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--- |
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license: apache-2.0 |
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base_model: facebook/convnextv2-pico-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-pico-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-pico-1k-224-finetuned-galaxy10-decals |
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This model is a fine-tuned version of [facebook/convnextv2-pico-1k-224](https://huggingface.co/facebook/convnextv2-pico-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: 0.5795 |
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- Accuracy: 0.8546 |
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- Precision: 0.8565 |
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- Recall: 0.8546 |
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- F1: 0.8545 |
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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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| 1.0752 | 0.99 | 62 | 0.9584 | 0.6697 | 0.6820 | 0.6697 | 0.6581 | |
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| 0.814 | 2.0 | 125 | 0.6716 | 0.7728 | 0.7780 | 0.7728 | 0.7695 | |
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| 0.7289 | 2.99 | 187 | 0.6071 | 0.7959 | 0.8093 | 0.7959 | 0.7943 | |
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| 0.6444 | 4.0 | 250 | 0.5873 | 0.8055 | 0.8103 | 0.8055 | 0.8019 | |
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| 0.5855 | 4.99 | 312 | 0.5889 | 0.8106 | 0.8226 | 0.8106 | 0.8106 | |
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| 0.5778 | 6.0 | 375 | 0.5039 | 0.8281 | 0.8321 | 0.8281 | 0.8274 | |
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| 0.5575 | 6.99 | 437 | 0.5162 | 0.8140 | 0.8235 | 0.8140 | 0.8148 | |
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| 0.5011 | 8.0 | 500 | 0.5369 | 0.8207 | 0.8234 | 0.8207 | 0.8205 | |
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| 0.4968 | 8.99 | 562 | 0.5152 | 0.8292 | 0.8282 | 0.8292 | 0.8270 | |
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| 0.4593 | 10.0 | 625 | 0.4854 | 0.8382 | 0.8408 | 0.8382 | 0.8367 | |
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| 0.4442 | 10.99 | 687 | 0.4923 | 0.8416 | 0.8423 | 0.8416 | 0.8411 | |
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| 0.4071 | 12.0 | 750 | 0.5312 | 0.8377 | 0.8356 | 0.8377 | 0.8331 | |
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| 0.4057 | 12.99 | 812 | 0.4954 | 0.8433 | 0.8449 | 0.8433 | 0.8416 | |
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| 0.4074 | 14.0 | 875 | 0.4735 | 0.8534 | 0.8509 | 0.8534 | 0.8493 | |
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| 0.3709 | 14.99 | 937 | 0.4977 | 0.8461 | 0.8450 | 0.8461 | 0.8442 | |
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| 0.3467 | 16.0 | 1000 | 0.5364 | 0.8286 | 0.8278 | 0.8286 | 0.8274 | |
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| 0.3129 | 16.99 | 1062 | 0.5695 | 0.8422 | 0.8413 | 0.8422 | 0.8376 | |
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| 0.3242 | 18.0 | 1125 | 0.5131 | 0.8455 | 0.8469 | 0.8455 | 0.8450 | |
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| 0.3046 | 18.99 | 1187 | 0.5553 | 0.8399 | 0.8382 | 0.8399 | 0.8371 | |
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| 0.2805 | 20.0 | 1250 | 0.5871 | 0.8523 | 0.8532 | 0.8523 | 0.8456 | |
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| 0.2776 | 20.99 | 1312 | 0.5428 | 0.8433 | 0.8404 | 0.8433 | 0.8404 | |
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| 0.2975 | 22.0 | 1375 | 0.5624 | 0.8393 | 0.8344 | 0.8393 | 0.8359 | |
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| 0.268 | 22.99 | 1437 | 0.5485 | 0.8495 | 0.8518 | 0.8495 | 0.8498 | |
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| 0.2535 | 24.0 | 1500 | 0.6135 | 0.8382 | 0.8367 | 0.8382 | 0.8358 | |
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| 0.2543 | 24.99 | 1562 | 0.6103 | 0.8393 | 0.8389 | 0.8393 | 0.8375 | |
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| 0.2283 | 26.0 | 1625 | 0.5639 | 0.8484 | 0.8499 | 0.8484 | 0.8480 | |
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| 0.2341 | 26.99 | 1687 | 0.5795 | 0.8546 | 0.8565 | 0.8546 | 0.8545 | |
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| 0.2404 | 28.0 | 1750 | 0.5794 | 0.8534 | 0.8515 | 0.8534 | 0.8511 | |
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| 0.2168 | 28.99 | 1812 | 0.5652 | 0.8546 | 0.8525 | 0.8546 | 0.8524 | |
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| 0.2057 | 29.76 | 1860 | 0.5650 | 0.8546 | 0.8519 | 0.8546 | 0.8518 | |
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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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