
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# jpqd-swin-b-20eph-r1.00-s2e5-mock-main-merge-pr2

This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2715
- Accuracy: 0.9179

## 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: 128
- 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: 20.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 5.1452        | 0.42  | 500   | 5.4928          | 0.6440   |
| 0.9839        | 0.84  | 1000  | 0.7956          | 0.8580   |
| 0.8533        | 1.27  | 1500  | 0.4392          | 0.8911   |
| 0.6123        | 1.69  | 2000  | 0.3768          | 0.8983   |
| 12.3076       | 2.11  | 2500  | 12.0798         | 0.8953   |
| 49.301        | 2.54  | 3000  | 48.6292         | 0.8343   |
| 75.6345       | 2.96  | 3500  | 75.7027         | 0.6777   |
| 94.2556       | 3.38  | 4000  | 93.5852         | 0.5604   |
| 103.3226      | 3.8   | 4500  | 103.1255        | 0.5702   |
| 107.3423      | 4.23  | 5000  | 107.9250        | 0.5359   |
| 108.9013      | 4.65  | 5500  | 108.5225        | 0.5882   |
| 2.045         | 5.07  | 6000  | 1.1149          | 0.8154   |
| 1.3377        | 5.49  | 6500  | 0.6747          | 0.8665   |
| 0.7565        | 5.92  | 7000  | 0.5814          | 0.8765   |
| 0.7493        | 6.34  | 7500  | 0.5460          | 0.8840   |
| 0.7693        | 6.76  | 8000  | 0.5109          | 0.8851   |
| 0.6082        | 7.19  | 8500  | 0.4893          | 0.8895   |
| 0.7575        | 7.61  | 9000  | 0.4521          | 0.8943   |
| 0.7943        | 8.03  | 9500  | 0.4465          | 0.8941   |
| 0.5521        | 8.45  | 10000 | 0.4119          | 0.8967   |
| 0.6536        | 8.88  | 10500 | 0.4071          | 0.9010   |
| 0.5164        | 9.3   | 11000 | 0.3945          | 0.9010   |
| 0.6687        | 9.72  | 11500 | 0.3884          | 0.9030   |
| 0.4374        | 10.14 | 12000 | 0.3764          | 0.9040   |
| 0.7326        | 10.57 | 12500 | 0.3678          | 0.9060   |
| 0.6148        | 10.99 | 13000 | 0.3602          | 0.9057   |
| 0.6068        | 11.41 | 13500 | 0.3566          | 0.9075   |
| 0.6105        | 11.83 | 14000 | 0.3456          | 0.9074   |
| 0.5277        | 12.26 | 14500 | 0.3383          | 0.9107   |
| 0.5255        | 12.68 | 15000 | 0.3328          | 0.9097   |
| 0.4536        | 13.1  | 15500 | 0.3268          | 0.9108   |
| 0.5337        | 13.52 | 16000 | 0.3256          | 0.9107   |
| 0.5299        | 13.95 | 16500 | 0.3161          | 0.9124   |
| 0.3037        | 14.37 | 17000 | 0.3162          | 0.9123   |
| 0.4171        | 14.79 | 17500 | 0.3078          | 0.9124   |
| 0.5375        | 15.22 | 18000 | 0.3002          | 0.9116   |
| 0.2722        | 15.64 | 18500 | 0.2953          | 0.9134   |
| 0.3684        | 16.06 | 19000 | 0.2960          | 0.9137   |
| 0.4369        | 16.48 | 19500 | 0.2918          | 0.9150   |
| 0.3346        | 16.91 | 20000 | 0.2856          | 0.9171   |
| 0.3645        | 17.33 | 20500 | 0.2856          | 0.9162   |
| 0.4475        | 17.75 | 21000 | 0.2833          | 0.9157   |
| 0.2553        | 18.17 | 21500 | 0.2788          | 0.9167   |
| 0.5098        | 18.6  | 22000 | 0.2766          | 0.9164   |
| 0.4149        | 19.02 | 22500 | 0.2732          | 0.9177   |
| 0.3737        | 19.44 | 23000 | 0.2734          | 0.9181   |
| 0.325         | 19.86 | 23500 | 0.2715          | 0.9176   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["image-classification", "vision", "generated_from_trainer"], "datasets": ["food101"], "metrics": ["accuracy"], "model-index": [{"name": "jpqd-swin-b-20eph-r1.00-s2e5-mock-main-merge-pr2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "food101", "type": "food101", "config": "default", "split": "validation", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9179009900990099, "name": "Accuracy"}]}]}]}
transformers pytorch openvino swin image-classification vision generated_from_trainer dataset:food101 license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #openvino #swin #image-classification #vision #generated_from_trainer #dataset-food101 #license-apache-2.0 #model-index #endpoints_compatible #region-us 

jpqd-swin-b-20eph-r1.00-s2e5-mock-main-merge-pr2
================================================


This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224 on the food101 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2715
* Accuracy: 0.9179


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: 128
* 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: 20.0


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 128
* 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: 20.0 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #openvino #swin #image-classification #vision #generated_from_trainer #dataset-food101 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 128
* 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: 20.0 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #openvino #swin #image-classification #vision #generated_from_trainer #dataset-food101 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 128
* 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: 20.0### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2