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# car_identified_model_9

This model is a fine-tuned version of [apple/mobilevitv2-1.0-imagenet1k-256](https://huggingface.co/apple/mobilevitv2-1.0-imagenet1k-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3580
- F1: 0.8333
- Roc Auc: 0.8333
- Accuracy: 0.6667

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.2586        | 1.0   | 1    | 0.6925          | 0.3889 | 0.5417  | 0.0      |
| 0.2586        | 2.0   | 2    | 0.6926          | 0.3889 | 0.5417  | 0.0      |
| 0.2586        | 3.0   | 4    | 0.6924          | 0.3889 | 0.5417  | 0.0      |
| 0.2586        | 4.0   | 5    | 0.6922          | 0.3889 | 0.5417  | 0.0      |
| 0.2586        | 5.0   | 6    | 0.6917          | 0.5    | 0.5     | 0.0      |
| 0.2586        | 6.0   | 8    | 0.6904          | 0.6667 | 0.5833  | 0.0      |
| 0.2586        | 7.0   | 9    | 0.6890          | 0.6667 | 0.5833  | 0.0      |
| 0.2586        | 8.0   | 10   | 0.6871          | 0.6667 | 0.5833  | 0.0      |
| 0.2586        | 9.0   | 11   | 0.6845          | 0.6667 | 0.5833  | 0.0      |
| 0.2586        | 10.0  | 12   | 0.6813          | 0.6667 | 0.5833  | 0.0      |
| 0.2586        | 11.0  | 14   | 0.6764          | 0.6667 | 0.5833  | 0.0      |
| 0.2586        | 12.0  | 15   | 0.6724          | 0.6667 | 0.5833  | 0.0      |
| 0.2586        | 13.0  | 16   | 0.6689          | 0.6667 | 0.5833  | 0.0      |
| 0.2586        | 14.0  | 18   | 0.6653          | 0.6667 | 0.5833  | 0.0      |
| 0.2586        | 15.0  | 19   | 0.6634          | 0.6667 | 0.5833  | 0.0      |
| 0.2586        | 16.0  | 20   | 0.6618          | 0.625  | 0.625   | 0.3333   |
| 0.2586        | 17.0  | 21   | 0.6601          | 0.625  | 0.625   | 0.3333   |
| 0.2586        | 18.0  | 22   | 0.6586          | 0.625  | 0.625   | 0.3333   |
| 0.2586        | 19.0  | 24   | 0.6844          | 0.3889 | 0.5417  | 0.0      |
| 0.2586        | 20.0  | 25   | 0.8059          | 0.4583 | 0.4583  | 0.25     |
| 0.2586        | 21.0  | 26   | 0.9269          | 0.4583 | 0.4583  | 0.25     |
| 0.2586        | 22.0  | 28   | 1.0221          | 0.4583 | 0.4583  | 0.25     |
| 0.2586        | 23.0  | 29   | 1.0359          | 0.4583 | 0.4583  | 0.25     |
| 0.2586        | 24.0  | 30   | 1.0373          | 0.4583 | 0.4583  | 0.25     |
| 0.2586        | 25.0  | 31   | 1.0350          | 0.4583 | 0.4583  | 0.25     |
| 0.2586        | 26.0  | 32   | 0.9747          | 0.4583 | 0.4583  | 0.25     |
| 0.2586        | 27.0  | 34   | 0.8764          | 0.5417 | 0.5417  | 0.3333   |
| 0.2586        | 28.0  | 35   | 0.7686          | 0.5417 | 0.5417  | 0.3333   |
| 0.2586        | 29.0  | 36   | 0.6511          | 0.6667 | 0.6667  | 0.4167   |
| 0.2586        | 30.0  | 38   | 0.5987          | 0.75   | 0.75    | 0.5      |
| 0.2586        | 31.0  | 39   | 0.5267          | 0.75   | 0.75    | 0.5      |
| 0.2586        | 32.0  | 40   | 0.4412          | 0.7917 | 0.7917  | 0.5833   |
| 0.2586        | 33.0  | 41   | 0.3719          | 0.875  | 0.875   | 0.75     |
| 0.2586        | 34.0  | 42   | 0.3447          | 0.875  | 0.875   | 0.75     |
| 0.2586        | 35.0  | 44   | 0.3333          | 0.875  | 0.875   | 0.75     |
| 0.2586        | 36.0  | 45   | 0.3295          | 0.875  | 0.875   | 0.75     |
| 0.2586        | 37.0  | 46   | 0.3310          | 0.8980 | 0.8958  | 0.75     |
| 0.2586        | 38.0  | 48   | 0.3435          | 0.8980 | 0.8958  | 0.75     |
| 0.2586        | 39.0  | 49   | 0.3457          | 0.8980 | 0.8958  | 0.75     |
