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--- |
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license: apache-2.0 |
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base_model: google/siglip-base-patch16-224 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- stanford-dogs |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: google-siglip-base-patch16-224-batch64-lr5e-05-standford-dogs |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: stanford-dogs |
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type: stanford-dogs |
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config: default |
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split: full |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8364917395529641 |
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- name: F1 |
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type: f1 |
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value: 0.8328749982143954 |
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- name: Precision |
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type: precision |
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value: 0.8377481660081763 |
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- name: Recall |
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type: recall |
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value: 0.8330663170433035 |
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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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# google-siglip-base-patch16-224-batch64-lr5e-05-standford-dogs |
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This model is a fine-tuned version of [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) on the stanford-dogs dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5612 |
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- Accuracy: 0.8365 |
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- F1: 0.8329 |
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- Precision: 0.8377 |
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- Recall: 0.8331 |
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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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- training_steps: 1000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 4.822 | 0.1550 | 10 | 4.2549 | 0.0782 | 0.0493 | 0.0987 | 0.0726 | |
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| 4.236 | 0.3101 | 20 | 3.5279 | 0.1907 | 0.1507 | 0.2201 | 0.1830 | |
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| 3.5066 | 0.4651 | 30 | 2.5316 | 0.3319 | 0.2941 | 0.4180 | 0.3205 | |
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| 2.8064 | 0.6202 | 40 | 2.1243 | 0.4361 | 0.4090 | 0.5324 | 0.4282 | |
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| 2.441 | 0.7752 | 50 | 1.5798 | 0.5510 | 0.5250 | 0.6242 | 0.5438 | |
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| 2.0985 | 0.9302 | 60 | 1.4242 | 0.5843 | 0.5577 | 0.6400 | 0.5768 | |
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| 1.8689 | 1.0853 | 70 | 1.1481 | 0.6625 | 0.6456 | 0.7143 | 0.6565 | |
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| 1.6588 | 1.2403 | 80 | 1.1937 | 0.6465 | 0.6361 | 0.7062 | 0.6439 | |
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| 1.5807 | 1.3953 | 90 | 0.9818 | 0.7058 | 0.6890 | 0.7438 | 0.6981 | |
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| 1.4851 | 1.5504 | 100 | 1.0181 | 0.7000 | 0.6839 | 0.7373 | 0.6959 | |
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| 1.5033 | 1.7054 | 110 | 1.0169 | 0.6914 | 0.6845 | 0.7490 | 0.6883 | |
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| 1.3022 | 1.8605 | 120 | 0.9087 | 0.7276 | 0.7170 | 0.7643 | 0.7222 | |
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| 1.3106 | 2.0155 | 130 | 0.8385 | 0.7432 | 0.7352 | 0.7667 | 0.7363 | |
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| 1.1721 | 2.1705 | 140 | 0.8957 | 0.7128 | 0.7026 | 0.7592 | 0.7075 | |
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| 1.131 | 2.3256 | 150 | 0.8730 | 0.7259 | 0.7149 | 0.7687 | 0.7196 | |
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| 1.1223 | 2.4806 | 160 | 0.8132 | 0.7546 | 0.7457 | 0.7855 | 0.7482 | |
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| 1.0688 | 2.6357 | 170 | 0.7485 | 0.7704 | 0.7601 | 0.7863 | 0.7631 | |
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| 1.0686 | 2.7907 | 180 | 0.7559 | 0.7651 | 0.7587 | 0.7920 | 0.7609 | |
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| 0.9733 | 2.9457 | 190 | 0.7779 | 0.7553 | 0.7458 | 0.7797 | 0.7521 | |
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| 0.9287 | 3.1008 | 200 | 0.7048 | 0.7818 | 0.7756 | 0.7981 | 0.7756 | |
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| 0.8746 | 3.2558 | 210 | 0.6848 | 0.7867 | 0.7774 | 0.8034 | 0.7822 | |
