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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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- 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-22k-384-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-22k-384-finetuned-galaxy10-decals |
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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 matthieulel/galaxy10_decals dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4641 |
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- Accuracy: 0.8675 |
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- Precision: 0.8664 |
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- Recall: 0.8675 |
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- F1: 0.8661 |
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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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| 1.5875 | 0.99 | 62 | 1.3967 | 0.5423 | 0.5237 | 0.5423 | 0.5001 | |
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| 0.8561 | 2.0 | 125 | 0.7084 | 0.7773 | 0.7769 | 0.7773 | 0.7692 | |
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| 0.7139 | 2.99 | 187 | 0.5607 | 0.8230 | 0.8201 | 0.8230 | 0.8148 | |
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| 0.5799 | 4.0 | 250 | 0.4982 | 0.8410 | 0.8428 | 0.8410 | 0.8324 | |
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| 0.5352 | 4.99 | 312 | 0.4781 | 0.8461 | 0.8470 | 0.8461 | 0.8446 | |
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| 0.539 | 6.0 | 375 | 0.4538 | 0.8523 | 0.8578 | 0.8523 | 0.8482 | |
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| 0.5129 | 6.99 | 437 | 0.4496 | 0.8472 | 0.8486 | 0.8472 | 0.8468 | |
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| 0.4685 | 8.0 | 500 | 0.4458 | 0.8551 | 0.8589 | 0.8551 | 0.8542 | |
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| 0.4675 | 8.99 | 562 | 0.4352 | 0.8613 | 0.8651 | 0.8613 | 0.8579 | |
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| 0.441 | 10.0 | 625 | 0.4076 | 0.8636 | 0.8616 | 0.8636 | 0.8607 | |
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| 0.4214 | 10.99 | 687 | 0.4346 | 0.8517 | 0.8556 | 0.8517 | 0.8522 | |
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| 0.4016 | 12.0 | 750 | 0.4300 | 0.8591 | 0.8597 | 0.8591 | 0.8573 | |
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| 0.3913 | 12.99 | 812 | 0.4164 | 0.8625 | 0.8624 | 0.8625 | 0.8601 | |
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| 0.3882 | 14.0 | 875 | 0.4246 | 0.8591 | 0.8618 | 0.8591 | 0.8570 | |
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| 0.3341 | 14.99 | 937 | 0.4321 | 0.8574 | 0.8555 | 0.8574 | 0.8555 | |
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| 0.3522 | 16.0 | 1000 | 0.4322 | 0.8568 | 0.8561 | 0.8568 | 0.8542 | |
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| 0.2824 | 16.99 | 1062 | 0.4364 | 0.8608 | 0.8606 | 0.8608 | 0.8586 | |
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| 0.315 | 18.0 | 1125 | 0.4495 | 0.8579 | 0.8581 | 0.8579 | 0.8559 | |
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| 0.3084 | 18.99 | 1187 | 0.4536 | 0.8608 | 0.8593 | 0.8608 | 0.8590 | |
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| 0.2864 | 20.0 | 1250 | 0.4417 | 0.8630 | 0.8621 | 0.8630 | 0.8607 | |
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| 0.2654 | 20.99 | 1312 | 0.4585 | 0.8630 | 0.8628 | 0.8630 | 0.8610 | |
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| 0.3067 | 22.0 | 1375 | 0.4673 | 0.8557 | 0.8562 | 0.8557 | 0.8538 | |
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| 0.2771 | 22.99 | 1437 | 0.4679 | 0.8596 | 0.8577 | 0.8596 | 0.8577 | |
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| 0.2588 | 24.0 | 1500 | 0.4616 | 0.8653 | 0.8646 | 0.8653 | 0.8633 | |
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| 0.2583 | 24.99 | 1562 | 0.4726 | 0.8591 | 0.8572 | 0.8591 | 0.8567 | |
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| 0.2517 | 26.0 | 1625 | 0.4618 | 0.8630 | 0.8625 | 0.8630 | 0.8619 | |
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| 0.2454 | 26.99 | 1687 | 0.4612 | 0.8641 | 0.8630 | 0.8641 | 0.8629 | |
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| 0.259 | 28.0 | 1750 | 0.4685 | 0.8613 | 0.8595 | 0.8613 | 0.8594 | |
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| 0.2388 | 28.99 | 1812 | 0.4668 | 0.8636 | 0.8622 | 0.8636 | 0.8620 | |
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| 0.2414 | 29.76 | 1860 | 0.4641 | 0.8675 | 0.8664 | 0.8675 | 0.8661 | |
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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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