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---
license: other
base_model: apple/mobilevitv2-1.0-imagenet1k-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- f1
- accuracy
model-index:
- name: car_identified_model_9
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: F1
type: f1
value: 0.8333333333333334
- name: Accuracy
type: accuracy
value: 0.6666666666666666
---
<!-- 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. -->
# 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
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