End of training
Browse files- README.md +191 -0
- config.json +261 -0
- config.toml +27 -0
- model.safetensors +3 -0
- preprocessor_config.json +37 -0
- train.ipynb +0 -0
- training_args.bin +3 -0
README.md
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| 1 |
+
---
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| 2 |
+
license: apache-2.0
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| 3 |
+
base_model: google/siglip-base-patch16-224
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| 4 |
+
tags:
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| 5 |
+
- generated_from_trainer
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| 6 |
+
datasets:
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- stanford-dogs
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| 8 |
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metrics:
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| 9 |
+
- accuracy
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| 10 |
+
- f1
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| 11 |
+
- precision
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| 12 |
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- recall
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| 13 |
+
model-index:
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| 14 |
+
- name: google-siglip-base-patch16-224-batch32-lr0.005-standford-dogs
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| 15 |
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results:
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| 16 |
+
- task:
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| 17 |
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name: Image Classification
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| 18 |
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type: image-classification
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| 19 |
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dataset:
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name: stanford-dogs
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| 21 |
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type: stanford-dogs
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config: default
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| 23 |
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split: full
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| 24 |
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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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| 28 |
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value: 0.8406219630709426
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| 29 |
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- name: F1
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| 30 |
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type: f1
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| 31 |
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value: 0.8370399997829452
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| 32 |
+
- name: Precision
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| 33 |
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type: precision
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| 34 |
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value: 0.8413608553082093
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| 35 |
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- name: Recall
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| 36 |
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type: recall
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| 37 |
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value: 0.837163245644009
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| 38 |
+
---