| 0.2586        | 40.0  | 50   | 0.3664          | 0.9167 | 0.9167  | 0.8333   |
| 0.2586        | 41.0  | 51   | 0.3809          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 42.0  | 52   | 0.4175          | 0.8400 | 0.8333  | 0.5833   |
| 0.2586        | 43.0  | 54   | 0.4183          | 0.8163 | 0.8125  | 0.5833   |
| 0.2586        | 44.0  | 55   | 0.4444          | 0.7755 | 0.7708  | 0.5833   |
| 0.2586        | 45.0  | 56   | 0.4301          | 0.8333 | 0.8333  | 0.75     |
| 0.2586        | 46.0  | 58   | 0.4282          | 0.8333 | 0.8333  | 0.75     |
| 0.2586        | 47.0  | 59   | 0.4202          | 0.8333 | 0.8333  | 0.75     |
| 0.2586        | 48.0  | 60   | 0.3871          | 0.875  | 0.875   | 0.8333   |
| 0.2586        | 49.0  | 61   | 0.3560          | 0.875  | 0.875   | 0.8333   |
| 0.2586        | 50.0  | 62   | 0.3330          | 0.8571 | 0.8542  | 0.75     |
| 0.2586        | 51.0  | 64   | 0.3034          | 0.8980 | 0.8958  | 0.75     |
| 0.2586        | 52.0  | 65   | 0.3170          | 0.8980 | 0.8958  | 0.75     |
| 0.2586        | 53.0  | 66   | 0.3288          | 0.8980 | 0.8958  | 0.75     |
| 0.2586        | 54.0  | 68   | 0.3157          | 0.9388 | 0.9375  | 0.8333   |
| 0.2586        | 55.0  | 69   | 0.3490          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 56.0  | 70   | 0.3491          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 57.0  | 71   | 0.3429          | 0.8980 | 0.8958  | 0.75     |
| 0.2586        | 58.0  | 72   | 0.3620          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 59.0  | 74   | 0.4072          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 60.0  | 75   | 0.4153          | 0.8333 | 0.8333  | 0.5833   |
| 0.2586        | 61.0  | 76   | 0.4254          | 0.8333 | 0.8333  | 0.5833   |
| 0.2586        | 62.0  | 78   | 0.4320          | 0.8163 | 0.8125  | 0.5833   |
| 0.2586        | 63.0  | 79   | 0.4318          | 0.8163 | 0.8125  | 0.5833   |
| 0.2586        | 64.0  | 80   | 0.4116          | 0.8163 | 0.8125  | 0.5833   |
| 0.2586        | 65.0  | 81   | 0.3835          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 66.0  | 82   | 0.3554          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 67.0  | 84   | 0.3407          | 0.875  | 0.875   | 0.75     |
| 0.2586        | 68.0  | 85   | 0.3252          | 0.875  | 0.875   | 0.75     |
| 0.2586        | 69.0  | 86   | 0.3069          | 0.875  | 0.875   | 0.75     |
| 0.2586        | 70.0  | 88   | 0.2970          | 0.875  | 0.875   | 0.75     |
| 0.2586        | 71.0  | 89   | 0.2934          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 72.0  | 90   | 0.2999          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 73.0  | 91   | 0.3068          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 74.0  | 92   | 0.3181          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 75.0  | 94   | 0.3391          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 76.0  | 95   | 0.3482          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 77.0  | 96   | 0.3577          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 78.0  | 98   | 0.4279          | 0.8163 | 0.8125  | 0.5833   |
| 0.2586        | 79.0  | 99   | 0.4492          | 0.7347 | 0.7292  | 0.5      |
| 0.2586        | 80.0  | 100  | 0.4291          | 0.7755 | 0.7708  | 0.5833   |
| 0.2586        | 81.0  | 101  | 0.4267          | 0.7755 | 0.7708  | 0.5833   |
| 0.2586        | 82.0  | 102  | 0.4160          | 0.7755 | 0.7708  | 0.5833   |
