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---
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
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