metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
base_model: microsoft/resnet-50
model-index:
- name: resnet-50-finetuned-eurosat
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- type: accuracy
value: 0.8239812959251837
name: Accuracy
resnet-50-finetuned-eurosat
This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.9095
- Accuracy: 0.8240
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: 0.0001
- 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.78 | 0.96 | 17 | 1.7432 | 0.4321 |
| 1.7105 | 1.96 | 34 | 1.6596 | 0.6307 |
| 1.6045 | 2.96 | 51 | 1.5369 | 0.6758 |
| 1.6526 | 3.96 | 68 | 1.4111 | 0.7139 |
| 1.4018 | 4.96 | 85 | 1.2686 | 0.7602 |
| 1.2812 | 5.96 | 102 | 1.1433 | 0.7714 |
| 1.3282 | 6.96 | 119 | 1.0643 | 0.7910 |
| 1.1246 | 7.96 | 136 | 0.9794 | 0.8133 |
| 1.0731 | 8.96 | 153 | 0.9279 | 0.8087 |
| 1.0531 | 9.96 | 170 | 0.9095 | 0.8240 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1