metadata
license: apache-2.0
base_model: facebook/convnextv2-tiny-1k-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: convnextv2-tiny-1k-224-finetuned-eurosat
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: 0.625
convnextv2-tiny-1k-224-finetuned-eurosat
This model is a fine-tuned version of facebook/convnextv2-tiny-1k-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.2021
- Accuracy: 0.625
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: 130
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 1 | 2.0300 | 0.0 |
| No log | 2.0 | 3 | 2.0208 | 0.0 |
| No log | 3.0 | 5 | 1.9970 | 0.0 |
| No log | 4.0 | 6 | 1.9853 | 0.125 |
| No log | 5.0 | 7 | 1.9666 | 0.125 |
| No log | 6.0 | 9 | 1.9215 | 0.25 |
| 1.024 | 7.0 | 11 | 1.8757 | 0.125 |
| 1.024 | 8.0 | 12 | 1.8580 | 0.125 |
| 1.024 | 9.0 | 13 | 1.8413 | 0.125 |
| 1.024 | 10.0 | 15 | 1.7954 | 0.375 |
| 1.024 | 11.0 | 17 | 1.7510 | 0.5 |
| 1.024 | 12.0 | 18 | 1.7309 | 0.625 |
| 1.024 | 13.0 | 19 | 1.7132 | 0.625 |
| 0.8487 | 14.0 | 21 | 1.6768 | 0.625 |
| 0.8487 | 15.0 | 23 | 1.6402 | 0.625 |
| 0.8487 | 16.0 | 24 | 1.6197 | 0.625 |
| 0.8487 | 17.0 | 25 | 1.5952 | 0.625 |
| 0.8487 | 18.0 | 27 | 1.5259 | 0.625 |
| 0.8487 | 19.0 | 29 | 1.4599 | 0.625 |
| 0.6549 | 20.0 | 30 | 1.4526 | 0.625 |
| 0.6549 | 21.0 | 31 | 1.4459 | 0.625 |
| 0.6549 | 22.0 | 33 | 1.4222 | 0.625 |
| 0.6549 | 23.0 | 35 | 1.4136 | 0.625 |
| 0.6549 | 24.0 | 36 | 1.4238 | 0.625 |
| 0.6549 | 25.0 | 37 | 1.4286 | 0.625 |
| 0.6549 | 26.0 | 39 | 1.4231 | 0.625 |
| 0.479 | 27.0 | 41 | 1.3964 | 0.625 |
| 0.479 | 28.0 | 42 | 1.3757 | 0.625 |
| 0.479 | 29.0 | 43 | 1.3501 | 0.625 |
| 0.479 | 30.0 | 45 | 1.2779 | 0.625 |
| 0.479 | 31.0 | 47 | 1.2360 | 0.625 |
| 0.479 | 32.0 | 48 | 1.2185 | 0.625 |
| 0.479 | 33.0 | 49 | 1.1920 | 0.625 |
| 0.3504 | 34.0 | 51 | 1.1326 | 0.625 |
| 0.3504 | 35.0 | 53 | 1.1018 | 0.625 |
| 0.3504 | 36.0 | 54 | 1.0970 | 0.625 |
| 0.3504 | 37.0 | 55 | 1.1030 | 0.625 |
| 0.3504 | 38.0 | 57 | 1.1378 | 0.625 |
| 0.3504 | 39.0 | 59 | 1.1720 | 0.625 |
| 0.2864 | 40.0 | 60 | 1.1867 | 0.625 |
| 0.2864 | 41.0 | 61 | 1.1960 | 0.625 |
| 0.2864 | 42.0 | 63 | 1.1959 | 0.625 |
| 0.2864 | 43.0 | 65 | 1.1727 | 0.625 |
| 0.2864 | 44.0 | 66 | 1.1653 | 0.625 |
| 0.2864 | 45.0 | 67 | 1.1644 | 0.625 |
| 0.2864 | 46.0 | 69 | 1.1809 | 0.625 |
| 0.2357 | 47.0 | 71 | 1.1902 | 0.625 |
| 0.2357 | 48.0 | 72 | 1.1872 | 0.625 |
| 0.2357 | 49.0 | 73 | 1.1894 | 0.625 |
| 0.2357 | 50.0 | 75 | 1.1982 | 0.625 |
| 0.2357 | 51.0 | 77 | 1.2418 | 0.625 |
| 0.2357 | 52.0 | 78 | 1.2575 | 0.625 |
| 0.2357 | 53.0 | 79 | 1.2708 | 0.625 |
| 0.1561 | 54.0 | 81 | 1.2666 | 0.625 |
| 0.1561 | 55.0 | 83 | 1.2241 | 0.625 |
| 0.1561 | 56.0 | 84 | 1.2089 | 0.625 |
| 0.1561 | 57.0 | 85 | 1.1914 | 0.625 |
| 0.1561 | 58.0 | 87 | 1.1559 | 0.625 |
| 0.1561 | 59.0 | 89 | 1.1387 | 0.625 |
| 0.1453 | 60.0 | 90 | 1.1337 | 0.625 |
| 0.1453 | 61.0 | 91 | 1.1290 | 0.625 |
| 0.1453 | 62.0 | 93 | 1.1369 | 0.625 |
| 0.1453 | 63.0 | 95 | 1.1439 | 0.625 |
| 0.1453 | 64.0 | 96 | 1.1448 | 0.625 |
| 0.1453 | 65.0 | 97 | 1.1530 | 0.625 |
| 0.1453 | 66.0 | 99 | 1.1718 | 0.625 |
| 0.1271 | 67.0 | 101 | 1.1965 | 0.625 |
| 0.1271 | 68.0 | 102 | 1.2092 | 0.625 |
| 0.1271 | 69.0 | 103 | 1.2176 | 0.625 |
| 0.1271 | 70.0 | 105 | 1.2337 | 0.625 |
| 0.1271 | 71.0 | 107 | 1.2376 | 0.625 |
| 0.1271 | 72.0 | 108 | 1.2384 | 0.625 |
| 0.1271 | 73.0 | 109 | 1.2378 | 0.625 |
| 0.1153 | 74.0 | 111 | 1.2385 | 0.625 |
| 0.1153 | 75.0 | 113 | 1.2316 | 0.625 |
| 0.1153 | 76.0 | 114 | 1.2274 | 0.625 |
| 0.1153 | 77.0 | 115 | 1.2252 | 0.625 |
| 0.1153 | 78.0 | 117 | 1.2196 | 0.625 |
| 0.1153 | 79.0 | 119 | 1.2145 | 0.625 |
| 0.0882 | 80.0 | 120 | 1.2130 | 0.625 |
| 0.0882 | 81.0 | 121 | 1.2117 | 0.625 |
| 0.0882 | 82.0 | 123 | 1.2097 | 0.625 |
| 0.0882 | 83.0 | 125 | 1.2075 | 0.625 |
| 0.0882 | 84.0 | 126 | 1.2054 | 0.625 |
| 0.0882 | 85.0 | 127 | 1.2039 | 0.625 |
| 0.0882 | 86.0 | 129 | 1.2025 | 0.625 |
| 0.0987 | 86.6667 | 130 | 1.2021 | 0.625 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1