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# my-cool-timm-model-2

This model is a fine-tuned version of [resnet18](https://huggingface.co/resnet18) on the cats_vs_dogs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2510
- Acc1: 95.2150
- Acc5: 100.0

## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Acc1    | Acc5  |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-----:|
| No log        | 0.07  | 5    | 0.3436          | 92.0820 | 100.0 |
| 0.4914        | 0.14  | 10   | 0.2510          | 95.2150 | 100.0 |


### Framework versions

- Transformers 4.12.3
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3

{"tags": ["image-classification", "timm", "generated_from_trainer"], "datasets": ["cats_vs_dogs"], "library_tag": "timm"}
timm pytorch tensorboard image-classification generated_from_trainer dataset:cats_vs_dogs region:us
TAGS
#timm #pytorch #tensorboard #image-classification #generated_from_trainer #dataset-cats_vs_dogs #region-us 

my-cool-timm-model-2
====================


This model is a fine-tuned version of resnet18 on the cats\_vs\_dogs dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2510
* Acc1: 95.2150
* Acc5: 100.0


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: 256
* eval\_batch\_size: 256
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.12.3
* Pytorch 1.10.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 256
* eval\_batch\_size: 256
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.12.3
* Pytorch 1.10.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
TAGS
#timm #pytorch #tensorboard #image-classification #generated_from_trainer #dataset-cats_vs_dogs #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 256
* eval\_batch\_size: 256
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.12.3
* Pytorch 1.10.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
passage: TAGS
#timm #pytorch #tensorboard #image-classification #generated_from_trainer #dataset-cats_vs_dogs #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 256
* eval\_batch\_size: 256
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.12.3
* Pytorch 1.10.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3