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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: Action_agent_small_34_class |
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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: 0.09523809523809523 |
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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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# Action_agent_small_34_class |
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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: nan |
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- Accuracy: 0.0952 |
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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: 1e-05 |
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- train_batch_size: 32 |
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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: 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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| 0.0 | 0.32 | 100 | nan | 0.0952 | |
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| 0.0 | 0.64 | 200 | nan | 0.0952 | |
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| 0.0 | 0.96 | 300 | nan | 0.0952 | |
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| 0.0 | 1.27 | 400 | nan | 0.0952 | |
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| 0.0 | 1.59 | 500 | nan | 0.0952 | |
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| 0.0 | 1.91 | 600 | nan | 0.0952 | |
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| 0.0 | 2.23 | 700 | nan | 0.0952 | |
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| 0.0 | 2.55 | 800 | nan | 0.0952 | |
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| 0.0 | 2.87 | 900 | nan | 0.0952 | |
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| 0.0 | 3.18 | 1000 | nan | 0.0952 | |
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| 0.0 | 3.5 | 1100 | nan | 0.0952 | |
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| 0.0 | 3.82 | 1200 | nan | 0.0952 | |
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| 0.0 | 4.14 | 1300 | nan | 0.0952 | |
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| 0.0 | 4.46 | 1400 | nan | 0.0952 | |
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| 0.0 | 4.78 | 1500 | nan | 0.0952 | |
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| 0.0 | 5.1 | 1600 | nan | 0.0952 | |
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| 0.0 | 5.41 | 1700 | nan | 0.0952 | |
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| 0.0 | 5.73 | 1800 | nan | 0.0952 | |
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| 0.0 | 6.05 | 1900 | nan | 0.0952 | |
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| 0.0 | 6.37 | 2000 | nan | 0.0952 | |
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| 0.0 | 6.69 | 2100 | nan | 0.0952 | |
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| 0.0 | 7.01 | 2200 | nan | 0.0952 | |
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| 0.0 | 7.32 | 2300 | nan | 0.0952 | |
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| 0.0 | 7.64 | 2400 | nan | 0.0952 | |
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| 0.0 | 7.96 | 2500 | nan | 0.0952 | |
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| 0.0 | 8.28 | 2600 | nan | 0.0952 | |
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| 0.0 | 8.6 | 2700 | nan | 0.0952 | |
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| 0.0 | 8.92 | 2800 | nan | 0.0952 | |
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| 0.0 | 9.24 | 2900 | nan | 0.0952 | |
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| 0.0 | 9.55 | 3000 | nan | 0.0952 | |
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| 0.0 | 9.87 | 3100 | nan | 0.0952 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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