MobileNet-V2-food / README.md
arnabdhar's picture
End of training
95ba34b
metadata
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: []

MobileNet-V2-food

This model is a fine-tuned version of 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