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| 0.7982 | 3.4109 | 220 | 0.6930 | 0.7884 | 0.7796 | 0.8025 | 0.7846 | |
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| 0.823 | 3.5659 | 230 | 0.7041 | 0.7804 | 0.7717 | 0.7975 | 0.7752 | |
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| 0.8713 | 3.7209 | 240 | 0.7418 | 0.7755 | 0.7646 | 0.8053 | 0.7711 | |
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| 0.8651 | 3.8760 | 250 | 0.6847 | 0.7828 | 0.7773 | 0.8048 | 0.7782 | |
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| 0.784 | 4.0310 | 260 | 0.6662 | 0.7923 | 0.7841 | 0.8097 | 0.7860 | |
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| 0.6894 | 4.1860 | 270 | 0.6980 | 0.7843 | 0.7781 | 0.8024 | 0.7779 | |
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| 0.7727 | 4.3411 | 280 | 0.6629 | 0.7833 | 0.7804 | 0.8030 | 0.7798 | |
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| 0.6978 | 4.4961 | 290 | 0.6820 | 0.7845 | 0.7800 | 0.8011 | 0.7820 | |
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| 0.7032 | 4.6512 | 300 | 0.6148 | 0.8032 | 0.7969 | 0.8094 | 0.7985 | |
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| 0.6978 | 4.8062 | 310 | 0.6457 | 0.7940 | 0.7872 | 0.8085 | 0.7892 | |
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| 0.66 | 4.9612 | 320 | 0.6242 | 0.8088 | 0.8033 | 0.8246 | 0.8058 | |
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| 0.5706 | 5.1163 | 330 | 0.6404 | 0.7966 | 0.7905 | 0.8097 | 0.7928 | |
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| 0.5456 | 5.2713 | 340 | 0.7147 | 0.7872 | 0.7767 | 0.8060 | 0.7819 | |
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| 0.5869 | 5.4264 | 350 | 0.6267 | 0.8066 | 0.8016 | 0.8188 | 0.8025 | |
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| 0.6022 | 5.5814 | 360 | 0.6197 | 0.8061 | 0.8028 | 0.8209 | 0.8027 | |
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| 0.5676 | 5.7364 | 370 | 0.6061 | 0.8059 | 0.8005 | 0.8140 | 0.8024 | |
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| 0.5456 | 5.8915 | 380 | 0.6018 | 0.8069 | 0.8006 | 0.8254 | 0.8033 | |
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| 0.56 | 6.0465 | 390 | 0.6126 | 0.8090 | 0.8037 | 0.8206 | 0.8045 | |
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| 0.4582 | 6.2016 | 400 | 0.6122 | 0.8115 | 0.8062 | 0.8196 | 0.8061 | |
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| 0.4594 | 6.3566 | 410 | 0.6058 | 0.8122 | 0.8081 | 0.8235 | 0.8082 | |
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| 0.4868 | 6.5116 | 420 | 0.5890 | 0.8195 | 0.8131 | 0.8300 | 0.8141 | |
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| 0.4841 | 6.6667 | 430 | 0.5909 | 0.8175 | 0.8119 | 0.8250 | 0.8133 | |
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| 0.4537 | 6.8217 | 440 | 0.5889 | 0.8195 | 0.8153 | 0.8261 | 0.8164 | |
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| 0.4807 | 6.9767 | 450 | 0.6105 | 0.8144 | 0.8104 | 0.8300 | 0.8106 | |
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| 0.4051 | 7.1318 | 460 | 0.5917 | 0.8171 | 0.8103 | 0.8217 | 0.8131 | |
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| 0.3727 | 7.2868 | 470 | 0.6037 | 0.8166 | 0.8116 | 0.8262 | 0.8125 | |
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| 0.4034 | 7.4419 | 480 | 0.6407 | 0.8032 | 0.8003 | 0.8146 | 0.8015 | |
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| 0.3684 | 7.5969 | 490 | 0.6205 | 0.8061 | 0.7997 | 0.8176 | 0.8008 | |
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| 0.416 | 7.7519 | 500 | 0.5855 | 0.8258 | 0.8207 | 0.8364 | 0.8211 | |
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| 0.3947 | 7.9070 | 510 | 0.5802 | 0.8214 | 0.8179 | 0.8283 | 0.8179 | |
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| 0.3731 | 8.0620 | 520 | 0.5870 | 0.8239 | 0.8191 | 0.8324 | 0.8188 | |
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| 0.3203 | 8.2171 | 530 | 0.5783 | 0.8265 | 0.8211 | 0.8302 | 0.8216 | |
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| 0.337 | 8.3721 | 540 | 0.5836 | 0.8200 | 0.8162 | 0.8247 | 0.8166 | |
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| 0.3396 | 8.5271 | 550 | 0.5992 | 0.8156 | 0.8121 | 0.8253 | 0.8115 | |
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| 0.3355 | 8.6822 | 560 | 0.5755 | 0.8229 | 0.8182 | 0.8281 | 0.8187 | |
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| 0.3273 | 8.8372 | 570 | 0.5819 | 0.8246 | 0.8194 | 0.8268 | 0.8208 | |
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| 0.3181 | 8.9922 | 580 | 0.5840 | 0.8205 | 0.8174 | 0.8279 | 0.8168 | |
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| 0.2855 | 9.1473 | 590 | 0.5997 | 0.8144 | 0.8098 | 0.8213 | 0.8103 | |
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| 0.254 | 9.3023 | 600 | 0.5863 | 0.8183 | 0.8132 | 0.8251 | 0.8133 | |
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| 0.2781 | 9.4574 | 610 | 0.5779 | 0.8224 | 0.8169 | 0.8275 | 0.8195 | |
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| 0.2691 | 9.6124 | 620 | 0.5816 | 0.8219 | 0.8177 | 0.8257 | 0.8186 | |
