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--- |
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license: apache-2.0 |
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base_model: google/vit-large-patch32-224-in21k |
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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: vit-large-patch32-224-in21k-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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# vit-large-patch32-224-in21k-finetuned-galaxy10-decals |
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This model is a fine-tuned version of [google/vit-large-patch32-224-in21k](https://huggingface.co/google/vit-large-patch32-224-in21k) on the matthieulel/galaxy10_decals dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5281 |
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- Accuracy: 0.8382 |
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- Precision: 0.8372 |
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- Recall: 0.8382 |
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- F1: 0.8356 |
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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: 0.0001 |
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- train_batch_size: 128 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 512 |
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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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| 1.8923 | 0.99 | 31 | 1.6725 | 0.4600 | 0.5537 | 0.4600 | 0.3682 | |
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| 1.1787 | 1.98 | 62 | 0.9949 | 0.7339 | 0.7513 | 0.7339 | 0.7095 | |
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| 0.9165 | 2.98 | 93 | 0.7946 | 0.7700 | 0.7694 | 0.7700 | 0.7540 | |
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| 0.802 | 4.0 | 125 | 0.6747 | 0.7948 | 0.7954 | 0.7948 | 0.7843 | |
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| 0.7074 | 4.99 | 156 | 0.6196 | 0.8117 | 0.8139 | 0.8117 | 0.8115 | |
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| 0.6424 | 5.98 | 187 | 0.6205 | 0.8021 | 0.8075 | 0.8021 | 0.7961 | |
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| 0.6309 | 6.98 | 218 | 0.5760 | 0.8117 | 0.8231 | 0.8117 | 0.8127 | |
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| 0.5682 | 8.0 | 250 | 0.5748 | 0.8151 | 0.8196 | 0.8151 | 0.8157 | |
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| 0.5981 | 8.99 | 281 | 0.5704 | 0.8213 | 0.8269 | 0.8213 | 0.8158 | |
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| 0.547 | 9.98 | 312 | 0.5282 | 0.8377 | 0.8352 | 0.8377 | 0.8345 | |
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| 0.5067 | 10.98 | 343 | 0.5281 | 0.8382 | 0.8372 | 0.8382 | 0.8356 | |
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| 0.5066 | 12.0 | 375 | 0.5441 | 0.8247 | 0.8286 | 0.8247 | 0.8219 | |
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| 0.4919 | 12.99 | 406 | 0.5580 | 0.8157 | 0.8236 | 0.8157 | 0.8155 | |
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| 0.4508 | 13.98 | 437 | 0.5269 | 0.8303 | 0.8331 | 0.8303 | 0.8279 | |
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| 0.4415 | 14.98 | 468 | 0.5399 | 0.8185 | 0.8249 | 0.8185 | 0.8203 | |
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| 0.4178 | 16.0 | 500 | 0.5229 | 0.8320 | 0.8358 | 0.8320 | 0.8301 | |
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| 0.366 | 16.99 | 531 | 0.5427 | 0.8275 | 0.8281 | 0.8275 | 0.8241 | |
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| 0.3706 | 17.98 | 562 | 0.5389 | 0.8241 | 0.8242 | 0.8241 | 0.8230 | |
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| 0.3609 | 18.98 | 593 | 0.5573 | 0.8247 | 0.8262 | 0.8247 | 0.8239 | |
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| 0.3443 | 20.0 | 625 | 0.5605 | 0.8320 | 0.8325 | 0.8320 | 0.8302 | |
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| 0.3214 | 20.99 | 656 | 0.5667 | 0.8281 | 0.8295 | 0.8281 | 0.8254 | |
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| 0.3262 | 21.98 | 687 | 0.5797 | 0.8236 | 0.8237 | 0.8236 | 0.8214 | |
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| 0.299 | 22.98 | 718 | 0.5938 | 0.8202 | 0.8225 | 0.8202 | 0.8195 | |
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| 0.2792 | 24.0 | 750 | 0.5909 | 0.8275 | 0.8258 | 0.8275 | 0.8251 | |
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| 0.2969 | 24.99 | 781 | 0.5658 | 0.8309 | 0.8319 | 0.8309 | 0.8306 | |
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| 0.2559 | 25.98 | 812 | 0.5936 | 0.8309 | 0.8294 | 0.8309 | 0.8294 | |
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| 0.2756 | 26.98 | 843 | 0.5898 | 0.8292 | 0.8295 | 0.8292 | 0.8287 | |
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| 0.254 | 28.0 | 875 | 0.6043 | 0.8303 | 0.8319 | 0.8303 | 0.8289 | |
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| 0.2674 | 28.99 | 906 | 0.5950 | 0.8371 | 0.8365 | 0.8371 | 0.8353 | |
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| 0.2432 | 29.76 | 930 | 0.5907 | 0.8360 | 0.8348 | 0.8360 | 0.8345 | |
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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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