transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_base_rms_001_fold2
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7953410981697171
---

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# smids_5x_deit_base_rms_001_fold2

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6861
- Accuracy: 0.7953

## 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: 32
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9457        | 1.0   | 375   | 0.8476          | 0.5324   |
| 0.8382        | 2.0   | 750   | 0.8448          | 0.5391   |
| 0.809         | 3.0   | 1125  | 0.7879          | 0.5424   |
| 0.745         | 4.0   | 1500  | 0.7390          | 0.6323   |
| 0.7171        | 5.0   | 1875  | 0.7030          | 0.6622   |
| 0.6968        | 6.0   | 2250  | 0.7889          | 0.6456   |
| 0.6875        | 7.0   | 2625  | 0.6388          | 0.7072   |
| 0.6501        | 8.0   | 3000  | 0.6553          | 0.7038   |
| 0.651         | 9.0   | 3375  | 0.5748          | 0.7354   |
| 0.6283        | 10.0  | 3750  | 0.5788          | 0.7421   |
| 0.6238        | 11.0  | 4125  | 0.5592          | 0.7621   |
| 0.6557        | 12.0  | 4500  | 0.5567          | 0.7471   |
| 0.5365        | 13.0  | 4875  | 0.5457          | 0.7704   |
| 0.6238        | 14.0  | 5250  | 0.5542          | 0.7571   |
| 0.5702        | 15.0  | 5625  | 0.5827          | 0.7321   |
| 0.4879        | 16.0  | 6000  | 0.5598          | 0.7671   |
| 0.5127        | 17.0  | 6375  | 0.5330          | 0.7687   |
| 0.4935        | 18.0  | 6750  | 0.5680          | 0.7537   |
| 0.5245        | 19.0  | 7125  | 0.5110          | 0.7837   |
| 0.4876        | 20.0  | 7500  | 0.5252          | 0.7804   |
| 0.4771        | 21.0  | 7875  | 0.5233          | 0.7770   |
| 0.5009        | 22.0  | 8250  | 0.5096          | 0.8003   |
| 0.4963        | 23.0  | 8625  | 0.5344          | 0.7870   |
| 0.4814        | 24.0  | 9000  | 0.5289          | 0.8087   |
| 0.4304        | 25.0  | 9375  | 0.5706          | 0.7854   |
| 0.4721        | 26.0  | 9750  | 0.5627          | 0.7637   |
| 0.4743        | 27.0  | 10125 | 0.5566          | 0.7770   |
| 0.387         | 28.0  | 10500 | 0.5497          | 0.7953   |
| 0.4853        | 29.0  | 10875 | 0.5210          | 0.7887   |
| 0.347         | 30.0  | 11250 | 0.5276          | 0.8120   |
| 0.4668        | 31.0  | 11625 | 0.5138          | 0.8136   |
| 0.424         | 32.0  | 12000 | 0.5446          | 0.7787   |
| 0.4005        | 33.0  | 12375 | 0.5357          | 0.8020   |
| 0.4114        | 34.0  | 12750 | 0.5259          | 0.7903   |
| 0.3661        | 35.0  | 13125 | 0.5604          | 0.7903   |
| 0.3542        | 36.0  | 13500 | 0.5807          | 0.7987   |
| 0.3711        | 37.0  | 13875 | 0.5589          | 0.8037   |
| 0.3372        | 38.0  | 14250 | 0.5483          | 0.8003   |
| 0.3656        | 39.0  | 14625 | 0.6008          | 0.7704   |
| 0.3244        | 40.0  | 15000 | 0.5754          | 0.8120   |
| 0.3484        | 41.0  | 15375 | 0.5934          | 0.7970   |
| 0.3143        | 42.0  | 15750 | 0.6293          | 0.7937   |
| 0.3053        | 43.0  | 16125 | 0.5834          | 0.7920   |
| 0.3117        | 44.0  | 16500 | 0.5894          | 0.7970   |
| 0.3041        | 45.0  | 16875 | 0.6216          | 0.7887   |
| 0.284         | 46.0  | 17250 | 0.6533          | 0.7953   |
| 0.36          | 47.0  | 17625 | 0.6167          | 0.7987   |
| 0.2345        | 48.0  | 18000 | 0.6554          | 0.8003   |
| 0.2899        | 49.0  | 18375 | 0.6795          | 0.7937   |
| 0.264         | 50.0  | 18750 | 0.6861          | 0.7953   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
