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# swin-food101-jpqd-1to2r1.5-epo7-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: 0.2658
- Accuracy: 0.9124

## 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: 7.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2977        | 0.42  | 500  | 0.1949          | 0.9112   |
| 0.3183        | 0.84  | 1000 | 0.1867          | 0.9144   |
| 99.9552       | 1.27  | 1500 | 99.4882         | 0.7577   |
| 162.4195      | 1.69  | 2000 | 162.7763        | 0.3373   |
| 1.2272        | 2.11  | 2500 | 0.7333          | 0.8564   |
| 1.0236        | 2.54  | 3000 | 0.5016          | 0.8823   |
| 0.6472        | 2.96  | 3500 | 0.4337          | 0.8908   |
| 0.52          | 3.38  | 4000 | 0.3927          | 0.8974   |
| 0.6075        | 3.8   | 4500 | 0.3506          | 0.9011   |
| 0.5348        | 4.23  | 5000 | 0.3425          | 0.9006   |
| 0.444         | 4.65  | 5500 | 0.3268          | 0.9044   |
| 0.5787        | 5.07  | 6000 | 0.3020          | 0.9078   |
| 0.3995        | 5.49  | 6500 | 0.2932          | 0.9095   |
| 0.414         | 5.92  | 7000 | 0.2806          | 0.9104   |
| 0.4386        | 6.34  | 7500 | 0.2738          | 0.9112   |
| 0.452         | 6.76  | 8000 | 0.2673          | 0.9127   |


### 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-epo7-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": 0.9123960396039604, "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-epo7-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: 0.2658
* Accuracy: 0.9124


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: 7.0


### 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: 7.0 ### 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: 7.0 ### 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: 7.0### Training results### Framework versions


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