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vit-base-patch32-224-in21k-finetuned-galaxy10-decals

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  2. model.safetensors +1 -1
README.md CHANGED
@@ -2,8 +2,6 @@
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  license: apache-2.0
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  base_model: google/vit-base-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
@@ -20,13 +18,13 @@ should probably proofread and complete it, then remove this comment. -->
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  # vit-base-patch32-224-in21k-finetuned-galaxy10-decals
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- This model is a fine-tuned version of [google/vit-base-patch32-224-in21k](https://huggingface.co/google/vit-base-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: 0.5180
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- - Accuracy: 0.8382
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- - Precision: 0.8363
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- - Recall: 0.8382
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- - F1: 0.8346
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  ## Model description
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@@ -46,11 +44,11 @@ More information needed
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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: 32
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- - eval_batch_size: 32
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  - seed: 42
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  - gradient_accumulation_steps: 4
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- - total_train_batch_size: 128
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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
@@ -60,36 +58,36 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | 1.4731 | 0.99 | 124 | 1.3850 | 0.6110 | 0.5791 | 0.6110 | 0.5797 |
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- | 0.9858 | 2.0 | 249 | 0.8900 | 0.7508 | 0.7578 | 0.7508 | 0.7337 |
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- | 0.9475 | 3.0 | 374 | 0.7799 | 0.7599 | 0.7667 | 0.7599 | 0.7559 |
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- | 0.7778 | 4.0 | 499 | 0.6798 | 0.7779 | 0.7825 | 0.7779 | 0.7729 |
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- | 0.6831 | 4.99 | 623 | 0.6352 | 0.7914 | 0.7916 | 0.7914 | 0.7889 |
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- | 0.6953 | 6.0 | 748 | 0.5931 | 0.8044 | 0.8076 | 0.8044 | 0.8023 |
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- | 0.6725 | 7.0 | 873 | 0.7304 | 0.7537 | 0.7671 | 0.7537 | 0.7519 |
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- | 0.5648 | 8.0 | 998 | 0.6352 | 0.7909 | 0.7961 | 0.7909 | 0.7868 |
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- | 0.6127 | 8.99 | 1122 | 0.6087 | 0.7858 | 0.7879 | 0.7858 | 0.7820 |
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- | 0.529 | 10.0 | 1247 | 0.5827 | 0.8072 | 0.8074 | 0.8072 | 0.8041 |
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- | 0.5212 | 11.0 | 1372 | 0.5787 | 0.8179 | 0.8177 | 0.8179 | 0.8108 |
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- | 0.4665 | 12.0 | 1497 | 0.5597 | 0.8168 | 0.8213 | 0.8168 | 0.8134 |
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- | 0.5123 | 12.99 | 1621 | 0.5840 | 0.8044 | 0.8163 | 0.8044 | 0.8044 |
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- | 0.4918 | 14.0 | 1746 | 0.5592 | 0.8219 | 0.8221 | 0.8219 | 0.8195 |
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- | 0.4733 | 15.0 | 1871 | 0.5180 | 0.8382 | 0.8363 | 0.8382 | 0.8346 |
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- | 0.4552 | 16.0 | 1996 | 0.5673 | 0.8174 | 0.8181 | 0.8174 | 0.8153 |
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- | 0.4004 | 16.99 | 2120 | 0.5711 | 0.8224 | 0.8239 | 0.8224 | 0.8199 |
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- | 0.3359 | 18.0 | 2245 | 0.5813 | 0.8168 | 0.8153 | 0.8168 | 0.8147 |
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- | 0.4069 | 19.0 | 2370 | 0.5482 | 0.8343 | 0.8352 | 0.8343 | 0.8307 |
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- | 0.3783 | 20.0 | 2495 | 0.5658 | 0.8179 | 0.8169 | 0.8179 | 0.8150 |
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- | 0.3293 | 20.99 | 2619 | 0.5647 | 0.8247 | 0.8234 | 0.8247 | 0.8230 |
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- | 0.3214 | 22.0 | 2744 | 0.5654 | 0.8309 | 0.8289 | 0.8309 | 0.8293 |
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- | 0.3285 | 23.0 | 2869 | 0.5943 | 0.8213 | 0.8226 | 0.8213 | 0.8201 |
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- | 0.2934 | 24.0 | 2994 | 0.5931 | 0.8264 | 0.8287 | 0.8264 | 0.8259 |
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- | 0.3051 | 24.99 | 3118 | 0.5788 | 0.8309 | 0.8325 | 0.8309 | 0.8303 |
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- | 0.2911 | 26.0 | 3243 | 0.5700 | 0.8377 | 0.8354 | 0.8377 | 0.8358 |
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- | 0.2893 | 27.0 | 3368 | 0.5971 | 0.8286 | 0.8320 | 0.8286 | 0.8291 |
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- | 0.2794 | 28.0 | 3493 | 0.5908 | 0.8315 | 0.8307 | 0.8315 | 0.8303 |
