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---
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
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