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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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metrics: |
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- accuracy |
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model-index: |
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- name: resnet-101-finetuned_resnet101-cnn-autotags |
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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: 0.9228571428571428 |
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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-101-finetuned_resnet101-cnn-autotags |
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This model is a fine-tuned version of [microsoft/resnet-101](https://huggingface.co/microsoft/resnet-101) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2099 |
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- Accuracy: 0.9229 |
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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: 0.001 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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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| 2.0434 | 0.99 | 65 | 1.6045 | 0.4829 | |
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| 0.9013 | 1.99 | 130 | 0.6946 | 0.7648 | |
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| 0.7097 | 2.99 | 195 | 0.4928 | 0.8295 | |
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| 0.4386 | 3.99 | 260 | 0.3632 | 0.8610 | |
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| 0.4261 | 4.99 | 325 | 0.3269 | 0.8838 | |
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| 0.3181 | 5.99 | 390 | 0.2790 | 0.9010 | |
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| 0.2349 | 6.99 | 455 | 0.2377 | 0.9190 | |
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| 0.1615 | 7.99 | 520 | 0.2416 | 0.9114 | |
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| 0.1146 | 8.99 | 585 | 0.2162 | 0.9219 | |
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| 0.1254 | 9.99 | 650 | 0.2099 | 0.9229 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.2 |
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