metadata
library_name: transformers
license: apache-2.0
base_model: itsLeen/swin-large-ai-or-not
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-large-ai-or-not
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train[:2000]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.98
swin-large-ai-or-not
This model is a fine-tuned version of itsLeen/swin-large-ai-or-not on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.1841
- Accuracy: 0.98
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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0095 | 4.0 | 100 | 0.2067 | 0.97 |
| 0.0013 | 8.0 | 200 | 0.1890 | 0.9725 |
| 0.0014 | 12.0 | 300 | 0.2007 | 0.9725 |
| 0.0003 | 16.0 | 400 | 0.1674 | 0.98 |
| 0.0001 | 20.0 | 500 | 0.1841 | 0.98 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1