metadata
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: 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: 0.9310344827586207
resnet-50
This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.6922
- Accuracy: 0.9310
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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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 | 0.9655 | 7 | 0.6922 | 0.9310 |
| 0.6927 | 1.9310 | 14 | 0.6895 | 0.9310 |
| 0.6916 | 2.8966 | 21 | 0.6878 | 0.9310 |
| 0.6916 | 4.0 | 29 | 0.6853 | 0.9310 |
| 0.6899 | 4.9655 | 36 | 0.6839 | 0.9310 |
| 0.6878 | 5.9310 | 43 | 0.6811 | 0.9310 |
| 0.6868 | 6.8966 | 50 | 0.6826 | 0.9310 |
| 0.6868 | 8.0 | 58 | 0.6804 | 0.9310 |
| 0.6864 | 8.9655 | 65 | 0.6801 | 0.9310 |
| 0.686 | 9.6552 | 70 | 0.6800 | 0.9310 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1