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# my_awesome_pokemon_model_resnet18

This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the pokemon-classification dataset.
It achieves the following results on the evaluation set:
- Loss: 6.8019
- Accuracy: 0.0108

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.275         | 1.0   | 76   | 6.1680          | 0.0014   |
| 3.3896        | 1.99  | 152  | 6.6421          | 0.0115   |
| 3.0563        | 2.99  | 228  | 6.8019          | 0.0108   |


### Framework versions

- Transformers 4.29.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["pokemon-classification"], "metrics": ["accuracy"], "model-index": [{"name": "my_awesome_pokemon_model_resnet18", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "pokemon-classification", "type": "pokemon-classification", "config": "full", "split": "validation", "args": "full"}, "metrics": [{"type": "accuracy", "value": 0.01079136690647482, "name": "Accuracy"}]}]}]}
transformers pytorch safetensors resnet image-classification generated_from_trainer dataset:pokemon-classification license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #resnet #image-classification #generated_from_trainer #dataset-pokemon-classification #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

my\_awesome\_pokemon\_model\_resnet18
=====================================


This model is a fine-tuned version of microsoft/resnet-18 on the pokemon-classification dataset.
It achieves the following results on the evaluation set:


* Loss: 6.8019
* Accuracy: 0.0108


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: 3


### Training results



### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.3



### 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: 3 ### Training results ### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #safetensors #resnet #image-classification #generated_from_trainer #dataset-pokemon-classification #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: 3 ### Training results ### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.3