metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-50-shortSleeveCleanedData
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: 0.9781420765027322
resnet-50-shortSleeveCleanedData
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: 0.1103
- Accuracy: 0.9781
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 7
- total_train_batch_size: 56
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.973 | 1.0 | 147 | 0.9371 | 0.7268 |
| 0.6565 | 2.0 | 294 | 0.5520 | 0.8710 |
| 0.4609 | 3.0 | 441 | 0.2983 | 0.9279 |
| 0.3937 | 4.0 | 588 | 0.2051 | 0.9486 |
| 0.3723 | 5.0 | 735 | 0.1521 | 0.9727 |
| 0.3926 | 6.0 | 882 | 0.1490 | 0.9672 |
| 0.3326 | 7.0 | 1029 | 0.1367 | 0.9650 |
| 0.3166 | 8.0 | 1176 | 0.1109 | 0.9738 |
| 0.3492 | 9.0 | 1323 | 0.1108 | 0.9760 |
| 0.3228 | 10.0 | 1470 | 0.1103 | 0.9781 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3