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| 39 |
+
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| 40 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| 41 |
+
should probably proofread and complete it, then remove this comment. -->
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| 42 |
+
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| 43 |
+
# google-siglip-base-patch16-224-batch32-lr0.005-standford-dogs
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| 44 |
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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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| 46 |
+
It achieves the following results on the evaluation set:
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| 47 |
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- Loss: 0.5144
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| 48 |
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- Accuracy: 0.8406
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| 49 |
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- F1: 0.8370
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| 50 |
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- Precision: 0.8414
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| 51 |
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- Recall: 0.8372
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| 52 |
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| 53 |
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## Model description
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| 54 |
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| 55 |
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More information needed
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| 56 |
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## Intended uses & limitations
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| 58 |
+
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| 59 |
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More information needed
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| 60 |
+
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| 61 |
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## Training and evaluation data
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| 62 |
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| 63 |
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More information needed
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| 64 |
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| 65 |
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## Training procedure
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| 66 |
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| 67 |
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### Training hyperparameters
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| 68 |
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| 69 |
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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| 71 |
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- train_batch_size: 32
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| 72 |
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- eval_batch_size: 32
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| 73 |
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- seed: 42
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| 74 |
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- gradient_accumulation_steps: 4
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| 75 |
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- total_train_batch_size: 128
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| 76 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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| 77 |
