| | --- |
| | library_name: transformers |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - imagefolder |
| | metrics: |
| | - accuracy |
| | - f1 |
| | model-index: |
| | - name: resnet-kitchen-object |
| | 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: 0.650461837627613 |
| | - name: F1 |
| | type: f1 |
| | value: 0.6481801350383302 |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # resnet-kitchen-object |
| |
|
| | This model is a fine-tuned version of [](https://huggingface.co/) on the imagefolder dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 1.1453 |
| | - Accuracy: 0.6505 |
| | - F1: 0.6482 |
| |
|
| | ## 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: 32 |
| | - eval_batch_size: 32 |
| | - seed: 42 |
| | - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: cosine |
| | - lr_scheduler_warmup_ratio: 0.05 |
| | - num_epochs: 14 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
| | | 2.0106 | 1.0 | 224 | 2.3071 | 0.2022 | 0.1647 | |
| | | 1.7945 | 2.0 | 448 | 1.8394 | 0.3369 | 0.3385 | |
| | | 1.6123 | 3.0 | 672 | 1.8258 | 0.3709 | 0.3426 | |
| | | 1.5264 | 4.0 | 896 | 1.7281 | 0.4088 | 0.4060 | |
| | | 1.3383 | 5.0 | 1120 | 1.7189 | 0.4093 | 0.4109 | |
| | | 1.254 | 6.0 | 1344 | 1.4396 | 0.5012 | 0.4885 | |
| | | 1.1198 | 7.0 | 1568 | 1.4400 | 0.5090 | 0.5126 | |
| | | 0.9935 | 8.0 | 1792 | 1.5129 | 0.5177 | 0.5282 | |
| | | 0.8163 | 9.0 | 2016 | 1.2204 | 0.6067 | 0.6020 | |
| | | 0.5996 | 10.0 | 2240 | 1.2234 | 0.6179 | 0.6069 | |
| | | 0.4508 | 11.0 | 2464 | 1.1936 | 0.6354 | 0.6288 | |
| | | 0.3668 | 12.0 | 2688 | 1.1787 | 0.6364 | 0.6313 | |
| | | 0.2702 | 13.0 | 2912 | 1.1435 | 0.6441 | 0.6427 | |
| | | 0.2471 | 14.0 | 3136 | 1.1453 | 0.6505 | 0.6482 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.57.1 |
| | - Pytorch 2.8.0+cu126 |
| | - Datasets 4.0.0 |
| | - Tokenizers 0.22.1 |
| |
|
