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--- |
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license: apache-2.0 |
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base_model: facebook/convnextv2-tiny-22k-384 |
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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: convnext-tiny-1e-4 |
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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: 0.9341269841269841 |
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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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# convnext-tiny-1e-4 |
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This model is a fine-tuned version of [facebook/convnextv2-tiny-22k-384](https://huggingface.co/facebook/convnextv2-tiny-22k-384) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2564 |
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- Accuracy: 0.9341 |
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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: 32 |
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- eval_batch_size: 32 |
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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: cosine |
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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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| 0.5828 | 1.0 | 550 | 0.4074 | 0.8875 | |
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| 0.4404 | 2.0 | 1100 | 0.4093 | 0.8811 | |
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| 0.3503 | 3.0 | 1650 | 0.3391 | 0.9018 | |
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| 0.2636 | 4.0 | 2200 | 0.3079 | 0.9161 | |
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| 0.2217 | 5.0 | 2750 | 0.3068 | 0.9169 | |
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| 0.2024 | 6.0 | 3300 | 0.2839 | 0.9284 | |
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| 0.1565 | 7.0 | 3850 | 0.2781 | 0.9324 | |
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| 0.1203 | 8.0 | 4400 | 0.2708 | 0.9388 | |
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| 0.1281 | 9.0 | 4950 | 0.2707 | 0.9364 | |
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| 0.1014 | 10.0 | 5500 | 0.2693 | 0.9380 | |
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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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