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- lr_scheduler_type: linear
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| 78 |
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- training_steps: 1000
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| 79 |
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| 80 |
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### Training results
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| 81 |
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| 82 |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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| 83 |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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| 84 |
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| 4.9299 | 0.0777 | 10 | 4.4830 | 0.0437 | 0.0194 | 0.0268 | 0.0406 |
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| 85 |
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| 4.4701 | 0.1553 | 20 | 4.1771 | 0.0897 | 0.0695 | 0.1215 | 0.0872 |
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| 86 |
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| 3.925 | 0.2330 | 30 | 3.1443 | 0.2087 | 0.1820 | 0.2840 | 0.2050 |
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| 87 |
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| 3.3549 | 0.3107 | 40 | 2.5232 | 0.3494 | 0.3224 | 0.4544 | 0.3458 |
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| 88 |
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| 2.8512 | 0.3883 | 50 | 2.1503 | 0.4332 | 0.3991 | 0.5244 | 0.4215 |
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| 89 |
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| 2.5846 | 0.4660 | 60 | 1.7744 | 0.5180 | 0.4863 | 0.6052 | 0.5127 |
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| 90 |
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| 2.2942 | 0.5437 | 70 | 1.5619 | 0.5437 | 0.5333 | 0.6446 | 0.5421 |
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| 91 |
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| 2.1577 | 0.6214 | 80 | 1.5739 | 0.5622 | 0.5452 | 0.6301 | 0.5600 |
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| 92 |
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| 1.9423 | 0.6990 | 90 | 1.2747 | 0.6198 | 0.6010 | 0.6840 | 0.6156 |
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| 93 |
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| 1.663 | 0.7767 | 100 | 1.3143 | 0.6115 | 0.6013 | 0.7101 | 0.6084 |
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| 94 |
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| 1.8093 | 0.8544 | 110 | 1.1125 | 0.6667 | 0.6491 | 0.7107 | 0.6618 |
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| 95 |
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| 1.6925 | 0.9320 | 120 | 1.3373 | 0.6110 | 0.5992 | 0.6918 | 0.6074 |
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| 96 |
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| 1.7028 | 1.0097 | 130 | 1.0352 | 0.6934 | 0.6810 | 0.7321 | 0.6878 |
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| 97 |
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| 1.4451 | 1.0874 | 140 | 0.9891 | 0.7000 | 0.6924 | 0.7438 | 0.6931 |
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| 98 |
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| 1.4583 | 1.1650 | 150 | 0.9574 | 0.7153 | 0.7045 | 0.7411 | 0.7120 |
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| 99 |
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| 1.4219 | 1.2427 | 160 | 0.9801 | 0.7024 | 0.6929 | 0.7473 | 0.6991 |
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| 100 |
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| 1.442 | 1.3204 | 170 | 1.0351 | 0.6829 | 0.6794 | 0.7294 | 0.6780 |
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| 101 |
