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# SMIDS_10x_beit_large_rmsprop_lr0001_fold2

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: 1.2561
- Accuracy: 0.9018

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2917        | 1.0   | 1500  | 0.4494          | 0.8186   |
| 0.1303        | 2.0   | 3000  | 0.3035          | 0.8902   |
| 0.2149        | 3.0   | 4500  | 0.2689          | 0.9052   |
| 0.2276        | 4.0   | 6000  | 0.3497          | 0.9035   |
| 0.099         | 5.0   | 7500  | 0.4657          | 0.8785   |
| 0.0923        | 6.0   | 9000  | 0.4071          | 0.8952   |
| 0.2529        | 7.0   | 10500 | 0.4143          | 0.8835   |
| 0.0648        | 8.0   | 12000 | 0.4042          | 0.9018   |
| 0.0679        | 9.0   | 13500 | 0.5196          | 0.9118   |
| 0.0891        | 10.0  | 15000 | 0.5019          | 0.9151   |
| 0.073         | 11.0  | 16500 | 0.5188          | 0.8902   |
| 0.0844        | 12.0  | 18000 | 0.6704          | 0.8869   |
| 0.1071        | 13.0  | 19500 | 0.6262          | 0.8735   |
| 0.0343        | 14.0  | 21000 | 0.6842          | 0.8935   |
| 0.037         | 15.0  | 22500 | 0.5639          | 0.9035   |
| 0.0627        | 16.0  | 24000 | 0.5975          | 0.8935   |
| 0.0009        | 17.0  | 25500 | 0.5746          | 0.9101   |
| 0.0328        | 18.0  | 27000 | 0.6955          | 0.9035   |
| 0.0028        | 19.0  | 28500 | 0.8434          | 0.8918   |
| 0.0004        | 20.0  | 30000 | 0.6444          | 0.8935   |
| 0.0544        | 21.0  | 31500 | 0.7223          | 0.8885   |
| 0.0284        | 22.0  | 33000 | 0.5794          | 0.8918   |
| 0.0006        | 23.0  | 34500 | 0.6545          | 0.9085   |
| 0.0           | 24.0  | 36000 | 0.7854          | 0.9002   |
| 0.0052        | 25.0  | 37500 | 0.6385          | 0.9035   |
| 0.0003        | 26.0  | 39000 | 0.7034          | 0.8952   |
| 0.1084        | 27.0  | 40500 | 0.6707          | 0.9018   |
| 0.0004        | 28.0  | 42000 | 0.7654          | 0.9035   |
| 0.0548        | 29.0  | 43500 | 0.9122          | 0.8852   |
| 0.0005        | 30.0  | 45000 | 0.8983          | 0.9085   |
| 0.0           | 31.0  | 46500 | 0.7955          | 0.9135   |
| 0.0001        | 32.0  | 48000 | 0.9824          | 0.8968   |
| 0.0001        | 33.0  | 49500 | 0.9140          | 0.8918   |
| 0.0003        | 34.0  | 51000 | 1.0008          | 0.8918   |
| 0.0           | 35.0  | 52500 | 0.9887          | 0.9068   |
| 0.0           | 36.0  | 54000 | 0.9050          | 0.9068   |
| 0.034         | 37.0  | 55500 | 0.8779          | 0.9052   |
| 0.0           | 38.0  | 57000 | 1.0913          | 0.8902   |
| 0.0           | 39.0  | 58500 | 1.0222          | 0.9035   |
| 0.0           | 40.0  | 60000 | 1.0156          | 0.9085   |
| 0.0           | 41.0  | 61500 | 1.0518          | 0.9085   |
| 0.0           | 42.0  | 63000 | 1.1525          | 0.9035   |
| 0.0           | 43.0  | 64500 | 1.1836          | 0.9052   |
| 0.0           | 44.0  | 66000 | 1.1521          | 0.9118   |
| 0.0           | 45.0  | 67500 | 1.2277          | 0.8968   |
| 0.0           | 46.0  | 69000 | 1.1742          | 0.9052   |
| 0.0           | 47.0  | 70500 | 1.3084          | 0.8952   |
| 0.0           | 48.0  | 72000 | 1.2268          | 0.9035   |
| 0.0           | 49.0  | 73500 | 1.2357          | 0.9035   |
| 0.0           | 50.0  | 75000 | 1.2561          | 0.9018   |


### 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_lr0001_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9018302828618968, "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