metadata
license: apache-2.0
base_model: LaLegumbreArtificial/Fraunhofer_Classical
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Fraunhofer_Classical_multiclass
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.761384335154827
Fraunhofer_Classical_multiclass
This model is a fine-tuned version of LaLegumbreArtificial/Fraunhofer_Classical on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.1740
- Accuracy: 0.7614
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: 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0716 | 0.9976 | 208 | 0.8637 | 0.7752 |
| 0.0478 | 2.0 | 417 | 0.7157 | 0.8339 |
| 0.0408 | 2.9976 | 625 | 0.9172 | 0.8080 |
| 0.031 | 4.0 | 834 | 0.9607 | 0.8104 |
| 0.0258 | 4.9880 | 1040 | 1.1740 | 0.7614 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1