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# resnet-50-bottomCleanedData

This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0822
- Accuracy: 0.9762

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 7
- total_train_batch_size: 56
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3323        | 1.0   | 141  | 1.3319          | 0.5187   |
| 1.1302        | 2.0   | 283  | 1.1059          | 0.5335   |
| 0.8793        | 2.99  | 424  | 0.7848          | 0.7094   |
| 0.7652        | 4.0   | 566  | 0.7255          | 0.7219   |
| 0.7708        | 4.99  | 707  | 0.5280          | 0.8173   |
| 0.6153        | 6.0   | 849  | 0.4221          | 0.8490   |
| 0.5895        | 7.0   | 991  | 0.4015          | 0.8570   |
| 0.5617        | 8.0   | 1132 | 0.2998          | 0.9001   |
| 0.517         | 9.0   | 1274 | 0.2737          | 0.9160   |
| 0.5366        | 9.99  | 1415 | 0.2229          | 0.9240   |
| 0.4645        | 11.0  | 1557 | 0.2038          | 0.9330   |
| 0.4114        | 11.99 | 1698 | 0.1851          | 0.9376   |
| 0.4528        | 13.0  | 1840 | 0.1796          | 0.9432   |
| 0.4182        | 14.0  | 1982 | 0.1578          | 0.9523   |
| 0.432         | 15.0  | 2123 | 0.1660          | 0.9421   |
| 0.4442        | 16.0  | 2265 | 0.1401          | 0.9557   |
| 0.4059        | 16.99 | 2406 | 0.1332          | 0.9591   |
| 0.3498        | 18.0  | 2548 | 0.1431          | 0.9535   |
| 0.3869        | 18.99 | 2689 | 0.1237          | 0.9512   |
| 0.3639        | 20.0  | 2831 | 0.1193          | 0.9603   |
| 0.3819        | 21.0  | 2973 | 0.1234          | 0.9557   |
| 0.3491        | 22.0  | 3114 | 0.1207          | 0.9569   |
| 0.3259        | 23.0  | 3256 | 0.1234          | 0.9591   |
| 0.3199        | 23.99 | 3397 | 0.1028          | 0.9659   |
| 0.3398        | 25.0  | 3539 | 0.1010          | 0.9603   |
| 0.3108        | 25.99 | 3680 | 0.1015          | 0.9671   |
| 0.3417        | 27.0  | 3822 | 0.1080          | 0.9614   |
| 0.3835        | 28.0  | 3964 | 0.1056          | 0.9591   |
| 0.3336        | 29.0  | 4105 | 0.1011          | 0.9637   |
| 0.3035        | 30.0  | 4247 | 0.0972          | 0.9614   |
| 0.2559        | 30.99 | 4388 | 0.0941          | 0.9659   |
| 0.378         | 32.0  | 4530 | 0.0963          | 0.9603   |
| 0.2932        | 32.99 | 4671 | 0.0916          | 0.9716   |
| 0.3072        | 34.0  | 4813 | 0.0917          | 0.9671   |
| 0.3081        | 35.0  | 4955 | 0.1025          | 0.9625   |
| 0.2724        | 36.0  | 5096 | 0.0874          | 0.9671   |
| 0.2621        | 37.0  | 5238 | 0.0847          | 0.9705   |
| 0.3521        | 37.99 | 5379 | 0.0829          | 0.9728   |
| 0.2883        | 39.0  | 5521 | 0.0860          | 0.9728   |
| 0.2617        | 39.99 | 5662 | 0.0898          | 0.9682   |
| 0.2893        | 41.0  | 5804 | 0.0877          | 0.9671   |
| 0.2994        | 42.0  | 5946 | 0.0822          | 0.9762   |
| 0.2483        | 43.0  | 6087 | 0.0834          | 0.9705   |
| 0.301         | 44.0  | 6229 | 0.0883          | 0.9694   |
| 0.2648        | 44.99 | 6370 | 0.0834          | 0.9705   |
| 0.2902        | 46.0  | 6512 | 0.0879          | 0.9648   |
| 0.299         | 46.99 | 6653 | 0.0843          | 0.9694   |
| 0.2726        | 48.0  | 6795 | 0.0920          | 0.9659   |
| 0.3252        | 49.0  | 6937 | 0.0857          | 0.9716   |
| 0.274         | 49.8  | 7050 | 0.0813          | 0.9762   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "resnet-50-bottomCleanedData", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9761634506242906, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard resnet image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

resnet-50-bottomCleanedData
===========================


This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0822
* Accuracy: 0.9762


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: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 7
* total\_train\_batch\_size: 56
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.01
* num\_epochs: 50


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+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: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 7
* total\_train\_batch\_size: 56
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.01
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #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: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 7
* total\_train\_batch\_size: 56
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.01
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3