metadata
license: apache-2.0
base_model: microsoft/resnet-152
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Resnet152-30VN
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8353174603174603
Resnet152-30VN
This model is a fine-tuned version of microsoft/resnet-152 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.5769
- Accuracy: 0.8353
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: 0.0003
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|---|---|---|---|---|
| 1.4198 | 1.0 | 275 | 0.7348 | 0.8741 |
| 0.565 | 2.0 | 550 | 0.8119 | 0.6347 |
| 0.2846 | 3.0 | 825 | 0.8310 | 0.6003 |
| 0.1727 | 4.0 | 1100 | 0.8410 | 0.6041 |
| 0.0835 | 5.0 | 1375 | 0.8461 | 0.6464 |
| 0.0534 | 6.0 | 1650 | 0.8565 | 0.6776 |
| 0.0283 | 7.0 | 1925 | 0.7107 | 0.8501 |
| 0.0186 | 8.0 | 2200 | 0.7066 | 0.8620 |
| 0.0111 | 9.0 | 2475 | 0.6772 | 0.8648 |
| 0.0096 | 10.0 | 2750 | 0.6898 | 0.8628 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2