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# resnet-152-finetuned_resnet152-adam-optimizer5e-4-autotags

This model is a fine-tuned version of [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2399
- Accuracy: 0.9305

## 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: 0.0005
- 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.4009        | 0.99  | 65   | 2.1414          | 0.3971   |
| 0.9201        | 1.99  | 130  | 0.8123          | 0.7210   |
| 0.7575        | 2.99  | 195  | 0.5730          | 0.8124   |
| 0.4792        | 3.99  | 260  | 0.4166          | 0.8648   |
| 0.4253        | 4.99  | 325  | 0.3811          | 0.8810   |
| 0.3331        | 5.99  | 390  | 0.4290          | 0.8705   |
| 0.2347        | 6.99  | 455  | 0.4600          | 0.8952   |
| 0.1732        | 7.99  | 520  | 0.3018          | 0.8924   |
| 0.1777        | 8.99  | 585  | 0.4851          | 0.8914   |
| 0.1298        | 9.99  | 650  | 0.2941          | 0.92     |
| 0.1164        | 10.99 | 715  | 0.3915          | 0.9095   |
| 0.1284        | 11.99 | 780  | 0.3701          | 0.9152   |
| 0.0986        | 12.99 | 845  | 0.3416          | 0.9171   |
| 0.0944        | 13.99 | 910  | 0.3145          | 0.9210   |
| 0.0929        | 14.99 | 975  | 0.2677          | 0.9229   |
| 0.1014        | 15.99 | 1040 | 0.2745          | 0.9295   |
| 0.0971        | 16.99 | 1105 | 0.2932          | 0.9267   |
| 0.0691        | 17.99 | 1170 | 0.2174          | 0.9333   |
| 0.0557        | 18.99 | 1235 | 0.2233          | 0.9324   |
| 0.06          | 19.99 | 1300 | 0.2399          | 0.9305   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "resnet-152-finetuned_resnet152-adam-optimizer5e-4-autotags", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9304761904761905, "name": "Accuracy"}]}]}]}
transformers pytorch resnet image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #resnet #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

resnet-152-finetuned\_resnet152-adam-optimizer5e-4-autotags
===========================================================


This model is a fine-tuned version of microsoft/resnet-152 on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2399
* Accuracy: 0.9305


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: 0.0005
* 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: 20


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.11.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* 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: 20 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #resnet #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* 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: 20 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #resnet #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* 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: 20### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.11.0
* Tokenizers 0.13.2