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# convnextv2-base-22k-224-finetuned-critique-100k

This model is a fine-tuned version of [facebook/convnextv2-base-22k-224](https://huggingface.co/facebook/convnextv2-base-22k-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1309
- Accuracy: 0.9479

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6277        | 0.07  | 50   | 0.5987          | 0.6767   |
| 0.5459        | 0.14  | 100  | 0.5187          | 0.7401   |
| 0.4397        | 0.21  | 150  | 0.4448          | 0.7768   |
| 0.4197        | 0.28  | 200  | 0.3686          | 0.8401   |
| 0.3397        | 0.36  | 250  | 0.3153          | 0.8664   |
| 0.3345        | 0.43  | 300  | 0.3071          | 0.8701   |
| 0.3177        | 0.5   | 350  | 0.2576          | 0.8938   |
| 0.3182        | 0.57  | 400  | 0.2546          | 0.8926   |
| 0.2596        | 0.64  | 450  | 0.2320          | 0.9004   |
| 0.2563        | 0.71  | 500  | 0.2205          | 0.9082   |
| 0.2543        | 0.78  | 550  | 0.2142          | 0.9147   |
| 0.2768        | 0.85  | 600  | 0.2136          | 0.9132   |
| 0.2486        | 0.92  | 650  | 0.2052          | 0.9175   |
| 0.2504        | 1.0   | 700  | 0.2314          | 0.9058   |
| 0.2437        | 1.07  | 750  | 0.1943          | 0.9235   |
| 0.212         | 1.14  | 800  | 0.2019          | 0.9183   |
| 0.1891        | 1.21  | 850  | 0.1845          | 0.9254   |
| 0.2105        | 1.28  | 900  | 0.1834          | 0.9288   |
| 0.2285        | 1.35  | 950  | 0.1994          | 0.9206   |
| 0.2214        | 1.42  | 1000 | 0.1804          | 0.9251   |
| 0.1848        | 1.49  | 1050 | 0.1975          | 0.9196   |
| 0.191         | 1.56  | 1100 | 0.1795          | 0.9269   |
| 0.1794        | 1.64  | 1150 | 0.1606          | 0.9358   |
| 0.2084        | 1.71  | 1200 | 0.1807          | 0.9293   |
| 0.199         | 1.78  | 1250 | 0.1697          | 0.9307   |
| 0.1874        | 1.85  | 1300 | 0.1650          | 0.9372   |
| 0.1681        | 1.92  | 1350 | 0.1515          | 0.939    |
| 0.1696        | 1.99  | 1400 | 0.1473          | 0.9416   |
| 0.1651        | 2.06  | 1450 | 0.1489          | 0.9428   |
| 0.1627        | 2.13  | 1500 | 0.1529          | 0.9395   |
| 0.1754        | 2.2   | 1550 | 0.1540          | 0.9379   |
| 0.1302        | 2.28  | 1600 | 0.1579          | 0.939    |
| 0.1643        | 2.35  | 1650 | 0.1518          | 0.9401   |
| 0.1938        | 2.42  | 1700 | 0.1479          | 0.941    |
| 0.1441        | 2.49  | 1750 | 0.1451          | 0.9436   |
| 0.1478        | 2.56  | 1800 | 0.1324          | 0.9472   |
| 0.1275        | 2.63  | 1850 | 0.1340          | 0.9466   |
| 0.1582        | 2.7   | 1900 | 0.1501          | 0.9391   |
| 0.1472        | 2.77  | 1950 | 0.1354          | 0.9451   |
| 0.1522        | 2.84  | 2000 | 0.1309          | 0.9479   |
| 0.1593        | 2.92  | 2050 | 0.1433          | 0.9452   |
| 0.1541        | 2.99  | 2100 | 0.1381          | 0.9466   |
| 0.1297        | 3.06  | 2150 | 0.1320          | 0.9479   |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.13.1
- Datasets 2.13.1
- Tokenizers 0.13.3

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