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# 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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["fl_image_category_ds"], "metrics": ["accuracy"], "model-index": [{"name": "fl_image_category", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "fl_image_category_ds", "type": "fl_image_category_ds", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6216216216216216, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard resnet image-classification generated_from_trainer dataset:fl_image_category_ds license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #dataset-fl_image_category_ds #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

fl\_image\_category
===================


This model is a fine-tuned version of 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



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1
* Datasets 2.8.0
* Tokenizers 0.13.2



### 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 ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #dataset-fl_image_category_ds #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #dataset-fl_image_category_ds #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1
* Datasets 2.8.0
* Tokenizers 0.13.2