
<!-- 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. -->

# SMIDS_10x_beit_large_rmsprop_lr001_fold4

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: 1.5768
- Accuracy: 0.815

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.795         | 1.0   | 1500  | 0.7604          | 0.58     |
| 0.7792        | 2.0   | 3000  | 0.7385          | 0.6267   |
| 0.7703        | 3.0   | 4500  | 0.7702          | 0.6483   |
| 0.64          | 4.0   | 6000  | 0.6315          | 0.7167   |
| 0.7371        | 5.0   | 7500  | 0.6202          | 0.7183   |
| 0.5639        | 6.0   | 9000  | 0.6355          | 0.7167   |
| 0.6178        | 7.0   | 10500 | 0.6362          | 0.7067   |
| 0.5942        | 8.0   | 12000 | 0.6410          | 0.7517   |
| 0.7617        | 9.0   | 13500 | 0.7058          | 0.6667   |
| 0.682         | 10.0  | 15000 | 0.6048          | 0.7317   |
| 0.5633        | 11.0  | 16500 | 0.6339          | 0.7417   |
| 0.6103        | 12.0  | 18000 | 0.5790          | 0.775    |
| 0.5117        | 13.0  | 19500 | 0.6194          | 0.7483   |
| 0.5007        | 14.0  | 21000 | 0.6762          | 0.7267   |
| 0.522         | 15.0  | 22500 | 0.5551          | 0.7533   |
| 0.4368        | 16.0  | 24000 | 0.5533          | 0.7733   |
| 0.4169        | 17.0  | 25500 | 0.5722          | 0.7683   |
| 0.4272        | 18.0  | 27000 | 0.5809          | 0.7733   |
| 0.5159        | 19.0  | 28500 | 0.5836          | 0.78     |
| 0.5118        | 20.0  | 30000 | 0.5328          | 0.7717   |
| 0.4214        | 21.0  | 31500 | 0.4910          | 0.8083   |
| 0.3456        | 22.0  | 33000 | 0.6512          | 0.7483   |
| 0.3782        | 23.0  | 34500 | 0.5142          | 0.8067   |
| 0.3993        | 24.0  | 36000 | 0.5795          | 0.7833   |
| 0.5068        | 25.0  | 37500 | 0.6045          | 0.74     |
| 0.4986        | 26.0  | 39000 | 0.6069          | 0.7633   |
| 0.3855        | 27.0  | 40500 | 0.5756          | 0.77     |
| 0.2885        | 28.0  | 42000 | 0.6110          | 0.7733   |
| 0.4429        | 29.0  | 43500 | 0.5973          | 0.8      |
| 0.3783        | 30.0  | 45000 | 0.5510          | 0.8067   |
| 0.3025        | 31.0  | 46500 | 0.5541          | 0.8217   |
| 0.2805        | 32.0  | 48000 | 0.5534          | 0.8067   |
| 0.3144        | 33.0  | 49500 | 0.5834          | 0.8      |
| 0.3064        | 34.0  | 51000 | 0.6483          | 0.8017   |
| 0.2888        | 35.0  | 52500 | 0.6092          | 0.8083   |
| 0.2742        | 36.0  | 54000 | 0.6176          | 0.8117   |
| 0.2345        | 37.0  | 55500 | 0.6258          | 0.83     |
| 0.2625        | 38.0  | 57000 | 0.6121          | 0.8283   |
| 0.2584        | 39.0  | 58500 | 0.7081          | 0.8217   |
| 0.2474        | 40.0  | 60000 | 0.7309          | 0.825    |
| 0.1838        | 41.0  | 61500 | 0.7498          | 0.8167   |
| 0.2034        | 42.0  | 63000 | 0.7890          | 0.8217   |
| 0.1944        | 43.0  | 64500 | 0.8068          | 0.8267   |
| 0.2367        | 44.0  | 66000 | 0.9360          | 0.8183   |
| 0.1327        | 45.0  | 67500 | 1.0947          | 0.8217   |
| 0.1921        | 46.0  | 69000 | 1.1021          | 0.8267   |
| 0.0854        | 47.0  | 70500 | 1.2998          | 0.815    |
| 0.102         | 48.0  | 72000 | 1.4018          | 0.8183   |
| 0.073         | 49.0  | 73500 | 1.4474          | 0.8167   |
| 0.0782        | 50.0  | 75000 | 1.5768          | 0.815    |


### 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_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.815, "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