 830  | 2.2357          | 0.5577   |
| 0.7177        | 8.0769 | 840  | 2.2340          | 0.5481   |
| 0.6543        | 8.1731 | 850  | 2.2337          | 0.5481   |
| 0.7563        | 8.2692 | 860  | 2.2259          | 0.5529   |
| 0.549         | 8.3654 | 870  | 2.2310          | 0.5529   |
| 0.6981        | 8.4615 | 880  | 2.2381          | 0.5529   |
| 0.6172        | 8.5577 | 890  | 2.2285          | 0.5481   |
| 0.587         | 8.6538 | 900  | 2.2165          | 0.5481   |
| 0.613         | 8.75   | 910  | 2.2215          | 0.5481   |
| 0.5558        | 8.8462 | 920  | 2.2274          | 0.5529   |
| 0.6892        | 8.9423 | 930  | 2.2221          | 0.5433   |
| 0.5876        | 9.0385 | 940  | 2.2209          | 0.5481   |
| 0.5845        | 9.1346 | 950  | 2.2211          | 0.5481   |
| 0.645         | 9.2308 | 960  | 2.2208          | 0.5481   |
| 0.5619        | 9.3269 | 970  | 2.2204          | 0.5481   |
| 0.5371        | 9.4231 | 980  | 2.2219          | 0.5481   |
| 0.5048        | 9.5192 | 990  | 2.2203          | 0.5481   |
| 0.6007        | 9.6154 | 1000 | 2.2192          | 0.5481   |
| 0.5706        | 9.7115 | 1010 | 2.2187          | 0.5481   |
| 0.571         | 9.8077 | 1020 | 2.2189          | 0.5481   |
| 0.6692        | 9.9038 | 1030 | 2.2191          | 0.5481   |
| 0.5411        | 10.0   | 1040 | 2.2189          | 0.5481   |


### Framework versions

- Transformers 4.44.1
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1

label_0 label_1 label_2 label_3 label_4 label_5 label_6 label_7 label_8 label_9 label_10 label_11 label_12 label_13 label_14 label_15 label_16 label_17 label_18 label_19 label_20 label_21 label_22 label_23 label_24 label_25 label_26 label_27 label_28 label_29 label_30 label_31 label_32 label_33 label_34 label_35 label_36 label_37 label_38 label_39 label_40 label_41 label_42 label_43 label_44 label_45 label_46 label_47 label_48 label_49 label_50 label_51 label_52 label_53 label_54 label_55 label_56 label_57 label_58 label_59 label_60 label_61 label_62 label_63 label_64 label_65 label_66 label_67 label_68 label_69 label_70 label_71 label_72 label_73 label_74 label_75 label_76 label_77 label_78 label_79 label_80 label_81 label_82 label_83 label_84 label_85 label_86 label_87 label_88 label_89 label_90 label_91 label_92 label_93 label_94 label_95 label_96 label_97 label_98 label_99 label_100 label_101 label_102 label_103 label_104 label_105 label_106 label_107 label_108 label_109 label_110 label_111 label_112 label_113 label_114 label_115