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| 1.3218 | 1.3981 | 180 | 0.9178 | 0.7238 | 0.7196 | 0.7632 | 0.7227 |
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| 102 |
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| 1.2486 | 1.4757 | 190 | 0.8521 | 0.7415 | 0.7361 | 0.7669 | 0.7363 |
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| 103 |
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| 1.2239 | 1.5534 | 200 | 0.9499 | 0.7046 | 0.6979 | 0.7582 | 0.7027 |
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| 104 |
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| 1.2724 | 1.6311 | 210 | 0.9332 | 0.7157 | 0.7050 | 0.7604 | 0.7111 |
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| 105 |
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| 1.205 | 1.7087 | 220 | 0.8662 | 0.7391 | 0.7287 | 0.7770 | 0.7349 |
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| 106 |
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| 1.2263 | 1.7864 | 230 | 0.8464 | 0.7393 | 0.7302 | 0.7668 | 0.7343 |
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| 107 |
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| 1.1301 | 1.8641 | 240 | 0.8417 | 0.7374 | 0.7302 | 0.7795 | 0.7342 |
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| 108 |
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| 1.2035 | 1.9417 | 250 | 0.7798 | 0.7629 | 0.7553 | 0.7873 | 0.7600 |
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| 109 |
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| 1.1138 | 2.0194 | 260 | 0.8089 | 0.7495 | 0.7368 | 0.7789 | 0.7444 |
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| 110 |
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| 0.9266 | 2.0971 | 270 | 0.7645 | 0.7566 | 0.7539 | 0.7864 | 0.7550 |
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| 111 |
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| 0.9438 | 2.1748 | 280 | 0.7555 | 0.7653 | 0.7575 | 0.7916 | 0.7609 |
|
| 112 |
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| 0.9776 | 2.2524 | 290 | 0.7824 | 0.7544 | 0.7494 | 0.7787 | 0.7531 |
|
| 113 |
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| 0.9083 | 2.3301 | 300 | 0.7687 | 0.7626 | 0.7543 | 0.7914 | 0.7606 |
|
| 114 |
+
| 0.9157 | 2.4078 | 310 | 0.7573 | 0.7682 | 0.7634 | 0.7966 | 0.7637 |
|
| 115 |
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| 0.9962 | 2.4854 | 320 | 0.7704 | 0.7631 | 0.7539 | 0.7950 | 0.7600 |
|
| 116 |
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| 0.9313 | 2.5631 | 330 | 0.7552 | 0.7609 | 0.7560 | 0.7879 | 0.7564 |
|
| 117 |
+
| 0.8893 | 2.6408 | 340 | 0.7491 | 0.7655 | 0.7568 | 0.7846 | 0.7621 |
|
| 118 |
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| 0.9724 | 2.7184 | 350 | 0.7167 | 0.7787 | 0.7721 | 0.7976 | 0.7738 |
|
| 119 |
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| 0.9045 | 2.7961 | 360 | 0.7067 | 0.7799 | 0.7728 | 0.8082 | 0.7739 |
|
| 120 |
+
| 0.8922 | 2.8738 | 370 | 0.7028 | 0.7799 | 0.7696 | 0.8017 | 0.7745 |
|
| 121 |
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| 0.9082 | 2.9515 | 380 | 0.6934 | 0.7767 | 0.7716 | 0.7892 | 0.7736 |
|
| 122 |
+
| 0.8438 | 3.0291 | 390 | 0.6680 | 0.7869 | 0.7795 | 0.8068 | 0.7814 |
|
| 123 |
+
| 0.7603 | 3.1068 | 400 | 0.6601 | 0.7918 | 0.7837 | 0.8060 | 0.7871 |
|
| 124 |
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| 0.6695 | 3.1845 | 410 | 0.6628 | 0.7954 | 0.7881 | 0.8113 | 0.7927 |
|
| 125 |
+
| 0.7315 | 3.2621 | 420 | 0.6564 | 0.7983 | 0.7908 | 0.8113 | 0.7942 |
|
| 126 |
+
| 0.7155 | 3.3398 | 430 | 0.6562 | 0.7906 | 0.7876 | 0.8119 | 0.7866 |
|
| 127 |
+
| 0.7216 | 3.4175 | 440 | 0.6499 | 0.7881 | 0.7828 | 0.8048 | 0.7850 |
|
| 128 |
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| 0.7167 | 3.4951 | 450 | 0.6440 | 0.7942 | 0.7890 | 0.8073 | 0.7928 |
|
| 129 |
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| 0.6772 | 3.5728 | 460 | 0.6147 | 0.8064 | 0.8014 | 0.8161 | 0.8021 |
