---
library_name: transformers
license: apache-2.0
base_model: microsoft/resnet-50
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: 0.8677685950413223
    - name: F1
      type: f1
      value: 0.8678765100301015
---

<!-- 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 [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4314
- Accuracy: 0.8678
- F1: 0.8679

## 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: 16
- eval_batch_size: 16
- 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: 12
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 2.0478        | 1.0   | 447  | 1.8372          | 0.5119   | 0.5147 |
| 0.9774        | 2.0   | 894  | 0.7962          | 0.7832   | 0.7816 |
| 0.6756        | 3.0   | 1341 | 0.5893          | 0.8221   | 0.8211 |
| 0.5692        | 4.0   | 1788 | 0.5361          | 0.8347   | 0.8349 |
| 0.5087        | 5.0   | 2235 | 0.5034          | 0.8439   | 0.8438 |
| 0.4525        | 6.0   | 2682 | 0.4738          | 0.8483   | 0.8480 |
| 0.4211        | 7.0   | 3129 | 0.4518          | 0.8610   | 0.8604 |
| 0.4156        | 8.0   | 3576 | 0.4418          | 0.8629   | 0.8628 |
| 0.3394        | 9.0   | 4023 | 0.4394          | 0.8663   | 0.8659 |
| 0.3452        | 10.0  | 4470 | 0.4341          | 0.8653   | 0.8654 |
| 0.3121        | 11.0  | 4917 | 0.4457          | 0.8658   | 0.8655 |
| 0.3392        | 12.0  | 5364 | 0.4314          | 0.8678   | 0.8679 |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1