| 0.2586        | 83.0  | 104  | 0.4000          | 0.8333 | 0.8333  | 0.5833   |
| 0.2586        | 84.0  | 105  | 0.3792          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 85.0  | 106  | 0.3368          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 86.0  | 108  | 0.3480          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 87.0  | 109  | 0.3798          | 0.8333 | 0.8333  | 0.5833   |
| 0.2586        | 88.0  | 110  | 0.3806          | 0.8163 | 0.8125  | 0.5833   |
| 0.2586        | 89.0  | 111  | 0.3533          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 90.0  | 112  | 0.3428          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 91.0  | 114  | 0.3447          | 0.875  | 0.875   | 0.6667   |
| 0.2586        | 92.0  | 115  | 0.3440          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 93.0  | 116  | 0.3433          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 94.0  | 118  | 0.3584          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 95.0  | 119  | 0.3502          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 96.0  | 120  | 0.3413          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 97.0  | 121  | 0.3247          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 98.0  | 122  | 0.3232          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 99.0  | 124  | 0.3178          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 100.0 | 125  | 0.3201          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 101.0 | 126  | 0.3100          | 0.8333 | 0.8333  | 0.6667   |
| 0.2586        | 102.0 | 128  | 0.3137          | 0.8511 | 0.8542  | 0.6667   |
| 0.2586        | 103.0 | 129  | 0.3140          | 0.8511 | 0.8542  | 0.6667   |
| 0.2586        | 104.0 | 130  | 0.3332          | 0.8333 | 0.8333  | 0.5833   |
| 0.2586        | 105.0 | 131  | 0.3598          | 0.7917 | 0.7917  | 0.5833   |
| 0.2586        | 106.0 | 132  | 0.3742          | 0.8511 | 0.8542  | 0.5833   |
| 0.2586        | 107.0 | 134  | 0.3924          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 108.0 | 135  | 0.4015          | 0.8333 | 0.8333  | 0.6667   |
| 0.2586        | 109.0 | 136  | 0.4096          | 0.8333 | 0.8333  | 0.6667   |
| 0.2586        | 110.0 | 138  | 0.4227          | 0.7917 | 0.7917  | 0.5833   |
| 0.2586        | 111.0 | 139  | 0.4343          | 0.7917 | 0.7917  | 0.5833   |
| 0.2586        | 112.0 | 140  | 0.4349          | 0.7917 | 0.7917  | 0.5833   |
| 0.2586        | 113.0 | 141  | 0.4187          | 0.7917 | 0.7917  | 0.5833   |
| 0.2586        | 114.0 | 142  | 0.4037          | 0.7917 | 0.7917  | 0.5833   |
| 0.2586        | 115.0 | 144  | 0.3776          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 116.0 | 145  | 0.3763          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 117.0 | 146  | 0.3666          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 118.0 | 148  | 0.3539          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 119.0 | 149  | 0.3497          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 120.0 | 150  | 0.3375          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 121.0 | 151  | 0.3265          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 122.0 | 152  | 0.3154          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 123.0 | 154  | 0.3044          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 124.0 | 155  | 0.3225          | 0.8333 | 0.8333  | 0.6667   |
| 0.2586        | 125.0 | 156  | 0.3338          | 0.8333 | 0.8333  | 0.6667   |
