metadata
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-18
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: 0.6425188074672611
resnet-18
This model was trained from scratch on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.9403
- Accuracy: 0.6425
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: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
1.4726 | 1.0 | 252 | 1.3072 | 0.5068 |
1.2683 | 2.0 | 505 | 1.0996 | 0.5865 |
1.2177 | 3.0 | 757 | 1.0444 | 0.6096 |
1.1636 | 4.0 | 1010 | 1.0185 | 0.6096 |
1.1372 | 5.0 | 1262 | 0.9945 | 0.6205 |
1.113 | 6.0 | 1515 | 0.9703 | 0.6342 |
1.0734 | 7.0 | 1767 | 0.9574 | 0.6333 |
1.0501 | 8.0 | 2020 | 0.9503 | 0.6375 |
1.0361 | 9.0 | 2272 | 0.9488 | 0.6389 |
1.0302 | 9.98 | 2520 | 0.9403 | 0.6425 |
Framework versions
- Transformers 4.30.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3