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# resnet-18-finetuned-eurosat

This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5506
- Accuracy: 0.8364

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3846        | 0.99  | 20   | 0.6352          | 0.7576   |
| 0.5009        | 1.98  | 40   | 0.6439          | 0.7485   |
| 0.5625        | 2.96  | 60   | 0.5722          | 0.7909   |
| 0.4928        | 4.0   | 81   | 0.5514          | 0.7970   |
| 0.4621        | 4.99  | 101  | 0.6104          | 0.7697   |
| 0.4367        | 5.98  | 121  | 0.5734          | 0.7939   |
| 0.4238        | 6.96  | 141  | 0.5558          | 0.8      |
| 0.4011        | 8.0   | 162  | 0.5549          | 0.8030   |
| 0.4129        | 8.99  | 182  | 0.5554          | 0.8061   |
| 0.384         | 9.98  | 202  | 0.5551          | 0.8152   |
| 0.3839        | 10.96 | 222  | 0.5742          | 0.8091   |
| 0.3496        | 12.0  | 243  | 0.5518          | 0.8303   |
| 0.3482        | 12.99 | 263  | 0.5390          | 0.8303   |
| 0.357         | 13.98 | 283  | 0.5544          | 0.8182   |
| 0.3341        | 14.96 | 303  | 0.5506          | 0.8364   |
| 0.3605        | 16.0  | 324  | 0.5546          | 0.8212   |
| 0.3041        | 16.99 | 344  | 0.5597          | 0.8212   |
| 0.3364        | 17.98 | 364  | 0.5730          | 0.8091   |
| 0.2976        | 18.96 | 384  | 0.5742          | 0.8091   |
| 0.3229        | 19.75 | 400  | 0.5653          | 0.8121   |


### Framework versions

- Transformers 4.30.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "resnet-18-finetuned-eurosat", "results": []}]}
transformers pytorch tensorboard resnet image-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

resnet-18-finetuned-eurosat
===========================


This model is a fine-tuned version of microsoft/resnet-18 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5506
* Accuracy: 0.8364


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: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3