|
| 130 |
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| 0.7298 | 3.6505 | 470 | 0.6643 | 0.7974 | 0.7942 | 0.8172 | 0.7928 |
|
| 131 |
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| 0.6712 | 3.7282 | 480 | 0.6114 | 0.8071 | 0.8032 | 0.8182 | 0.8030 |
|
| 132 |
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| 0.703 | 3.8058 | 490 | 0.6246 | 0.8069 | 0.8011 | 0.8188 | 0.8027 |
|
| 133 |
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| 0.724 | 3.8835 | 500 | 0.6386 | 0.8022 | 0.7958 | 0.8167 | 0.7980 |
|
| 134 |
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| 0.6467 | 3.9612 | 510 | 0.6490 | 0.8044 | 0.7981 | 0.8154 | 0.8002 |
|
| 135 |
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| 0.6781 | 4.0388 | 520 | 0.6296 | 0.8078 | 0.8037 | 0.8208 | 0.8055 |
|
| 136 |
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| 0.5615 | 4.1165 | 530 | 0.6108 | 0.8117 | 0.8075 | 0.8196 | 0.8091 |
|
| 137 |
+
| 0.5095 | 4.1942 | 540 | 0.6272 | 0.8090 | 0.8045 | 0.8228 | 0.8042 |
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| 138 |
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| 0.562 | 4.2718 | 550 | 0.6529 | 0.8008 | 0.7966 | 0.8197 | 0.7965 |
|
| 139 |
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| 0.5307 | 4.3495 | 560 | 0.6290 | 0.8071 | 0.8036 | 0.8164 | 0.8044 |
|
| 140 |
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| 0.5428 | 4.4272 | 570 | 0.6033 | 0.8154 | 0.8097 | 0.8221 | 0.8115 |
|
| 141 |
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| 0.5248 | 4.5049 | 580 | 0.6166 | 0.8141 | 0.8035 | 0.8228 | 0.8094 |
|
| 142 |
+
| 0.5608 | 4.5825 | 590 | 0.6010 | 0.8127 | 0.8061 | 0.8243 | 0.8078 |
|
| 143 |
+
| 0.5151 | 4.6602 | 600 | 0.6155 | 0.8061 | 0.8029 | 0.8231 | 0.8024 |
|
| 144 |
+
| 0.5712 | 4.7379 | 610 | 0.6015 | 0.8134 | 0.8091 | 0.8208 | 0.8093 |
|
| 145 |
+
| 0.564 | 4.8155 | 620 | 0.5740 | 0.8265 | 0.8217 | 0.8344 | 0.8222 |
|
| 146 |
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| 0.5198 | 4.8932 | 630 | 0.5693 | 0.8239 | 0.8165 | 0.8321 | 0.8186 |
|
| 147 |
+
| 0.4851 | 4.9709 | 640 | 0.5574 | 0.8273 | 0.8225 | 0.8315 | 0.8242 |
|
| 148 |
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| 0.4428 | 5.0485 | 650 | 0.5711 | 0.8253 | 0.8187 | 0.8331 | 0.8199 |
|
| 149 |
+
| 0.3877 | 5.1262 | 660 | 0.5714 | 0.8234 | 0.8198 | 0.8277 | 0.8210 |
|
| 150 |
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| 0.4377 | 5.2039 | 670 | 0.5736 | 0.8229 | 0.8168 | 0.8301 | 0.8196 |
|
| 151 |
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| 0.4056 | 5.2816 | 680 | 0.5670 | 0.8260 | 0.8228 | 0.8365 | 0.8232 |
|
| 152 |
+
| 0.4679 | 5.3592 | 690 | 0.5549 | 0.8297 | 0.8247 | 0.8344 | 0.8267 |
|
| 153 |
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| 0.3742 | 5.4369 | 700 | 0.5582 | 0.8246 | 0.8188 | 0.8314 | 0.8210 |
|
| 154 |
+
| 0.4215 | 5.5146 | 710 | 0.5588 | 0.8246 | 0.8211 | 0.8305 | 0.8209 |
|
| 155 |
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| 0.4136 | 5.5922 | 720 | 0.5594 | 0.8202 | 0.8153 | 0.8257 | 0.8160 |
|
| 156 |
+
| 0.4464 | 5.6699 | 730 | 0.5541 | 0.8258 | 0.8216 | 0.8307 | 0.8223 |
|
| 157 |
+
| 0.4684 | 5.7476 | 740 | 0.5477 | 0.8275 | 0.8241 | 0.8326 | 0.8238 |
|
| 158 |
+
| 0.4094 | 5.8252 | 750 | 0.5436 | 0.8287 | 0.8247 | 0.8341 | 0.8257 |
|
| 159 |
+
| 0.3757 | 5.9029 | 760 | 0.5514 | 0.8307 | 0.8258 | 0.8355 | 0.8275 |
|
| 160 |
+
| 0.388 | 5.9806 | 770 | 0.5523 | 0.8285 | 0.8244 | 0.8320 | 0.8254 |
|
| 161 |
+
| 0.3528 | 6.0583 | 780 | 0.5500 | 0.8246 | 0.8195 | 0.8291 | 0.8206 |
|
| 162 |
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| 0.3466 | 6.1359 | 790 | 0.5358 | 0.8311 | 0.8277 | 0.8335 | 0.8276 |
|
| 163 |
+
| 0.3149 | 6.2136 | 800 | 0.5389 | 0.8326 | 0.8283 | 0.8368 | 0.8286 |
|
| 164 |
+
| 0.3106 | 6.2913 | 810 | 0.5277 | 0.8379 | 0.8344 | 0.8397 | 0.8342 |
|
