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--- |
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license: apache-2.0 |
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base_model: facebook/convnextv2-nano-22k-384 |
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tags: |
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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-nano-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-nano-22k-384-finetuned-galaxy10-decals |
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This model is a fine-tuned version of [facebook/convnextv2-nano-22k-384](https://huggingface.co./facebook/convnextv2-nano-22k-384) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4449 |
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- Accuracy: 0.8630 |
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- Precision: 0.8607 |
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- Recall: 0.8630 |
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- F1: 0.8610 |
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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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| 1.6939 | 0.99 | 62 | 1.5326 | 0.4656 | 0.4580 | 0.4656 | 0.4176 | |
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| 0.9882 | 2.0 | 125 | 0.8491 | 0.7142 | 0.7196 | 0.7142 | 0.7066 | |
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| 0.7595 | 2.99 | 187 | 0.6041 | 0.7993 | 0.7990 | 0.7993 | 0.7947 | |
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| 0.6097 | 4.0 | 250 | 0.5397 | 0.8134 | 0.8078 | 0.8134 | 0.8069 | |
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| 0.5565 | 4.99 | 312 | 0.4990 | 0.8286 | 0.8269 | 0.8286 | 0.8268 | |
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| 0.5822 | 6.0 | 375 | 0.4684 | 0.8427 | 0.8425 | 0.8427 | 0.8374 | |
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| 0.5244 | 6.99 | 437 | 0.4484 | 0.8512 | 0.8476 | 0.8512 | 0.8483 | |
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| 0.4957 | 8.0 | 500 | 0.4487 | 0.8506 | 0.8543 | 0.8506 | 0.8514 | |
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| 0.4857 | 8.99 | 562 | 0.4369 | 0.8579 | 0.8572 | 0.8579 | 0.8545 | |
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| 0.4634 | 10.0 | 625 | 0.4104 | 0.8658 | 0.8630 | 0.8658 | 0.8639 | |
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| 0.4433 | 10.99 | 687 | 0.4117 | 0.8664 | 0.8649 | 0.8664 | 0.8651 | |
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| 0.4267 | 12.0 | 750 | 0.4096 | 0.8664 | 0.8632 | 0.8664 | 0.8634 | |
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| 0.4201 | 12.99 | 812 | 0.4212 | 0.8658 | 0.8645 | 0.8658 | 0.8631 | |
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| 0.4176 | 14.0 | 875 | 0.4057 | 0.8681 | 0.8662 | 0.8681 | 0.8650 | |
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| 0.3717 | 14.99 | 937 | 0.4299 | 0.8568 | 0.8547 | 0.8568 | 0.8551 | |
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| 0.3759 | 16.0 | 1000 | 0.4446 | 0.8585 | 0.8563 | 0.8585 | 0.8555 | |
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| 0.3264 | 16.99 | 1062 | 0.4276 | 0.8647 | 0.8630 | 0.8647 | 0.8623 | |
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| 0.3573 | 18.0 | 1125 | 0.4199 | 0.8641 | 0.8621 | 0.8641 | 0.8610 | |
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| 0.3356 | 18.99 | 1187 | 0.4388 | 0.8585 | 0.8597 | 0.8585 | 0.8579 | |
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| 0.3313 | 20.0 | 1250 | 0.4385 | 0.8602 | 0.8585 | 0.8602 | 0.8571 | |
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| 0.3044 | 20.99 | 1312 | 0.4485 | 0.8585 | 0.8578 | 0.8585 | 0.8560 | |
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| 0.3525 | 22.0 | 1375 | 0.4303 | 0.8647 | 0.8641 | 0.8647 | 0.8634 | |
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| 0.3207 | 22.99 | 1437 | 0.4525 | 0.8608 | 0.8597 | 0.8608 | 0.8591 | |
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| 0.3044 | 24.0 | 1500 | 0.4417 | 0.8591 | 0.8578 | 0.8591 | 0.8579 | |
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| 0.3088 | 24.99 | 1562 | 0.4626 | 0.8608 | 0.8586 | 0.8608 | 0.8582 | |
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| 0.2897 | 26.0 | 1625 | 0.4524 | 0.8630 | 0.8606 | 0.8630 | 0.8606 | |
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| 0.2823 | 26.99 | 1687 | 0.4433 | 0.8670 | 0.8657 | 0.8670 | 0.8657 | |
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| 0.2928 | 28.0 | 1750 | 0.4479 | 0.8658 | 0.8629 | 0.8658 | 0.8631 | |
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| 0.2695 | 28.99 | 1812 | 0.4455 | 0.8658 | 0.8637 | 0.8658 | 0.8639 | |
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| 0.274 | 29.76 | 1860 | 0.4449 | 0.8630 | 0.8607 | 0.8630 | 0.8610 | |
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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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