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# swin-base-food101-jpqd-ov

It was compressed using [NNCF](https://github.com/openvinotoolkit/nncf) with [Optimum Intel](https://github.com/huggingface/optimum-intel#openvino) following the 
JPQD image classification example.


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: 0.3396
- Accuracy: 0.9061

### 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: 10.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.2162        | 0.42  | 500   | 2.1111          | 0.7967   |
| 0.729         | 0.84  | 1000  | 0.5474          | 0.8773   |
| 0.7536        | 1.27  | 1500  | 0.3844          | 0.8984   |
| 0.4822        | 1.69  | 2000  | 0.3340          | 0.9043   |
| 12.2559       | 2.11  | 2500  | 12.0128         | 0.9033   |
| 48.7302       | 2.54  | 3000  | 48.3874         | 0.8681   |
| 75.1831       | 2.96  | 3500  | 75.3200         | 0.7183   |
| 93.5572       | 3.38  | 4000  | 93.4142         | 0.5939   |
| 103.798       | 3.8   | 4500  | 103.4427        | 0.5634   |
| 108.0993      | 4.23  | 5000  | 108.6461        | 0.5490   |
| 110.1265      | 4.65  | 5500  | 109.3663        | 0.5636   |
| 1.5584        | 5.07  | 6000  | 0.9255          | 0.8374   |
| 1.0883        | 5.49  | 6500  | 0.5841          | 0.8758   |
| 0.7024        | 5.92  | 7000  | 0.5055          | 0.8854   |
| 0.9033        | 6.34  | 7500  | 0.4639          | 0.8901   |
| 0.6901        | 6.76  | 8000  | 0.4360          | 0.8947   |
| 0.6114        | 7.19  | 8500  | 0.4080          | 0.8978   |
| 0.5102        | 7.61  | 9000  | 0.3911          | 0.9009   |
| 0.7154        | 8.03  | 9500  | 0.3747          | 0.9027   |
| 0.5621        | 8.45  | 10000 | 0.3622          | 0.9021   |
| 0.5262        | 8.88  | 10500 | 0.3554          | 0.9041   |
| 0.5442        | 9.3   | 11000 | 0.3462          | 0.9053   |
| 0.5615        | 9.72  | 11500 | 0.3416          | 0.9061   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- 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-base-food101-jpqd-ov", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "food101", "type": "food101", "config": "default", "split": "validation", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9060990099009901, "name": "Accuracy"}]}]}]}
transformers openvino swin image-classification vision generated_from_trainer dataset:food101 license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #openvino #swin #image-classification #vision #generated_from_trainer #dataset-food101 #license-apache-2.0 #model-index #endpoints_compatible #region-us 

swin-base-food101-jpqd-ov
=========================


It was compressed using NNCF with Optimum Intel following the
JPQD image classification example.


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: 0.3396
* Accuracy: 0.9061


### 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: 10.0


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* 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
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10.0 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #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: 10.0 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #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: 10.0### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.13.2