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--- |
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library_name: transformers |
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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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- 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: cat_dog_classifier_with_small_datasest |
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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: 0.95 |
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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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# cat_dog_classifier_with_small_datasest |
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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: 0.1369 |
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- Accuracy: 0.95 |
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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: 4e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 20 |
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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 | 70 | 0.5422 | 0.8571 | |
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| No log | 2.0 | 140 | 0.5221 | 0.8786 | |
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| No log | 3.0 | 210 | 0.4977 | 0.8571 | |
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| No log | 4.0 | 280 | 0.4617 | 0.8786 | |
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| No log | 5.0 | 350 | 0.3932 | 0.9143 | |
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| No log | 6.0 | 420 | 0.3411 | 0.9143 | |
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| No log | 7.0 | 490 | 0.2884 | 0.9143 | |
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| 0.4971 | 8.0 | 560 | 0.2429 | 0.9286 | |
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| 0.4971 | 9.0 | 630 | 0.2151 | 0.9429 | |
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| 0.4971 | 10.0 | 700 | 0.1962 | 0.9286 | |
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| 0.4971 | 11.0 | 770 | 0.1727 | 0.9357 | |
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| 0.4971 | 12.0 | 840 | 0.1676 | 0.95 | |
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| 0.4971 | 13.0 | 910 | 0.1764 | 0.9286 | |
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| 0.4971 | 14.0 | 980 | 0.1565 | 0.9429 | |
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| 0.2878 | 15.0 | 1050 | 0.1578 | 0.9429 | |
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| 0.2878 | 16.0 | 1120 | 0.1577 | 0.9429 | |
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| 0.2878 | 17.0 | 1190 | 0.1393 | 0.9429 | |
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| 0.2878 | 18.0 | 1260 | 0.1472 | 0.9429 | |
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| 0.2878 | 19.0 | 1330 | 0.1315 | 0.95 | |
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| 0.2878 | 20.0 | 1400 | 0.1369 | 0.95 | |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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