---
license: other
base_model: google/mobilenet_v2_1.0_224
tags:
- pytoroch
- MobileNetV2ForImageClassification
- food-classification
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
- f1
model-index:
- name: MobileNet-V2-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. -->

# MobileNet-V2-food

This model is a fine-tuned version of [google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobilenet_v2_1.0_224) on the ItsNotRohit/Food121-224 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6890
- Accuracy: 0.5793
- Recall: 0.5793
- Precision: 0.6006
- F1: 0.5769

## 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: 20329
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 20000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Recall | Precision | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 2.9653        | 0.33  | 2000  | 2.7802          | 0.3438   | 0.3438 | 0.3932    | 0.3105 |
| 2.3854        | 0.66  | 4000  | 2.3105          | 0.4440   | 0.4440 | 0.4979    | 0.4336 |
| 2.1576        | 0.99  | 6000  | 2.0508          | 0.4958   | 0.4958 | 0.5263    | 0.4837 |
| 1.9767        | 1.32  | 8000  | 1.9860          | 0.5086   | 0.5086 | 0.5504    | 0.4956 |
| 1.9215        | 1.65  | 10000 | 1.8312          | 0.5462   | 0.5462 | 0.5815    | 0.5390 |
| 1.782         | 1.98  | 12000 | 1.8554          | 0.5441   | 0.5441 | 0.5864    | 0.5431 |
| 1.7755        | 2.31  | 14000 | 1.9241          | 0.5308   | 0.5308 | 0.5841    | 0.5272 |
| 1.7006        | 2.64  | 16000 | 1.8625          | 0.5451   | 0.5451 | 0.6004    | 0.5466 |
| 1.7289        | 2.98  | 18000 | 1.8560          | 0.5432   | 0.5432 | 0.5940    | 0.5395 |
| 1.7296        | 3.31  | 20000 | 1.6890          | 0.5793   | 0.5793 | 0.6006    | 0.5769 |


### Framework versions

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