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- | 0.2506 | 28.99 | 3617 | 0.5914 | 0.8309 | 0.8314 | 0.8309 | 0.8306 |
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- | 0.2421 | 29.82 | 3720 | 0.5861 | 0.8365 | 0.8366 | 0.8365 | 0.8359 |
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  ### Framework versions
 
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  license: apache-2.0
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  base_model: google/vit-base-patch32-224-in21k
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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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  # vit-base-patch32-224-in21k-finetuned-galaxy10-decals
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+ This model is a fine-tuned version of [google/vit-base-patch32-224-in21k](https://huggingface.co/google/vit-base-patch32-224-in21k) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5087
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+ - Accuracy: 0.8388
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+ - Precision: 0.8389
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+ - Recall: 0.8388
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+ - F1: 0.8378
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  ## Model description
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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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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 2.0954 | 0.99 | 31 | 1.9514 | 0.3737 | 0.3322 | 0.3737 | 0.2452 |
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+ | 1.4835 | 1.98 | 62 | 1.3284 | 0.6184 | 0.6016 | 0.6184 | 0.5878 |
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+ | 1.1252 | 2.98 | 93 | 0.9771 | 0.7300 | 0.7400 | 0.7300 | 0.7004 |
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+ | 0.9605 | 4.0 | 125 | 0.8374 | 0.7570 | 0.7754 | 0.7570 | 0.7368 |
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+ | 0.8383 | 4.99 | 156 | 0.7286 | 0.7762 | 0.7728 | 0.7762 | 0.7650 |
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+ | 0.7665 | 5.98 | 187 | 0.7256 | 0.7689 | 0.7683 | 0.7689 | 0.7586 |
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+ | 0.7305 | 6.98 | 218 | 0.6640 | 0.7948 | 0.8031 | 0.7948 | 0.7966 |
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+ | 0.6689 | 8.0 | 250 | 0.6792 | 0.7807 | 0.7859 | 0.7807 | 0.7708 |
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+ | 0.6783 | 8.99 | 281 | 0.5985 | 0.8117 | 0.8076 | 0.8117 | 0.8071 |
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+ | 0.6225 | 9.98 | 312 | 0.6118 | 0.8050 | 0.8036 | 0.8050 | 0.8025 |
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+ | 0.6081 | 10.98 | 343 | 0.5966 | 0.8112 | 0.8108 | 0.8112 | 0.8080 |
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+ | 0.6028 | 12.0 | 375 | 0.5708 | 0.8202 | 0.8239 | 0.8202 | 0.8199 |
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+ | 0.6052 | 12.99 | 406 | 0.6035 | 0.8010 | 0.8116 | 0.8010 | 0.7982 |
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+ | 0.5553 | 13.98 | 437 | 0.5542 | 0.8196 | 0.8199 | 0.8196 | 0.8143 |
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+ | 0.5526 | 14.98 | 468 | 0.5385 | 0.8326 | 0.8346 | 0.8326 | 0.8317 |
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+ | 0.5199 | 16.0 | 500 | 0.5298 | 0.8219 | 0.8192 | 0.8219 | 0.8172 |
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+ | 0.4974 | 16.99 | 531 | 0.5291 | 0.8298 | 0.8306 | 0.8298 | 0.8260 |
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+ | 0.5015 | 17.98 | 562 | 0.5244 | 0.8275 | 0.8280 | 0.8275 | 0.8267 |
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+ | 0.4763 | 18.98 | 593 | 0.5190 | 0.8354 | 0.8357 | 0.8354 | 0.8316 |
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+ | 0.4763 | 20.0 | 625 | 0.5241 | 0.8264 | 0.8286 | 0.8264 | 0.8249 |
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+ | 0.4592 | 20.99 | 656 | 0.5061 | 0.8410 | 0.8439 | 0.8410 | 0.8406 |
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+ | 0.4414 | 21.98 | 687 | 0.5207 | 0.8269 | 0.8265 | 0.8269 | 0.8260 |
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+ | 0.4372 | 22.98 | 718 | 0.5342 | 0.8253 | 0.8283 | 0.8253 | 0.8254 |
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+ | 0.4118 | 24.0 | 750 | 0.5256 | 0.8275 | 0.8291 | 0.8275 | 0.8274 |
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+ | 0.4319 | 24.99 | 781 | 0.5055 | 0.8422 | 0.8413 | 0.8422 | 0.8406 |
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+ | 0.3807 | 25.98 | 812 | 0.5187 | 0.8377 | 0.8375 | 0.8377 | 0.8361 |
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+ | 0.4066 | 26.98 | 843 | 0.5203 | 0.8348 | 0.8333 | 0.8348 | 0.8326 |
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+ | 0.376 | 28.0 | 875 | 0.5128 | 0.8365 | 0.8361 | 0.8365 | 0.8348 |
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+ | 0.3992 | 28.99 | 906 | 0.5108 | 0.8377 | 0.8375 | 0.8377 | 0.8364 |
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+ | 0.3743 | 29.76 | 930 | 0.5087 | 0.8388 | 0.8389 | 0.8388 | 0.8378 |
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  ### Framework versions
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