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# SMIDS_1x_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: 0.7508
- Accuracy: 0.7417

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0913        | 1.0   | 150  | 1.0966          | 0.3283   |
| 0.9059        | 2.0   | 300  | 0.9096          | 0.5267   |
| 0.8769        | 3.0   | 450  | 1.1599          | 0.37     |
| 0.9776        | 4.0   | 600  | 0.8247          | 0.5483   |
| 0.8217        | 5.0   | 750  | 0.8153          | 0.5383   |
| 0.8112        | 6.0   | 900  | 0.7620          | 0.5683   |
| 0.8475        | 7.0   | 1050 | 0.8325          | 0.595    |
| 0.7993        | 8.0   | 1200 | 0.7842          | 0.635    |
| 0.7965        | 9.0   | 1350 | 0.7814          | 0.5817   |
| 0.6653        | 10.0  | 1500 | 0.8200          | 0.6167   |
| 0.7897        | 11.0  | 1650 | 0.7410          | 0.6267   |
| 0.7294        | 12.0  | 1800 | 0.7133          | 0.6783   |
| 0.7757        | 13.0  | 1950 | 0.7460          | 0.6417   |
| 0.7358        | 14.0  | 2100 | 0.7160          | 0.6617   |
| 0.6982        | 15.0  | 2250 | 0.7310          | 0.6683   |
| 0.8515        | 16.0  | 2400 | 0.7071          | 0.6883   |
| 0.6388        | 17.0  | 2550 | 0.7043          | 0.6983   |
| 0.8107        | 18.0  | 2700 | 0.7123          | 0.6383   |
| 0.8006        | 19.0  | 2850 | 0.6580          | 0.7083   |
| 0.661         | 20.0  | 3000 | 0.6840          | 0.7217   |
| 0.6954        | 21.0  | 3150 | 0.6814          | 0.6833   |
| 0.6874        | 22.0  | 3300 | 0.6538          | 0.7133   |
| 0.698         | 23.0  | 3450 | 0.6500          | 0.6867   |
| 0.6816        | 24.0  | 3600 | 0.6510          | 0.7183   |
| 0.5843        | 25.0  | 3750 | 0.6528          | 0.735    |
| 0.6161        | 26.0  | 3900 | 0.6489          | 0.7083   |
| 0.6181        | 27.0  | 4050 | 0.6775          | 0.6967   |
| 0.6213        | 28.0  | 4200 | 0.6140          | 0.7617   |
| 0.6111        | 29.0  | 4350 | 0.6332          | 0.7283   |
| 0.5671        | 30.0  | 4500 | 0.6050          | 0.7333   |
| 0.6222        | 31.0  | 4650 | 0.6371          | 0.72     |
| 0.6633        | 32.0  | 4800 | 0.6398          | 0.72     |
| 0.4417        | 33.0  | 4950 | 0.6502          | 0.7283   |
| 0.5811        | 34.0  | 5100 | 0.6594          | 0.71     |
| 0.5622        | 35.0  | 5250 | 0.6961          | 0.715    |
| 0.505         | 36.0  | 5400 | 0.6698          | 0.72     |
| 0.5958        | 37.0  | 5550 | 0.6332          | 0.72     |
| 0.5772        | 38.0  | 5700 | 0.6403          | 0.7417   |
| 0.4157        | 39.0  | 5850 | 0.6591          | 0.7417   |
| 0.567         | 40.0  | 6000 | 0.6466          | 0.7383   |
| 0.4829        | 41.0  | 6150 | 0.6496          | 0.7483   |
| 0.466         | 42.0  | 6300 | 0.6590          | 0.7417   |
| 0.4859        | 43.0  | 6450 | 0.6905          | 0.7367   |
| 0.4438        | 44.0  | 6600 | 0.6726          | 0.7417   |
| 0.4212        | 45.0  | 6750 | 0.6948          | 0.7517   |
| 0.4924        | 46.0  | 6900 | 0.6819          | 0.75     |
| 0.4428        | 47.0  | 7050 | 0.7231          | 0.7533   |
| 0.4447        | 48.0  | 7200 | 0.7306          | 0.755    |
| 0.3456        | 49.0  | 7350 | 0.7449          | 0.7517   |
| 0.398         | 50.0  | 7500 | 0.7508          | 0.7417   |


### 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_1x_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": 0.7416666666666667, "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