---
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: 0.9022556390977443
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: beans
      type: beans
      config: default
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.921875
      verified: true
    - name: Precision Macro
      type: precision
      value: 0.929318068690891
      verified: true
    - name: Precision Micro
      type: precision
      value: 0.921875
      verified: true
    - name: Precision Weighted
      type: precision
      value: 0.9287658661025386
      verified: true
    - name: Recall Macro
      type: recall
      value: 0.9217423403469915
      verified: true
    - name: Recall Micro
      type: recall
      value: 0.921875
      verified: true
    - name: Recall Weighted
      type: recall
      value: 0.921875
      verified: true
    - name: F1 Macro
      type: f1
      value: 0.9232919254658385
      verified: true
    - name: F1 Micro
      type: f1
      value: 0.921875
      verified: true
    - name: F1 Weighted
      type: f1
      value: 0.9230832686335404
      verified: true
    - name: loss
      type: loss
      value: 0.20743493735790253
      verified: true
---

<!-- 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: 0.2188
- Accuracy: 0.9023

## 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: 1.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5679        | 1.0   | 130  | 0.2188          | 0.9023   |


### Framework versions

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