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# SMIDS_10x_beit_large_rmsprop_lr001_fold5

This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8809
- Accuracy: 0.855

## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9007        | 1.0   | 1500  | 0.8324          | 0.54     |
| 0.6931        | 2.0   | 3000  | 0.7419          | 0.6483   |
| 0.7559        | 3.0   | 4500  | 0.7329          | 0.62     |
| 0.5328        | 4.0   | 6000  | 0.6672          | 0.6933   |
| 0.6642        | 5.0   | 7500  | 0.6029          | 0.7117   |
| 0.607         | 6.0   | 9000  | 0.5427          | 0.7633   |
| 0.5501        | 7.0   | 10500 | 0.5347          | 0.7683   |
| 0.5267        | 8.0   | 12000 | 0.4955          | 0.8      |
| 0.4514        | 9.0   | 13500 | 0.4700          | 0.8083   |
| 0.5483        | 10.0  | 15000 | 0.5583          | 0.775    |
| 0.4572        | 11.0  | 16500 | 0.5180          | 0.7783   |
| 0.4976        | 12.0  | 18000 | 0.5365          | 0.7717   |
| 0.4574        | 13.0  | 19500 | 0.4573          | 0.8217   |
| 0.3745        | 14.0  | 21000 | 0.4783          | 0.8267   |
| 0.4054        | 15.0  | 22500 | 0.4418          | 0.8333   |
| 0.3072        | 16.0  | 24000 | 0.4918          | 0.8217   |
| 0.3901        | 17.0  | 25500 | 0.4521          | 0.8333   |
| 0.3275        | 18.0  | 27000 | 0.5469          | 0.8      |
| 0.4185        | 19.0  | 28500 | 0.4742          | 0.8167   |
| 0.383         | 20.0  | 30000 | 0.4326          | 0.825    |
| 0.2624        | 21.0  | 31500 | 0.4177          | 0.8333   |
| 0.2914        | 22.0  | 33000 | 0.4362          | 0.8333   |
| 0.2955        | 23.0  | 34500 | 0.4355          | 0.84     |
| 0.3432        | 24.0  | 36000 | 0.4729          | 0.8233   |
| 0.2976        | 25.0  | 37500 | 0.5029          | 0.8217   |
| 0.3144        | 26.0  | 39000 | 0.4320          | 0.8367   |
| 0.2666        | 27.0  | 40500 | 0.4639          | 0.8417   |
| 0.364         | 28.0  | 42000 | 0.4273          | 0.8383   |
| 0.2508        | 29.0  | 43500 | 0.4211          | 0.8533   |
| 0.3054        | 30.0  | 45000 | 0.4260          | 0.8533   |
| 0.288         | 31.0  | 46500 | 0.4356          | 0.8467   |
| 0.2024        | 32.0  | 48000 | 0.4757          | 0.8433   |
| 0.2616        | 33.0  | 49500 | 0.4143          | 0.86     |
| 0.2164        | 34.0  | 51000 | 0.5732          | 0.86     |
| 0.2173        | 35.0  | 52500 | 0.4574          | 0.8633   |
| 0.2261        | 36.0  | 54000 | 0.4904          | 0.845    |
| 0.2338        | 37.0  | 55500 | 0.5747          | 0.845    |
| 0.1396        | 38.0  | 57000 | 0.5771          | 0.85     |
| 0.3099        | 39.0  | 58500 | 0.5528          | 0.855    |
| 0.1739        | 40.0  | 60000 | 0.5792          | 0.8583   |
| 0.2292        | 41.0  | 61500 | 0.6860          | 0.8583   |
| 0.1843        | 42.0  | 63000 | 0.6255          | 0.8633   |
| 0.1066        | 43.0  | 64500 | 0.7370          | 0.8517   |
| 0.3008        | 44.0  | 66000 | 0.7129          | 0.85     |
| 0.2553        | 45.0  | 67500 | 0.7201          | 0.8567   |
| 0.153         | 46.0  | 69000 | 0.8225          | 0.8567   |
| 0.2018        | 47.0  | 70500 | 0.8448          | 0.85     |
| 0.1739        | 48.0  | 72000 | 0.8673          | 0.845    |
| 0.2061        | 49.0  | 73500 | 0.8698          | 0.8583   |
| 0.0849        | 50.0  | 75000 | 0.8809          | 0.855    |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_10x_beit_large_rmsprop_lr001_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.855, "name": "Accuracy"}]}]}]}
transformers safetensors beit image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/beit-large-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us