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--- |
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license: apache-2.0 |
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base_model: microsoft/resnet-18 |
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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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metrics: |
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- accuracy |
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model-index: |
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- name: resnet-18 |
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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: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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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: 1.0 |
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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 |
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This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8444 |
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- Accuracy: 1.0 |
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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: 10 |
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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 | 0.9091 | 5 | 1.0318 | 0.4156 | |
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| 1.0893 | 2.0 | 11 | 0.9520 | 0.6364 | |
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| 1.0893 | 2.9091 | 16 | 0.9017 | 0.8442 | |
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| 0.9912 | 4.0 | 22 | 0.8444 | 1.0 | |
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| 0.9912 | 4.9091 | 27 | 0.8027 | 1.0 | |
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| 0.9248 | 6.0 | 33 | 0.7631 | 1.0 | |
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| 0.9248 | 6.9091 | 38 | 0.7369 | 1.0 | |
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| 0.8716 | 8.0 | 44 | 0.7156 | 1.0 | |
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| 0.8716 | 8.9091 | 49 | 0.7137 | 1.0 | |
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| 0.8517 | 9.0909 | 50 | 0.7117 | 1.0 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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