| 165 |
+
| 0.3226 | 6.3689 | 820 | 0.5399 | 0.8304 | 0.8266 | 0.8339 | 0.8268 |
|
| 166 |
+
| 0.3578 | 6.4466 | 830 | 0.5370 | 0.8328 | 0.8287 | 0.8369 | 0.8287 |
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| 167 |
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| 0.348 | 6.5243 | 840 | 0.5371 | 0.8307 | 0.8276 | 0.8340 | 0.8275 |
|
| 168 |
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| 0.3228 | 6.6019 | 850 | 0.5319 | 0.8338 | 0.8295 | 0.8371 | 0.8306 |
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| 169 |
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| 0.3022 | 6.6796 | 860 | 0.5332 | 0.8331 | 0.8295 | 0.8365 | 0.8295 |
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| 170 |
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| 0.3311 | 6.7573 | 870 | 0.5314 | 0.8307 | 0.8278 | 0.8329 | 0.8273 |
|
| 171 |
+
| 0.3221 | 6.8350 | 880 | 0.5267 | 0.8358 | 0.8324 | 0.8379 | 0.8322 |
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| 172 |
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| 0.3132 | 6.9126 | 890 | 0.5293 | 0.8336 | 0.8293 | 0.8363 | 0.8295 |
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| 173 |
+
| 0.2821 | 6.9903 | 900 | 0.5279 | 0.8314 | 0.8272 | 0.8342 | 0.8277 |
|
| 174 |
+
| 0.2486 | 7.0680 | 910 | 0.5286 | 0.8326 | 0.8287 | 0.8339 | 0.8291 |
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| 175 |
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| 0.2936 | 7.1456 | 920 | 0.5250 | 0.8370 | 0.8330 | 0.8380 | 0.8336 |
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| 176 |
+
| 0.292 | 7.2233 | 930 | 0.5205 | 0.8392 | 0.8351 | 0.8408 | 0.8353 |
|
| 177 |
+
| 0.2806 | 7.3010 | 940 | 0.5207 | 0.8387 | 0.8349 | 0.8403 | 0.8352 |
|
| 178 |
+
| 0.2406 | 7.3786 | 950 | 0.5148 | 0.8404 | 0.8364 | 0.8406 | 0.8369 |
|
| 179 |
+
| 0.2941 | 7.4563 | 960 | 0.5145 | 0.8404 | 0.8372 | 0.8419 | 0.8372 |
|
| 180 |
+
| 0.2597 | 7.5340 | 970 | 0.5156 | 0.8394 | 0.8358 | 0.8401 | 0.8359 |
|
| 181 |
+
| 0.2534 | 7.6117 | 980 | 0.5157 | 0.8404 | 0.8368 | 0.8410 | 0.8369 |
|
| 182 |
+
| 0.2487 | 7.6893 | 990 | 0.5150 | 0.8401 | 0.8364 | 0.8408 | 0.8366 |
|
| 183 |
+
| 0.2618 | 7.7670 | 1000 | 0.5144 | 0.8406 | 0.8370 | 0.8414 | 0.8372 |
|
| 184 |
+
|
| 185 |
+
|
| 186 |
+
### Framework versions
|
| 187 |
+
|
| 188 |
+
- Transformers 4.40.2
|
| 189 |
+
- Pytorch 2.3.0
|
| 190 |
+
- Datasets 2.19.1
|
| 191 |
+
- Tokenizers 0.19.1
|
config.json
ADDED
|
@@ -0,0 +1,261 @@
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|
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|
|
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|
|
|
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|
|
|
|
|
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|
|
|
|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
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|
|
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|
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|
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|
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|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "google/siglip-base-patch16-224",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"SiglipForImageClassification"
|
| 5 |
+
],
|
| 6 |
+
"id2label": {
|
| 7 |
+
"0": "Affenpinscher",
|
| 8 |
+
"1": "Afghan Hound",
|
| 9 |
+
"2": "African Hunting Dog",
|
| 10 |
+
"3": "Airedale",
|
| 11 |
+
"4": "American Staffordshire Terrier",
|
| 12 |
+
"5": "Appenzeller",
|
| 13 |
+
"6": "Australian Terrier",
|
| 14 |
+
"7": "Basenji",
|
| 15 |
+
"8": "Basset",
|
| 16 |
+
"9": "Beagle",
|
| 17 |
+
"10": "Bedlington Terrier",
|
| 18 |
+
"11": "Bernese Mountain Dog",
|
| 19 |
+
"12": "Black And Tan Coonhound",
|
| 20 |
+
"13": "Blenheim Spaniel",
|
| 21 |
+
"14": "Bloodhound",
|
| 22 |
+
"15": "Bluetick",
|
| 23 |
+
"16": "Border Collie",
|
| 24 |
+
"17": "Border Terrier",
