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--- |
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license: apache-2.0 |
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base_model: facebook/convnextv2-tiny-1k-224 |
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tags: |
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- image-classification |
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- vision |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: convnextv2-tiny-1k-224-finetuned-galaxy10-decals |
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results: [] |
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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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# convnextv2-tiny-1k-224-finetuned-galaxy10-decals |
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This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on the matthieulel/galaxy10_decals dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3993 |
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- Accuracy: 0.8732 |
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- Precision: 0.8714 |
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- Recall: 0.8732 |
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- F1: 0.8715 |
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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: 5e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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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: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 1.8139 | 0.99 | 62 | 1.6803 | 0.4628 | 0.4589 | 0.4628 | 0.3836 | |
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| 1.0894 | 2.0 | 125 | 0.9304 | 0.6984 | 0.6965 | 0.6984 | 0.6800 | |
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| 0.8423 | 2.99 | 187 | 0.6630 | 0.7880 | 0.7858 | 0.7880 | 0.7826 | |
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| 0.6564 | 4.0 | 250 | 0.5769 | 0.8055 | 0.8091 | 0.8055 | 0.7970 | |
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| 0.5927 | 4.99 | 312 | 0.5283 | 0.8241 | 0.8276 | 0.8241 | 0.8240 | |
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| 0.5853 | 6.0 | 375 | 0.5106 | 0.8303 | 0.8342 | 0.8303 | 0.8237 | |
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| 0.5757 | 6.99 | 437 | 0.4490 | 0.8540 | 0.8514 | 0.8540 | 0.8521 | |
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| 0.5235 | 8.0 | 500 | 0.4651 | 0.8546 | 0.8578 | 0.8546 | 0.8536 | |
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| 0.5166 | 8.99 | 562 | 0.4501 | 0.8563 | 0.8551 | 0.8563 | 0.8523 | |
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| 0.486 | 10.0 | 625 | 0.4352 | 0.8647 | 0.8624 | 0.8647 | 0.8626 | |
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| 0.4882 | 10.99 | 687 | 0.4296 | 0.8613 | 0.8594 | 0.8613 | 0.8597 | |
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| 0.4426 | 12.0 | 750 | 0.4314 | 0.8579 | 0.8614 | 0.8579 | 0.8566 | |
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| 0.457 | 12.99 | 812 | 0.4226 | 0.8641 | 0.8642 | 0.8641 | 0.8624 | |
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| 0.4512 | 14.0 | 875 | 0.4319 | 0.8619 | 0.8653 | 0.8619 | 0.8591 | |
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| 0.4059 | 14.99 | 937 | 0.4124 | 0.8692 | 0.8675 | 0.8692 | 0.8681 | |
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| 0.4147 | 16.0 | 1000 | 0.3993 | 0.8732 | 0.8714 | 0.8732 | 0.8715 | |
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| 0.3721 | 16.99 | 1062 | 0.4116 | 0.8636 | 0.8609 | 0.8636 | 0.8604 | |
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| 0.3908 | 18.0 | 1125 | 0.4098 | 0.8675 | 0.8663 | 0.8675 | 0.8665 | |
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| 0.3836 | 18.99 | 1187 | 0.4188 | 0.8670 | 0.8651 | 0.8670 | 0.8651 | |
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| 0.3716 | 20.0 | 1250 | 0.4172 | 0.8681 | 0.8653 | 0.8681 | 0.8661 | |
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| 0.3484 | 20.99 | 1312 | 0.4404 | 0.8653 | 0.8649 | 0.8653 | 0.8628 | |
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| 0.3895 | 22.0 | 1375 | 0.4194 | 0.8698 | 0.8689 | 0.8698 | 0.8688 | |
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| 0.3452 | 22.99 | 1437 | 0.4447 | 0.8630 | 0.8634 | 0.8630 | 0.8621 | |
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| 0.341 | 24.0 | 1500 | 0.4253 | 0.8720 | 0.8722 | 0.8720 | 0.8712 | |
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| 0.3481 | 24.99 | 1562 | 0.4325 | 0.8681 | 0.8656 | 0.8681 | 0.8658 | |
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| 0.3115 | 26.0 | 1625 | 0.4340 | 0.8619 | 0.8609 | 0.8619 | 0.8603 | |
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| 0.313 | 26.99 | 1687 | 0.4329 | 0.8653 | 0.8644 | 0.8653 | 0.8644 | |
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| 0.3362 | 28.0 | 1750 | 0.4329 | 0.8653 | 0.8636 | 0.8653 | 0.8639 | |
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| 0.3056 | 28.99 | 1812 | 0.4342 | 0.8658 | 0.8645 | 0.8658 | 0.8644 | |
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| 0.3206 | 29.76 | 1860 | 0.4343 | 0.8664 | 0.8648 | 0.8664 | 0.8649 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.3.0 |
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- Datasets 2.19.1 |
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- Tokenizers 0.15.1 |
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