metadata
library_name: transformers
license: apache-2.0
base_model: CGCTG/orientation_resnet-50
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: orientation_resnet-50
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: 0.7597684515195369
orientation_resnet-50
This model is a fine-tuned version of CGCTG/orientation_resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.5106
- Accuracy: 0.7598
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: 0.05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7836 | 1.0 | 87 | 1.2301 | 0.4986 |
| 0.6808 | 2.0 | 174 | 0.6659 | 0.6382 |
| 0.5641 | 2.9711 | 258 | 0.5106 | 0.7598 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0