| 0.2586        | 126.0 | 158  | 0.3363          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 127.0 | 159  | 0.3446          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 128.0 | 160  | 0.3458          | 0.8333 | 0.8333  | 0.6667   |
| 0.2586        | 129.0 | 161  | 0.3591          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 130.0 | 162  | 0.3573          | 0.8333 | 0.8333  | 0.6667   |
| 0.2586        | 131.0 | 164  | 0.3623          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 132.0 | 165  | 0.3636          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 133.0 | 166  | 0.3622          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 134.0 | 168  | 0.3569          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 135.0 | 169  | 0.3532          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 136.0 | 170  | 0.3576          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 137.0 | 171  | 0.3548          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 138.0 | 172  | 0.3503          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 139.0 | 174  | 0.3547          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 140.0 | 175  | 0.3484          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 141.0 | 176  | 0.3491          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 142.0 | 178  | 0.3511          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 143.0 | 179  | 0.3620          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 144.0 | 180  | 0.3616          | 0.8333 | 0.8333  | 0.6667   |
| 0.2586        | 145.0 | 181  | 0.3654          | 0.8333 | 0.8333  | 0.6667   |
| 0.2586        | 146.0 | 182  | 0.3541          | 0.8571 | 0.8542  | 0.6667   |
| 0.2586        | 147.0 | 184  | 0.3533          | 0.8333 | 0.8333  | 0.6667   |
| 0.2586        | 148.0 | 185  | 0.3675          | 0.8333 | 0.8333  | 0.6667   |
| 0.2586        | 149.0 | 186  | 0.3616          | 0.8333 | 0.8333  | 0.6667   |
| 0.2586        | 150.0 | 188  | 0.3747          | 0.8333 | 0.8333  | 0.6667   |
| 0.2586        | 151.0 | 189  | 0.3888          | 0.7917 | 0.7917  | 0.5833   |
| 0.2586        | 152.0 | 190  | 0.3884          | 0.8085 | 0.8125  | 0.5833   |
| 0.2586        | 153.0 | 191  | 0.3759          | 0.8333 | 0.8333  | 0.6667   |
| 0.2586        | 154.0 | 192  | 0.3743          | 0.8333 | 0.8333  | 0.6667   |
| 0.2586        | 155.0 | 194  | 0.3895          | 0.7917 | 0.7917  | 0.5833   |
| 0.2586        | 156.0 | 195  | 0.3965          | 0.7917 | 0.7917  | 0.5833   |
| 0.2586        | 157.0 | 196  | 0.3917          | 0.7917 | 0.7917  | 0.5833   |
| 0.2586        | 158.0 | 198  | 0.3845          | 0.7917 | 0.7917  | 0.5833   |
| 0.2586        | 159.0 | 199  | 0.3597          | 0.8333 | 0.8333  | 0.6667   |
| 0.2586        | 160.0 | 200  | 0.3580          | 0.8333 | 0.8333  | 0.6667   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1

{"license": "other", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["f1", "accuracy"], "base_model": "apple/mobilevitv2-1.0-imagenet1k-256", "model-index": [{"name": "car_identified_model_9", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "f1", "value": 0.8333333333333334, "name": "F1"}, {"type": "accuracy", "value": 0.6666666666666666, "name": "Accuracy"}]}]}]}
transformers tensorboard safetensors mobilevit image-classification generated_from_trainer dataset:imagefolder base_model:apple/mobilevitv2-1.0-imagenet1k-256 license:other model-index autotrain_compatible endpoints_compatible region:us