---
license: apache-2.0
base_model: microsoft/swinv2-base-patch4-window8-256
tags:
- pytoroch
- Swinv2ForImageClassification
- food-classification
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
- f1
model-index:
- name: Swin-V2-base-Food
  results: []
---

<!-- 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. -->

# Swin-V2-base-Food

This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window8-256](https://huggingface.co/microsoft/swinv2-base-patch4-window8-256) on the ItsNotRohit/Food121-224 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7099
- Accuracy: 0.8160
- Recall: 0.8160
- Precision: 0.8168
- F1: 0.8159

## 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: 17769929
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 20000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Recall | Precision | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.5169        | 0.33  | 2000  | 1.2680          | 0.6746   | 0.6746 | 0.7019    | 0.6737 |
| 1.2362        | 0.66  | 4000  | 1.0759          | 0.7169   | 0.7169 | 0.7411    | 0.7178 |
| 1.1076        | 0.99  | 6000  | 0.9757          | 0.7437   | 0.7437 | 0.7593    | 0.7430 |
| 0.9163        | 1.32  | 8000  | 0.9123          | 0.7623   | 0.7623 | 0.7737    | 0.7628 |
| 0.8291        | 1.65  | 10000 | 0.8397          | 0.7807   | 0.7807 | 0.7874    | 0.7796 |
| 0.7949        | 1.98  | 12000 | 0.7724          | 0.7965   | 0.7965 | 0.8014    | 0.7965 |
| 0.6455        | 2.31  | 14000 | 0.7458          | 0.8030   | 0.8030 | 0.8069    | 0.8031 |
| 0.6332        | 2.64  | 16000 | 0.7222          | 0.8110   | 0.8110 | 0.8122    | 0.8106 |
| 0.6132        | 2.98  | 18000 | 0.7021          | 0.8154   | 0.8154 | 0.8170    | 0.8155 |
| 0.57          | 3.31  | 20000 | 0.7099          | 0.8160   | 0.8160 | 0.8168    | 0.8159 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0