---
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: resnet-50-base-beans-demo
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: beans
      type: beans
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9699248120300752
---

<!-- 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-50-base-beans-demo

This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1014
- Accuracy: 0.9699

## 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: 0.002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9524        | 1.0   | 130  | 0.2826          | 0.8647   |
| 0.3596        | 2.0   | 260  | 0.2216          | 0.9023   |
| 0.2419        | 3.0   | 390  | 0.1324          | 0.9474   |
| 0.3248        | 4.0   | 520  | 0.1124          | 0.9699   |
| 0.1557        | 5.0   | 650  | 0.1014          | 0.9699   |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu102
- Datasets 2.2.1
- Tokenizers 0.12.1