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--- |
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license: apache-2.0 |
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base_model: facebook/convnextv2-atto-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-atto-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-atto-1k-224-finetuned-galaxy10-decals |
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This model is a fine-tuned version of [facebook/convnextv2-atto-1k-224](https://huggingface.co/facebook/convnextv2-atto-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: 0.4668 |
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- Accuracy: 0.8461 |
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- Precision: 0.8444 |
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- Recall: 0.8461 |
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- F1: 0.8442 |
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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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| 2.0062 | 0.99 | 62 | 1.8928 | 0.3450 | 0.3432 | 0.3450 | 0.2956 | |
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| 1.1323 | 2.0 | 125 | 1.0026 | 0.6590 | 0.6634 | 0.6590 | 0.6399 | |
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| 0.8977 | 2.99 | 187 | 0.7348 | 0.7486 | 0.7415 | 0.7486 | 0.7399 | |
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| 0.7119 | 4.0 | 250 | 0.6395 | 0.7892 | 0.7878 | 0.7892 | 0.7770 | |
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| 0.6393 | 4.99 | 312 | 0.5801 | 0.7971 | 0.7916 | 0.7971 | 0.7915 | |
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| 0.6463 | 6.0 | 375 | 0.5958 | 0.7976 | 0.8147 | 0.7976 | 0.7909 | |
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| 0.6197 | 6.99 | 437 | 0.5363 | 0.8151 | 0.8119 | 0.8151 | 0.8112 | |
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| 0.5779 | 8.0 | 500 | 0.5276 | 0.8207 | 0.8205 | 0.8207 | 0.8185 | |
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| 0.5841 | 8.99 | 562 | 0.5197 | 0.8185 | 0.8203 | 0.8185 | 0.8157 | |
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| 0.5597 | 10.0 | 625 | 0.5025 | 0.8253 | 0.8192 | 0.8253 | 0.8193 | |
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| 0.5437 | 10.99 | 687 | 0.4912 | 0.8309 | 0.8295 | 0.8309 | 0.8296 | |
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| 0.5242 | 12.0 | 750 | 0.5001 | 0.8275 | 0.8303 | 0.8275 | 0.8245 | |
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| 0.5029 | 12.99 | 812 | 0.5075 | 0.8241 | 0.8228 | 0.8241 | 0.8208 | |
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| 0.5396 | 14.0 | 875 | 0.4784 | 0.8393 | 0.8395 | 0.8393 | 0.8371 | |
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| 0.4746 | 14.99 | 937 | 0.4727 | 0.8331 | 0.8318 | 0.8331 | 0.8317 | |
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| 0.4786 | 16.0 | 1000 | 0.4856 | 0.8331 | 0.8308 | 0.8331 | 0.8300 | |
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| 0.4338 | 16.99 | 1062 | 0.4884 | 0.8337 | 0.8333 | 0.8337 | 0.8309 | |
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| 0.4772 | 18.0 | 1125 | 0.4618 | 0.8405 | 0.8370 | 0.8405 | 0.8377 | |
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| 0.4733 | 18.99 | 1187 | 0.4740 | 0.8393 | 0.8394 | 0.8393 | 0.8381 | |
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| 0.4475 | 20.0 | 1250 | 0.4678 | 0.8388 | 0.8349 | 0.8388 | 0.8345 | |
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| 0.4229 | 20.99 | 1312 | 0.4881 | 0.8331 | 0.8317 | 0.8331 | 0.8303 | |
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| 0.46 | 22.0 | 1375 | 0.4728 | 0.8410 | 0.8382 | 0.8410 | 0.8371 | |
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| 0.4298 | 22.99 | 1437 | 0.4642 | 0.8360 | 0.8348 | 0.8360 | 0.8345 | |
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| 0.4225 | 24.0 | 1500 | 0.4706 | 0.8371 | 0.8368 | 0.8371 | 0.8359 | |
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| 0.426 | 24.99 | 1562 | 0.4733 | 0.8399 | 0.8367 | 0.8399 | 0.8371 | |
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| 0.3839 | 26.0 | 1625 | 0.4682 | 0.8444 | 0.8423 | 0.8444 | 0.8422 | |
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| 0.4007 | 26.99 | 1687 | 0.4665 | 0.8382 | 0.8371 | 0.8382 | 0.8367 | |
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| 0.4245 | 28.0 | 1750 | 0.4695 | 0.8388 | 0.8357 | 0.8388 | 0.8358 | |
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| 0.3868 | 28.99 | 1812 | 0.4668 | 0.8461 | 0.8444 | 0.8461 | 0.8442 | |
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| 0.3933 | 29.76 | 1860 | 0.4657 | 0.8461 | 0.8442 | 0.8461 | 0.8440 | |
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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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