metadata
license: apache-2.0
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
convnext-tiny-224-finetuned-eurosat-albumentations
This model is a fine-tuned version of paom/convnext-tiny-224-finetuned-eurosat-albumentations on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.6948
- Accuracy: 0.0
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: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 1 | 0.6948 | 0.0 |
| No log | 2.0 | 2 | 0.5057 | 0.0 |
| No log | 3.0 | 3 | 0.2286 | 0.0 |
| No log | 4.0 | 4 | 0.0823 | 0.0 |
| No log | 5.0 | 5 | 0.0320 | 0.0 |
| No log | 6.0 | 6 | 0.0489 | 0.0 |
| No log | 7.0 | 7 | 0.0881 | 0.0 |
| No log | 8.0 | 8 | 0.1134 | 0.0 |
| No log | 9.0 | 9 | 0.1179 | 0.0 |
| 0.0638 | 10.0 | 10 | 0.1054 | 0.0 |
| 0.0638 | 11.0 | 11 | 0.0826 | 0.0 |
| 0.0638 | 12.0 | 12 | 0.0587 | 0.0 |
| 0.0638 | 13.0 | 13 | 0.0386 | 0.0 |
| 0.0638 | 14.0 | 14 | 0.0241 | 0.0 |
| 0.0638 | 15.0 | 15 | 0.0158 | 0.0 |
| 0.0638 | 16.0 | 16 | 0.0115 | 0.0 |
| 0.0638 | 17.0 | 17 | 0.0096 | 0.0 |
| 0.0638 | 18.0 | 18 | 0.0087 | 0.0 |
| 0.0638 | 19.0 | 19 | 0.0084 | 0.0 |
| 0.0048 | 20.0 | 20 | 0.0083 | 0.0 |
Framework versions
- Transformers 4.29.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3