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# mobilevitv2-1.0-imagenet1k-256-finetuned-ind-5-balanced-1000

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: 1007781762953927642172275961102336.0000
- Accuracy: 0.1868
- Recall: 0.1868
- F1: 0.0588
- Precision: 0.0349

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss                           | Epoch | Step | Validation Loss                         | Accuracy | Recall | F1     | Precision |
|:---------------------------------------:|:-----:|:----:|:---------------------------------------:|:--------:|:------:|:------:|:---------:|
| 972069575855120103244540638396416.0000  | 0.97  | 22   | 1007781762953927642172275961102336.0000 | 0.1868   | 0.1868 | 0.0588 | 0.0349    |
| 975942471268024415434562548531200.0000  | 1.98  | 45   | 1007781762953927642172275961102336.0000 | 0.1868   | 0.1868 | 0.0588 | 0.0349    |
| 1006924087146209806229394205704192.0000 | 2.99  | 68   | 1007781762953927642172275961102336.0000 | 0.1868   | 0.1868 | 0.0588 | 0.0349    |
| 975942347474020371604384597934080.0000  | 4.0   | 91   | 1007781762953927642172275961102336.0000 | 0.1868   | 0.1868 | 0.0588 | 0.0349    |
| 999176563204346153493927911489536.0000  | 4.97  | 113  | 1007781762953927642172275961102336.0000 | 0.1868   | 0.1868 | 0.0588 | 0.0349    |
| 960450889616407166674474907992064.0000  | 5.98  | 136  | 1007781762953927642172275961102336.0000 | 0.1868   | 0.1868 | 0.0588 | 0.0349    |
| 999177863041387316674103710056448.0000  | 6.99  | 159  | 1007781762953927642172275961102336.0000 | 0.1868   | 0.1868 | 0.0588 | 0.0349    |
| 1006924025249207784314305230405632.0000 | 8.0   | 182  | 1007781762953927642172275961102336.0000 | 0.1868   | 0.1868 | 0.0588 | 0.0349    |
| 999178667702412880994320009658368.0000  | 8.97  | 204  | 1007781762953927642172275961102336.0000 | 0.1868   | 0.1868 | 0.0588 | 0.0349    |
| 960451508586426809364612357554176.0000  | 9.98  | 227  | 1007781762953927642172275961102336.0000 | 0.1868   | 0.1868 | 0.0588 | 0.0349    |
| 979815923753946348399632932929536.0000  | 10.99 | 250  | 1007781762953927642172275961102336.0000 | 0.1868   | 0.1868 | 0.0588 | 0.0349    |
| 995305524701500769374318350041088.0000  | 12.0  | 273  | 1007781762953927642172275961102336.0000 | 0.1868   | 0.1868 | 0.0588 | 0.0349    |
| 995307691096569518789799423508480.0000  | 12.97 | 295  | 1007781762953927642172275961102336.0000 | 0.1868   | 0.1868 | 0.0588 | 0.0349    |
| 972068956885100460554403188834304.0000  | 13.98 | 318  | 1007781762953927642172275961102336.0000 | 0.1868   | 0.1868 | 0.0588 | 0.0349    |
| 956579851113561782554865346543616.0000  | 14.99 | 341  | 1007781762953927642172275961102336.0000 | 0.1868   | 0.1868 | 0.0588 | 0.0349    |
| 948835669609804142700066049622016.0000  | 16.0  | 364  | 1007781762953927642172275961102336.0000 | 0.1868   | 0.1868 | 0.0588 | 0.0349    |
| 987560290948709765769323004035072.0000  | 16.97 | 386  | 1007781762953927642172275961102336.0000 | 0.1868   | 0.1868 | 0.0588 | 0.0349    |
| 979815242886924683794406508068864.0000  | 17.98 | 409  | 1007781762953927642172275961102336.0000 | 0.1868   | 0.1868 | 0.0588 | 0.0349    |
| 999179348569434545599546434519040.0000  | 18.99 | 432  | 1007781762953927642172275961102336.0000 | 0.1868   | 0.1868 | 0.0588 | 0.0349    |
| 972066419108019867878764415287296.0000  | 19.34 | 440  | 1007781762953927642172275961102336.0000 | 0.1868   | 0.1868 | 0.0588 | 0.0349    |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0

{"license": "other", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy", "recall", "f1", "precision"], "base_model": "apple/mobilevitv2-1.0-imagenet1k-256", "model-index": [{"name": "mobilevitv2-1.0-imagenet1k-256-finetuned-ind-5-balanced-1000", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.18681318681318682, "name": "Accuracy"}, {"type": "recall", "value": 0.18681318681318682, "name": "Recall"}, {"type": "f1", "value": 0.05881155881155881, "name": "F1"}, {"type": "precision", "value": 0.03489916676729864, "name": "Precision"}]}]}]}
transformers tensorboard safetensors mobilevitv2 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