metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: image_classification_for_fracture
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: 0.85
image_classification_for_fracture
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.4783
- Accuracy: 0.85
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 |
|---|---|---|---|---|
| No log | 0.8 | 2 | 0.6696 | 0.75 |
| No log | 2.0 | 5 | 0.6296 | 0.7 |
| No log | 2.8 | 7 | 0.5853 | 0.775 |
| 0.639 | 4.0 | 10 | 0.5731 | 0.8 |
| 0.639 | 4.8 | 12 | 0.5430 | 0.825 |
| 0.639 | 6.0 | 15 | 0.5223 | 0.85 |
| 0.639 | 6.8 | 17 | 0.5036 | 0.8 |
| 0.5453 | 8.0 | 20 | 0.4783 | 0.85 |
Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2