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