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---
license: other
base_model: google/mobilenet_v2_1.0_224
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: mobilenet-finetuned-food101
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: train[:5000]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.821
---
<!-- 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-finetuned-food101
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 food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5518
- Accuracy: 0.821
## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.9575 | 0.153 |
| 1.9536 | 2.0 | 12 | 1.8509 | 0.265 |
| 1.9536 | 3.0 | 18 | 1.7003 | 0.451 |
| 1.7915 | 4.0 | 24 | 1.5181 | 0.578 |
| 1.4994 | 5.0 | 30 | 1.3609 | 0.631 |
| 1.4994 | 6.0 | 36 | 1.2321 | 0.669 |
| 1.2203 | 7.0 | 42 | 1.0696 | 0.69 |
| 1.2203 | 8.0 | 48 | 0.9676 | 0.723 |
| 1.0215 | 9.0 | 54 | 0.8888 | 0.729 |
| 0.8462 | 10.0 | 60 | 0.8380 | 0.74 |
| 0.8462 | 11.0 | 66 | 0.7461 | 0.778 |
| 0.744 | 12.0 | 72 | 0.6724 | 0.792 |
| 0.744 | 13.0 | 78 | 0.7314 | 0.769 |
| 0.6496 | 14.0 | 84 | 0.6831 | 0.77 |
| 0.6143 | 15.0 | 90 | 0.5937 | 0.81 |
| 0.6143 | 16.0 | 96 | 0.6217 | 0.793 |
| 0.5468 | 17.0 | 102 | 0.5965 | 0.788 |
| 0.5468 | 18.0 | 108 | 0.5944 | 0.813 |
| 0.5428 | 19.0 | 114 | 0.5869 | 0.812 |
| 0.5193 | 20.0 | 120 | 0.5565 | 0.82 |
| 0.5193 | 21.0 | 126 | 0.6155 | 0.803 |
| 0.4902 | 22.0 | 132 | 0.5685 | 0.817 |
| 0.4902 | 23.0 | 138 | 0.6097 | 0.789 |
| 0.4869 | 24.0 | 144 | 0.6002 | 0.8 |
| 0.4745 | 25.0 | 150 | 0.5569 | 0.814 |
| 0.4745 | 26.0 | 156 | 0.5414 | 0.821 |
| 0.4653 | 27.0 | 162 | 0.5806 | 0.807 |
| 0.4653 | 28.0 | 168 | 0.5663 | 0.807 |
| 0.4543 | 29.0 | 174 | 0.5412 | 0.825 |
| 0.4575 | 30.0 | 180 | 0.5518 | 0.821 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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