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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- fl_image_category_ds
metrics:
- accuracy
model-index:
- name: fl_image_category
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: fl_image_category_ds
type: fl_image_category_ds
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6216216216216216
---
<!-- 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. -->
# fl_image_category
This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co./microsoft/resnet-18) on the fl_image_category_ds dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9667
- Accuracy: 0.6216
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.274 | 1.0 | 88 | 1.2030 | 0.4986 |
| 1.069 | 2.0 | 176 | 1.0716 | 0.5605 |
| 1.0592 | 3.0 | 264 | 1.0385 | 0.5676 |
| 0.9571 | 4.0 | 352 | 0.9746 | 0.6131 |
| 0.8975 | 5.0 | 440 | 0.9667 | 0.6216 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2
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