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--- |
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license: other |
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base_model: google/mobilenet_v2_1.0_224 |
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tags: |
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- pytoroch |
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- MobileNetV2ForImageClassification |
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- food-classification |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- recall |
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- precision |
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- f1 |
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model-index: |
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- name: MobileNet-V2-food |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# MobileNet-V2-food |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6890 |
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- Accuracy: 0.5793 |
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- Recall: 0.5793 |
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- Precision: 0.6006 |
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- F1: 0.5769 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 128 |
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- seed: 20329 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- training_steps: 20000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 2.9653 | 0.33 | 2000 | 2.7802 | 0.3438 | 0.3438 | 0.3932 | 0.3105 | |
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| 2.3854 | 0.66 | 4000 | 2.3105 | 0.4440 | 0.4440 | 0.4979 | 0.4336 | |
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| 2.1576 | 0.99 | 6000 | 2.0508 | 0.4958 | 0.4958 | 0.5263 | 0.4837 | |
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| 1.9767 | 1.32 | 8000 | 1.9860 | 0.5086 | 0.5086 | 0.5504 | 0.4956 | |
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| 1.9215 | 1.65 | 10000 | 1.8312 | 0.5462 | 0.5462 | 0.5815 | 0.5390 | |
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| 1.782 | 1.98 | 12000 | 1.8554 | 0.5441 | 0.5441 | 0.5864 | 0.5431 | |
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| 1.7755 | 2.31 | 14000 | 1.9241 | 0.5308 | 0.5308 | 0.5841 | 0.5272 | |
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| 1.7006 | 2.64 | 16000 | 1.8625 | 0.5451 | 0.5451 | 0.6004 | 0.5466 | |
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| 1.7289 | 2.98 | 18000 | 1.8560 | 0.5432 | 0.5432 | 0.5940 | 0.5395 | |
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| 1.7296 | 3.31 | 20000 | 1.6890 | 0.5793 | 0.5793 | 0.6006 | 0.5769 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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