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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: plant-seedlings-model-ConvNet |
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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: 0.9522292993630573 |
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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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# plant-seedlings-model-ConvNet |
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This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2410 |
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- Accuracy: 0.9522 |
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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: 0.0002 |
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- train_batch_size: 16 |
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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: 20 |
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- mixed_precision_training: Native AMP |
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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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| 0.494 | 0.8 | 100 | 0.4274 | 0.8828 | |
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| 0.246 | 1.6 | 200 | 0.2878 | 0.8930 | |
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| 0.1042 | 2.4 | 300 | 0.2227 | 0.9172 | |
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| 0.0174 | 3.2 | 400 | 0.2208 | 0.9299 | |
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| 0.0088 | 4.0 | 500 | 0.3197 | 0.9185 | |
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| 0.0078 | 4.8 | 600 | 0.2555 | 0.9357 | |
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| 0.0013 | 5.6 | 700 | 0.2599 | 0.9427 | |
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| 0.0068 | 6.4 | 800 | 0.3072 | 0.9312 | |
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| 0.0007 | 7.2 | 900 | 0.2217 | 0.9484 | |
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| 0.0004 | 8.0 | 1000 | 0.2551 | 0.9401 | |
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| 0.0003 | 8.8 | 1100 | 0.2321 | 0.9478 | |
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| 0.0002 | 9.6 | 1200 | 0.2329 | 0.9484 | |
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| 0.0002 | 10.4 | 1300 | 0.2322 | 0.9478 | |
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| 0.0002 | 11.2 | 1400 | 0.2342 | 0.9478 | |
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| 0.0002 | 12.0 | 1500 | 0.2348 | 0.9490 | |
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| 0.0001 | 12.8 | 1600 | 0.2358 | 0.9490 | |
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| 0.0001 | 13.6 | 1700 | 0.2368 | 0.9497 | |
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| 0.0001 | 14.4 | 1800 | 0.2377 | 0.9510 | |
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| 0.0001 | 15.2 | 1900 | 0.2384 | 0.9516 | |
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| 0.0001 | 16.0 | 2000 | 0.2391 | 0.9516 | |
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| 0.0001 | 16.8 | 2100 | 0.2397 | 0.9522 | |
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| 0.0001 | 17.6 | 2200 | 0.2401 | 0.9522 | |
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| 0.0001 | 18.4 | 2300 | 0.2406 | 0.9522 | |
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| 0.0001 | 19.2 | 2400 | 0.2409 | 0.9522 | |
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| 0.0001 | 20.0 | 2500 | 0.2410 | 0.9522 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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