---
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