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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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- lewtun/dog_food |
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metrics: |
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- accuracy |
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model-index: |
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- name: resnet-18-finetuned-dogfood |
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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: lewtun/dog_food |
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type: lewtun/dog_food |
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args: lewtun--dog_food |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.896 |
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- task: |
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type: image-classification |
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name: Image Classification |
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dataset: |
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name: lewtun/dog_food |
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type: lewtun/dog_food |
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config: lewtun--dog_food |
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split: test |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8466666666666667 |
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verified: true |
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- name: Precision Macro |
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type: precision |
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value: 0.8850127293141284 |
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verified: true |
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- name: Precision Micro |
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type: precision |
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value: 0.8466666666666667 |
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verified: true |
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- name: Precision Weighted |
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type: precision |
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value: 0.8939157698241645 |
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verified: true |
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- name: Recall Macro |
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type: recall |
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value: 0.8555113273379528 |
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verified: true |
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- name: Recall Micro |
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type: recall |
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value: 0.8466666666666667 |
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verified: true |
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- name: Recall Weighted |
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type: recall |
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value: 0.8466666666666667 |
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verified: true |
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- name: F1 Macro |
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type: f1 |
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value: 0.8431399312051647 |
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verified: true |
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- name: F1 Micro |
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type: f1 |
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value: 0.8466666666666667 |
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verified: true |
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- name: F1 Weighted |
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type: f1 |
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value: 0.8430272582865614 |
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verified: true |
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- name: loss |
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type: loss |
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value: 0.3633290231227875 |
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verified: true |
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- name: matthews_correlation |
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type: matthews_correlation |
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value: 0.7973101366252381 |
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verified: true |
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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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# resnet-18-finetuned-dogfood |
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This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the lewtun/dog_food dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2991 |
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- Accuracy: 0.896 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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: 1 |
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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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| 0.846 | 1.0 | 16 | 0.2662 | 0.9156 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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