metadata
library_name: peft
license: apache-2.0
base_model: google/vit-large-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-large-patch16-224-in21k-testing-dungeons-lora-27Dec24-0001
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: rotated_maps
type: imagefolder
config: default
split: validation
args: default
metrics:
- type: accuracy
value: 0.9607142857142857
name: Accuracy
vit-large-patch16-224-in21k-testing-dungeons-lora-27Dec24-0001
This model is a fine-tuned version of google/vit-large-patch16-224-in21k on the rotated_maps dataset. It achieves the following results on the evaluation set:
- Loss: 0.1076
- Accuracy: 0.9607
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: 0.005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 12
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.7273 | 2 | 1.5502 | 0.2946 |
| No log | 1.7273 | 4 | 1.1408 | 0.6696 |
| No log | 2.7273 | 6 | 1.0113 | 0.6036 |
| No log | 3.7273 | 8 | 0.6030 | 0.8411 |
| 5.1081 | 4.7273 | 10 | 0.4665 | 0.8625 |
| 5.1081 | 5.7273 | 12 | 0.4145 | 0.8643 |
| 5.1081 | 6.7273 | 14 | 0.2846 | 0.9107 |
| 5.1081 | 7.7273 | 16 | 0.2386 | 0.9125 |
| 5.1081 | 8.7273 | 18 | 0.1564 | 0.9554 |
| 0.7653 | 9.7273 | 20 | 0.1178 | 0.9679 |
| 0.7653 | 10.7273 | 22 | 0.1241 | 0.9536 |
| 0.7653 | 11.7273 | 24 | 0.1076 | 0.9607 |
Framework versions
- PEFT 0.14.0
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0