|
| 25 |
+
"18": "Borzoi",
|
| 26 |
+
"19": "Boston Bull",
|
| 27 |
+
"20": "Bouvier Des Flandres",
|
| 28 |
+
"21": "Boxer",
|
| 29 |
+
"22": "Brabancon Griffon",
|
| 30 |
+
"23": "Briard",
|
| 31 |
+
"24": "Brittany Spaniel",
|
| 32 |
+
"25": "Bull Mastiff",
|
| 33 |
+
"26": "Cairn",
|
| 34 |
+
"27": "Cardigan",
|
| 35 |
+
"28": "Chesapeake Bay Retriever",
|
| 36 |
+
"29": "Chihuahua",
|
| 37 |
+
"30": "Chow",
|
| 38 |
+
"31": "Clumber",
|
| 39 |
+
"32": "Cocker Spaniel",
|
| 40 |
+
"33": "Collie",
|
| 41 |
+
"34": "Curly Coated Retriever",
|
| 42 |
+
"35": "Dandie Dinmont",
|
| 43 |
+
"36": "Dhole",
|
| 44 |
+
"37": "Dingo",
|
| 45 |
+
"38": "Doberman",
|
| 46 |
+
"39": "English Foxhound",
|
| 47 |
+
"40": "English Setter",
|
| 48 |
+
"41": "English Springer",
|
| 49 |
+
"42": "Entlebucher",
|
| 50 |
+
"43": "Eskimo Dog",
|
| 51 |
+
"44": "Flat Coated Retriever",
|
| 52 |
+
"45": "French Bulldog",
|
| 53 |
+
"46": "German Shepherd",
|
| 54 |
+
"47": "German Short Haired Pointer",
|
| 55 |
+
"48": "Giant Schnauzer",
|
| 56 |
+
"49": "Golden Retriever",
|
| 57 |
+
"50": "Gordon Setter",
|
| 58 |
+
"51": "Great Dane",
|
| 59 |
+
"52": "Great Pyrenees",
|
| 60 |
+
"53": "Greater Swiss Mountain Dog",
|
| 61 |
+
"54": "Groenendael",
|
| 62 |
+
"55": "Ibizan Hound",
|
| 63 |
+
"56": "Irish Setter",
|
| 64 |
+
"57": "Irish Terrier",
|
| 65 |
+
"58": "Irish Water Spaniel",
|
| 66 |
+
"59": "Irish Wolfhound",
|
| 67 |
+
"60": "Italian Greyhound",
|
| 68 |
+
"61": "Japanese Spaniel",
|
| 69 |
+
"62": "Keeshond",
|
| 70 |
+
"63": "Kelpie",
|
| 71 |
+
"64": "Kerry Blue Terrier",
|
| 72 |
+
"65": "Komondor",
|
| 73 |
+
"66": "Kuvasz",
|
| 74 |
+
"67": "Labrador Retriever",
|
| 75 |
+
"68": "Lakeland Terrier",
|
| 76 |
+
"69": "Leonberg",
|
| 77 |
+
"70": "Lhasa",
|
| 78 |
+
"71": "Malamute",
|
| 79 |
+
"72": "Malinois",
|
| 80 |
+
"73": "Maltese Dog",
|
| 81 |
+
"74": "Mexican Hairless",
|
| 82 |
+
"75": "Miniature Pinscher",
|
| 83 |
+
"76": "Miniature Poodle",
|
| 84 |
+
"77": "Miniature Schnauzer",
|
| 85 |
+
"78": "Newfoundland",
|
| 86 |
+
"79": "Norfolk Terrier",
|
| 87 |
+
"80": "Norwegian Elkhound",
|
| 88 |
+
"81": "Norwich Terrier",
|
| 89 |
+
"82": "Old English Sheepdog",
|
| 90 |
+
"83": "Otterhound",
|
| 91 |
+
"84": "Papillon",
|
| 92 |
+
"85": "Pekinese",
|
| 93 |
+
"86": "Pembroke",
|
| 94 |
+
"87": "Pomeranian",
|
| 95 |
+
"88": "Pug",
|
| 96 |
+
"89": "Redbone",
|
| 97 |
+
"90": "Rhodesian Ridgeback",
|
| 98 |
+
"91": "Rottweiler",
|
| 99 |
+
"92": "Saint Bernard",
|
| 100 |
+
"93": "Saluki",
|
| 101 |
+
"94": "Samoyed",
|
| 102 |
+
"95": "Schipperke",
|
| 103 |
+
"96": "Scotch Terrier",
|
| 104 |
+
"97": "Scottish Deerhound",
|
| 105 |
+
"98": "Sealyham Terrier",
|
| 106 |
+
"99": "Shetland Sheepdog",
|
| 107 |
+
"100": "Shih Tzu",
|
| 108 |
+
"101": "Siberian Husky",
|
| 109 |
+
"102": "Silky Terrier",
|
| 110 |
+
"103": "Soft Coated Wheaten Terrier",
|
| 111 |
+
"104": "Staffordshire Bullterrier",
|
| 112 |
+
"105": "Standard Poodle",
|
| 113 |
+
"106": "Standard Schnauzer",
|
| 114 |
+
"107": "Sussex Spaniel",
|
| 115 |
+
"108": "Tibetan Mastiff",
|
| 116 |
+
"109": "Tibetan Terrier",
|
| 117 |
+
"110": "Toy Poodle",
|
| 118 |
+
"111": "Toy Terrier",
|
| 119 |
+
"112": "Vizsla",
|
| 120 |
+
"113": "Walker Hound",
|
| 121 |
+
"114": "Weimaraner",
|
| 122 |
+
"115": "Welsh Springer Spaniel",
|
| 123 |
+
"116": "West Highland White Terrier",
|
| 124 |
+
"117": "Whippet",
|
| 125 |
+
"118": "Wire Haired Fox Terrier",
|
| 126 |
+
"119": "Yorkshire Terrier"
|
| 127 |
+
},
|
| 128 |
+
"initializer_factor": 1.0,
|
| 129 |
+
"label2id": {
|
| 130 |
+
"Affenpinscher": 0,
|
| 131 |
+
"Afghan Hound": 1,
|
| 132 |
+
"African Hunting Dog": 2,
|
| 133 |
+
"Airedale": 3,
|
| 134 |
+
"American Staffordshire Terrier": 4,
|
| 135 |
+
"Appenzeller": 5,
|
| 136 |
+
"Australian Terrier": 6,
|
| 137 |
+
"Basenji": 7,
|
| 138 |
+
"Basset": 8,
|
