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--- |
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license: apache-2.0 |
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base_model: microsoft/resnet-50 |
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tags: |
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- image-classification |
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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: camera-type |
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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: validation |
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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: 0.9382716049382716 |
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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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# camera-type |
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This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1654 |
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- Accuracy: 0.9383 |
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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: 0.0001 |
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- train_batch_size: 10 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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- num_epochs: 5 |
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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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| 0.4597 | 0.5 | 200 | 0.2801 | 0.9242 | |
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| 0.1375 | 0.99 | 400 | 0.1654 | 0.9383 | |
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| 0.0795 | 1.49 | 600 | 0.1904 | 0.9383 | |
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| 0.0686 | 1.98 | 800 | 0.1810 | 0.9453 | |
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| 0.026 | 2.48 | 1000 | 0.2216 | 0.9400 | |
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| 0.0495 | 2.97 | 1200 | 0.2096 | 0.9453 | |
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| 0.0487 | 3.47 | 1400 | 0.2174 | 0.9436 | |
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| 0.0268 | 3.96 | 1600 | 0.2304 | 0.9453 | |
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| 0.0254 | 4.46 | 1800 | 0.2574 | 0.9400 | |
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| 0.0186 | 4.95 | 2000 | 0.3212 | 0.9383 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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