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# convnextv2-tiny-1k-224-finetuned-eurosat-50

This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2472
- Accuracy: 0.7763

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9434        | 0.97  | 18   | 1.8549          | 0.2847   |
| 1.7722        | 2.0   | 37   | 1.6757          | 0.3661   |
| 1.5502        | 2.97  | 55   | 1.4652          | 0.4339   |
| 1.2595        | 4.0   | 74   | 1.1916          | 0.6068   |
| 0.9304        | 4.97  | 92   | 1.0282          | 0.6576   |
| 0.7333        | 6.0   | 111  | 0.8574          | 0.7051   |
| 0.6015        | 6.97  | 129  | 0.8427          | 0.6983   |
| 0.4617        | 8.0   | 148  | 0.7682          | 0.7458   |
| 0.3162        | 8.97  | 166  | 0.7453          | 0.7559   |
| 0.2249        | 10.0  | 185  | 0.7475          | 0.7661   |
| 0.1667        | 10.97 | 203  | 0.7677          | 0.7492   |
| 0.091         | 12.0  | 222  | 1.0114          | 0.7220   |
| 0.0783        | 12.97 | 240  | 1.0206          | 0.7186   |
| 0.0613        | 14.0  | 259  | 0.8466          | 0.7492   |
| 0.0703        | 14.97 | 277  | 1.1067          | 0.7119   |
| 0.0335        | 16.0  | 296  | 1.0117          | 0.7390   |
| 0.0171        | 16.97 | 314  | 0.9367          | 0.7525   |
| 0.0253        | 18.0  | 333  | 1.3196          | 0.7153   |
| 0.0201        | 18.97 | 351  | 1.0530          | 0.7525   |
| 0.0041        | 20.0  | 370  | 1.0523          | 0.7729   |
| 0.0154        | 20.97 | 388  | 1.1311          | 0.7661   |
| 0.0025        | 22.0  | 407  | 1.1477          | 0.7729   |
| 0.0036        | 22.97 | 425  | 1.1309          | 0.7627   |
| 0.002         | 24.0  | 444  | 1.1399          | 0.7729   |
| 0.0014        | 24.97 | 462  | 1.1543          | 0.7797   |
| 0.0011        | 26.0  | 481  | 1.1799          | 0.7763   |
| 0.0011        | 26.97 | 499  | 1.1579          | 0.7661   |
| 0.0009        | 28.0  | 518  | 1.1907          | 0.7627   |
| 0.0009        | 28.97 | 536  | 1.1878          | 0.7661   |
| 0.0008        | 30.0  | 555  | 1.1986          | 0.7661   |
| 0.0008        | 30.97 | 573  | 1.2051          | 0.7661   |
| 0.0007        | 32.0  | 592  | 1.2073          | 0.7661   |
| 0.0007        | 32.97 | 610  | 1.2156          | 0.7661   |
| 0.0007        | 34.0  | 629  | 1.2218          | 0.7627   |
| 0.0007        | 34.97 | 647  | 1.2173          | 0.7661   |
| 0.0006        | 36.0  | 666  | 1.2217          | 0.7729   |
| 0.0006        | 36.97 | 684  | 1.2272          | 0.7695   |
| 0.0006        | 38.0  | 703  | 1.2261          | 0.7763   |
| 0.0006        | 38.97 | 721  | 1.2305          | 0.7763   |
| 0.0006        | 40.0  | 740  | 1.2325          | 0.7763   |
| 0.0005        | 40.97 | 758  | 1.2362          | 0.7763   |
| 0.0005        | 42.0  | 777  | 1.2409          | 0.7763   |
| 0.0005        | 42.97 | 795  | 1.2422          | 0.7763   |
| 0.0005        | 44.0  | 814  | 1.2429          | 0.7729   |
| 0.0005        | 44.97 | 832  | 1.2434          | 0.7763   |
| 0.0005        | 46.0  | 851  | 1.2458          | 0.7763   |
| 0.0005        | 46.97 | 869  | 1.2468          | 0.7763   |
| 0.0005        | 48.0  | 888  | 1.2471          | 0.7763   |
| 0.0005        | 48.65 | 900  | 1.2472          | 0.7763   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3

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