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---
license: apache-2.0
base_model: facebook/convnext-tiny-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: convnext-tiny-224-finetuned-eurosat-albumentations
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.5
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# convnext-tiny-224-finetuned-eurosat-albumentations
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3086
- Accuracy: 0.5
## 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 5 | 2.0663 | 0.1562 |
| 2.0637 | 2.0 | 10 | 2.0571 | 0.1812 |
| 2.0637 | 3.0 | 15 | 2.0389 | 0.1938 |
| 2.0295 | 4.0 | 20 | 2.0109 | 0.2437 |
| 2.0295 | 5.0 | 25 | 1.9754 | 0.2875 |
| 1.9452 | 6.0 | 30 | 1.9335 | 0.2938 |
| 1.9452 | 7.0 | 35 | 1.8869 | 0.275 |
| 1.8289 | 8.0 | 40 | 1.8378 | 0.2812 |
| 1.8289 | 9.0 | 45 | 1.7800 | 0.3563 |
| 1.7075 | 10.0 | 50 | 1.7231 | 0.3563 |
| 1.7075 | 11.0 | 55 | 1.6730 | 0.3625 |
| 1.5909 | 12.0 | 60 | 1.6253 | 0.3688 |
| 1.5909 | 13.0 | 65 | 1.5897 | 0.3875 |
| 1.4997 | 14.0 | 70 | 1.5604 | 0.4 |
| 1.4997 | 15.0 | 75 | 1.5336 | 0.425 |
| 1.4066 | 16.0 | 80 | 1.5147 | 0.425 |
| 1.4066 | 17.0 | 85 | 1.4923 | 0.425 |
| 1.3344 | 18.0 | 90 | 1.4744 | 0.4375 |
| 1.3344 | 19.0 | 95 | 1.4615 | 0.4437 |
| 1.2545 | 20.0 | 100 | 1.4479 | 0.4437 |
| 1.2545 | 21.0 | 105 | 1.4311 | 0.45 |
| 1.1789 | 22.0 | 110 | 1.4222 | 0.475 |
| 1.1789 | 23.0 | 115 | 1.4099 | 0.4813 |
| 1.1186 | 24.0 | 120 | 1.3926 | 0.4688 |
| 1.1186 | 25.0 | 125 | 1.3835 | 0.4625 |
| 1.0685 | 26.0 | 130 | 1.3747 | 0.4625 |
| 1.0685 | 27.0 | 135 | 1.3622 | 0.4625 |
| 0.9935 | 28.0 | 140 | 1.3523 | 0.4688 |
| 0.9935 | 29.0 | 145 | 1.3514 | 0.45 |
| 0.9453 | 30.0 | 150 | 1.3413 | 0.4688 |
| 0.9453 | 31.0 | 155 | 1.3334 | 0.45 |
| 0.9162 | 32.0 | 160 | 1.3239 | 0.45 |
| 0.9162 | 33.0 | 165 | 1.3177 | 0.475 |
| 0.8637 | 34.0 | 170 | 1.3090 | 0.475 |
| 0.8637 | 35.0 | 175 | 1.3078 | 0.4938 |
| 0.8298 | 36.0 | 180 | 1.3086 | 0.5 |
| 0.8298 | 37.0 | 185 | 1.2990 | 0.5 |
| 0.7801 | 38.0 | 190 | 1.2975 | 0.4938 |
| 0.7801 | 39.0 | 195 | 1.2946 | 0.4938 |
| 0.7691 | 40.0 | 200 | 1.2921 | 0.4875 |
| 0.7691 | 41.0 | 205 | 1.2913 | 0.4938 |
| 0.7409 | 42.0 | 210 | 1.2902 | 0.4875 |
| 0.7409 | 43.0 | 215 | 1.2886 | 0.4875 |
| 0.7223 | 44.0 | 220 | 1.2860 | 0.4938 |
| 0.7223 | 45.0 | 225 | 1.2849 | 0.4875 |
| 0.7091 | 46.0 | 230 | 1.2849 | 0.4875 |
| 0.7091 | 47.0 | 235 | 1.2854 | 0.4875 |
| 0.6915 | 48.0 | 240 | 1.2845 | 0.4875 |
| 0.6915 | 49.0 | 245 | 1.2842 | 0.4875 |
| 0.6917 | 50.0 | 250 | 1.2840 | 0.4875 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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