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# Train

This model is a fine-tuned version of [microsoft/resnet-101](https://huggingface.co/microsoft/resnet-101) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8647
- Accuracy: 0.3282

## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.8186        | 0.13  | 100  | 2.8698          | 0.3205   |
| 3.3401        | 0.26  | 200  | 2.8968          | 0.3026   |
| 2.8737        | 0.39  | 300  | 2.8947          | 0.3128   |
| 3.1365        | 0.51  | 400  | 2.8635          | 0.3256   |
| 2.9823        | 0.64  | 500  | 2.8724          | 0.3128   |
| 2.7439        | 0.77  | 600  | 2.8736          | 0.3333   |
| 2.7354        | 0.9   | 700  | 2.8708          | 0.3436   |
| 2.688         | 1.03  | 800  | 2.8709          | 0.3231   |
| 3.172         | 1.16  | 900  | 2.9082          | 0.2692   |
| 2.7289        | 1.29  | 1000 | 2.8873          | 0.3564   |
| 2.7369        | 1.41  | 1100 | 2.9032          | 0.3      |
| 2.879         | 1.54  | 1200 | 2.8807          | 0.3308   |
| 2.9532        | 1.67  | 1300 | 2.8706          | 0.2923   |
| 3.2004        | 1.8   | 1400 | 2.8598          | 0.2872   |
| 2.9607        | 1.93  | 1500 | 2.8647          | 0.3282   |


### 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": "Train", "results": []}]}
transformers pytorch resnet image-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #resnet #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

Train
=====


This model is a fine-tuned version of microsoft/resnet-101 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 2.8647
* Accuracy: 0.3282


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: 0.0002
* train\_batch\_size: 2
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2


### 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: 0.0002
* train\_batch\_size: 2
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2 ### Training results ### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #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: 0.0002
* train\_batch\_size: 2
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2 ### Training results ### Framework versions


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