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| 0.3018 | 9.7674 | 630 | 0.5814 | 0.8297 | 0.8250 | 0.8370 | 0.8253 | |
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| 0.2615 | 9.9225 | 640 | 0.5761 | 0.8299 | 0.8261 | 0.8377 | 0.8262 | |
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| 0.2707 | 10.0775 | 650 | 0.5640 | 0.8326 | 0.8283 | 0.8385 | 0.8284 | |
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| 0.2482 | 10.2326 | 660 | 0.5685 | 0.8246 | 0.8206 | 0.8284 | 0.8218 | |
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| 0.2493 | 10.3876 | 670 | 0.5717 | 0.8241 | 0.8208 | 0.8311 | 0.8199 | |
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| 0.2167 | 10.5426 | 680 | 0.5741 | 0.8246 | 0.8204 | 0.8273 | 0.8204 | |
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| 0.2628 | 10.6977 | 690 | 0.5791 | 0.8248 | 0.8205 | 0.8281 | 0.8216 | |
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| 0.2316 | 10.8527 | 700 | 0.5770 | 0.8321 | 0.8272 | 0.8348 | 0.8284 | |
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| 0.2326 | 11.0078 | 710 | 0.5755 | 0.8280 | 0.8249 | 0.8348 | 0.8249 | |
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| 0.2001 | 11.1628 | 720 | 0.5783 | 0.8336 | 0.8299 | 0.8354 | 0.8310 | |
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| 0.1759 | 11.3178 | 730 | 0.5804 | 0.8345 | 0.8302 | 0.8367 | 0.8311 | |
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| 0.202 | 11.4729 | 740 | 0.5820 | 0.8316 | 0.8278 | 0.8353 | 0.8280 | |
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| 0.2191 | 11.6279 | 750 | 0.5724 | 0.8324 | 0.8279 | 0.8341 | 0.8287 | |
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| 0.1955 | 11.7829 | 760 | 0.5957 | 0.8226 | 0.8181 | 0.8268 | 0.8198 | |
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| 0.1972 | 11.9380 | 770 | 0.5722 | 0.8294 | 0.8254 | 0.8318 | 0.8263 | |
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| 0.1848 | 12.0930 | 780 | 0.5731 | 0.8311 | 0.8269 | 0.8339 | 0.8281 | |
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| 0.1613 | 12.2481 | 790 | 0.5682 | 0.8382 | 0.8344 | 0.8397 | 0.8356 | |
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| 0.1665 | 12.4031 | 800 | 0.5565 | 0.8350 | 0.8325 | 0.8365 | 0.8325 | |
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| 0.1739 | 12.5581 | 810 | 0.5738 | 0.8360 | 0.8328 | 0.8395 | 0.8326 | |
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| 0.1744 | 12.7132 | 820 | 0.5628 | 0.8360 | 0.8327 | 0.8387 | 0.8328 | |
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| 0.1737 | 12.8682 | 830 | 0.5712 | 0.8355 | 0.8320 | 0.8395 | 0.8324 | |
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| 0.1635 | 13.0233 | 840 | 0.5745 | 0.8309 | 0.8256 | 0.8328 | 0.8269 | |
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| 0.1689 | 13.1783 | 850 | 0.5781 | 0.8326 | 0.8288 | 0.8358 | 0.8294 | |
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| 0.1611 | 13.3333 | 860 | 0.5740 | 0.8328 | 0.8280 | 0.8349 | 0.8289 | |
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| 0.1624 | 13.4884 | 870 | 0.5656 | 0.8324 | 0.8279 | 0.8328 | 0.8287 | |
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| 0.1635 | 13.6434 | 880 | 0.5618 | 0.8319 | 0.8276 | 0.8328 | 0.8280 | |
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| 0.1395 | 13.7984 | 890 | 0.5648 | 0.8350 | 0.8311 | 0.8368 | 0.8312 | |
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| 0.1489 | 13.9535 | 900 | 0.5666 | 0.8341 | 0.8304 | 0.8370 | 0.8304 | |
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| 0.1174 | 14.1085 | 910 | 0.5700 | 0.8358 | 0.8321 | 0.8400 | 0.8320 | |
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| 0.1274 | 14.2636 | 920 | 0.5720 | 0.8331 | 0.8295 | 0.8366 | 0.8295 | |
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| 0.134 | 14.4186 | 930 | 0.5657 | 0.8353 | 0.8311 | 0.8369 | 0.8317 | |
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| 0.1327 | 14.5736 | 940 | 0.5662 | 0.8343 | 0.8308 | 0.8367 | 0.8307 | |
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| 0.1165 | 14.7287 | 950 | 0.5654 | 0.8341 | 0.8301 | 0.8355 | 0.8303 | |
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| 0.1277 | 14.8837 | 960 | 0.5661 | 0.8345 | 0.8308 | 0.8360 | 0.8310 | |
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| 0.1221 | 15.0388 | 970 | 0.5615 | 0.8370 | 0.8335 | 0.8388 | 0.8335 | |
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| 0.1194 | 15.1938 | 980 | 0.5632 | 0.8353 | 0.8318 | 0.8369 | 0.8319 | |
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| 0.1126 | 15.3488 | 990 | 0.5616 | 0.8362 | 0.8326 | 0.8376 | 0.8327 | |
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| 0.1256 | 15.5039 | 1000 | 0.5612 | 0.8365 | 0.8329 | 0.8377 | 0.8331 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.3.0 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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