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# swin-food101-jpqd-1to2r1.5-epo10-finetuned-student

This model is a fine-tuned version of [skylord/swin-finetuned-food101](https://huggingface.co/skylord/swin-finetuned-food101) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2391
- Accuracy: 0.9184

## 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
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3011        | 0.42  | 500   | 0.1951          | 0.9124   |
| 0.2613        | 0.84  | 1000  | 0.1897          | 0.9139   |
| 100.1552      | 1.27  | 1500  | 99.5975         | 0.7445   |
| 162.0751      | 1.69  | 2000  | 162.5020        | 0.3512   |
| 1.061         | 2.11  | 2500  | 0.7523          | 0.8550   |
| 0.9728        | 2.54  | 3000  | 0.5263          | 0.8767   |
| 0.5851        | 2.96  | 3500  | 0.4599          | 0.8892   |
| 0.4668        | 3.38  | 4000  | 0.4064          | 0.8938   |
| 0.6967        | 3.8   | 4500  | 0.3814          | 0.8986   |
| 0.4928        | 4.23  | 5000  | 0.3522          | 0.9036   |
| 0.4893        | 4.65  | 5500  | 0.3562          | 0.9026   |
| 0.5421        | 5.07  | 6000  | 0.3182          | 0.9049   |
| 0.4405        | 5.49  | 6500  | 0.3112          | 0.9071   |
| 0.4423        | 5.92  | 7000  | 0.3012          | 0.9092   |
| 0.4143        | 6.34  | 7500  | 0.2958          | 0.9095   |
| 0.4997        | 6.76  | 8000  | 0.2796          | 0.9126   |
| 0.2448        | 7.19  | 8500  | 0.2747          | 0.9124   |
| 0.4468        | 7.61  | 9000  | 0.2699          | 0.9144   |
| 0.4163        | 8.03  | 9500  | 0.2583          | 0.9166   |
| 0.3651        | 8.45  | 10000 | 0.2567          | 0.9165   |
| 0.3946        | 8.88  | 10500 | 0.2489          | 0.9176   |
| 0.3196        | 9.3   | 11000 | 0.2444          | 0.9180   |
| 0.312         | 9.72  | 11500 | 0.2402          | 0.9172   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["image-classification", "vision", "generated_from_trainer"], "datasets": ["food101"], "metrics": ["accuracy"], "model-index": [{"name": "swin-food101-jpqd-1to2r1.5-epo10-finetuned-student", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "food101", "type": "food101", "config": "default", "split": "validation", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9183762376237624, "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 

swin-food101-jpqd-1to2r1.5-epo10-finetuned-student
==================================================


This model is a fine-tuned version of skylord/swin-finetuned-food101 on the food101 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2391
* Accuracy: 0.9184


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
* num\_epochs: 10.0


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* 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
* num\_epochs: 10.0 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* 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
* num\_epochs: 10.0 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* 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
* num\_epochs: 10.0### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2