| 139 |
+
"Beagle": 9,
|
| 140 |
+
"Bedlington Terrier": 10,
|
| 141 |
+
"Bernese Mountain Dog": 11,
|
| 142 |
+
"Black And Tan Coonhound": 12,
|
| 143 |
+
"Blenheim Spaniel": 13,
|
| 144 |
+
"Bloodhound": 14,
|
| 145 |
+
"Bluetick": 15,
|
| 146 |
+
"Border Collie": 16,
|
| 147 |
+
"Border Terrier": 17,
|
| 148 |
+
"Borzoi": 18,
|
| 149 |
+
"Boston Bull": 19,
|
| 150 |
+
"Bouvier Des Flandres": 20,
|
| 151 |
+
"Boxer": 21,
|
| 152 |
+
"Brabancon Griffon": 22,
|
| 153 |
+
"Briard": 23,
|
| 154 |
+
"Brittany Spaniel": 24,
|
| 155 |
+
"Bull Mastiff": 25,
|
| 156 |
+
"Cairn": 26,
|
| 157 |
+
"Cardigan": 27,
|
| 158 |
+
"Chesapeake Bay Retriever": 28,
|
| 159 |
+
"Chihuahua": 29,
|
| 160 |
+
"Chow": 30,
|
| 161 |
+
"Clumber": 31,
|
| 162 |
+
"Cocker Spaniel": 32,
|
| 163 |
+
"Collie": 33,
|
| 164 |
+
"Curly Coated Retriever": 34,
|
| 165 |
+
"Dandie Dinmont": 35,
|
| 166 |
+
"Dhole": 36,
|
| 167 |
+
"Dingo": 37,
|
| 168 |
+
"Doberman": 38,
|
| 169 |
+
"English Foxhound": 39,
|
| 170 |
+
"English Setter": 40,
|
| 171 |
+
"English Springer": 41,
|
| 172 |
+
"Entlebucher": 42,
|
| 173 |
+
"Eskimo Dog": 43,
|
| 174 |
+
"Flat Coated Retriever": 44,
|
| 175 |
+
"French Bulldog": 45,
|
| 176 |
+
"German Shepherd": 46,
|
| 177 |
+
"German Short Haired Pointer": 47,
|
| 178 |
+
"Giant Schnauzer": 48,
|
| 179 |
+
"Golden Retriever": 49,
|
| 180 |
+
"Gordon Setter": 50,
|
| 181 |
+
"Great Dane": 51,
|
| 182 |
+
"Great Pyrenees": 52,
|
| 183 |
+
"Greater Swiss Mountain Dog": 53,
|
| 184 |
+
"Groenendael": 54,
|
| 185 |
+
"Ibizan Hound": 55,
|
| 186 |
+
"Irish Setter": 56,
|
| 187 |
+
"Irish Terrier": 57,
|
| 188 |
+
"Irish Water Spaniel": 58,
|
| 189 |
+
"Irish Wolfhound": 59,
|
| 190 |
+
"Italian Greyhound": 60,
|
| 191 |
+
"Japanese Spaniel": 61,
|
| 192 |
+
"Keeshond": 62,
|
| 193 |
+
"Kelpie": 63,
|
| 194 |
+
"Kerry Blue Terrier": 64,
|
| 195 |
+
"Komondor": 65,
|
| 196 |
+
"Kuvasz": 66,
|
| 197 |
+
"Labrador Retriever": 67,
|
| 198 |
+
"Lakeland Terrier": 68,
|
| 199 |
+
"Leonberg": 69,
|
| 200 |
+
"Lhasa": 70,
|
| 201 |
+
"Malamute": 71,
|
| 202 |
+
"Malinois": 72,
|
| 203 |
+
"Maltese Dog": 73,
|
| 204 |
+
"Mexican Hairless": 74,
|
| 205 |
+
"Miniature Pinscher": 75,
|
| 206 |
+
"Miniature Poodle": 76,
|
| 207 |
+
"Miniature Schnauzer": 77,
|
| 208 |
+
"Newfoundland": 78,
|
| 209 |
+
"Norfolk Terrier": 79,
|
| 210 |
+
"Norwegian Elkhound": 80,
|
| 211 |
+
"Norwich Terrier": 81,
|
| 212 |
+
"Old English Sheepdog": 82,
|
| 213 |
+
"Otterhound": 83,
|
| 214 |
+
"Papillon": 84,
|
| 215 |
+
"Pekinese": 85,
|
| 216 |
+
"Pembroke": 86,
|
| 217 |
+
"Pomeranian": 87,
|
| 218 |
+
"Pug": 88,
|
| 219 |
+
"Redbone": 89,
|
| 220 |
+
"Rhodesian Ridgeback": 90,
|
| 221 |
+
"Rottweiler": 91,
|
| 222 |
+
"Saint Bernard": 92,
|
| 223 |
+
"Saluki": 93,
|
| 224 |
+
"Samoyed": 94,
|
| 225 |
+
"Schipperke": 95,
|
| 226 |
+
"Scotch Terrier": 96,
|
| 227 |
+
"Scottish Deerhound": 97,
|
| 228 |
+
"Sealyham Terrier": 98,
|
| 229 |
+
"Shetland Sheepdog": 99,
|
| 230 |
+
"Shih Tzu": 100,
|
| 231 |
+
"Siberian Husky": 101,
|
| 232 |
+
"Silky Terrier": 102,
|
| 233 |
+
"Soft Coated Wheaten Terrier": 103,
|
| 234 |
+
"Staffordshire Bullterrier": 104,
|
| 235 |
+
"Standard Poodle": 105,
|
| 236 |
+
"Standard Schnauzer": 106,
|
| 237 |
+
"Sussex Spaniel": 107,
|
| 238 |
+
"Tibetan Mastiff": 108,
|
| 239 |
+
"Tibetan Terrier": 109,
|
| 240 |
+
"Toy Poodle": 110,
|
| 241 |
+
"Toy Terrier": 111,
|
| 242 |
+
"Vizsla": 112,
|
| 243 |
+
"Walker Hound": 113,
|
| 244 |
+
"Weimaraner": 114,
|
| 245 |
+
"Welsh Springer Spaniel": 115,
|
| 246 |
+
"West Highland White Terrier": 116,
|
| 247 |
+
"Whippet": 117,
|
| 248 |
+
"Wire Haired Fox Terrier": 118,
|
| 249 |
+
"Yorkshire Terrier": 119
|
| 250 |
+
},
|
| 251 |
+
"model_type": "siglip",
|
| 252 |
+
"problem_type": "single_label_classification",
|
| 253 |
+
"text_config": {
|
| 254 |
+
"model_type": "siglip_text_model"
|
| 255 |
+
},
|
| 256 |
+
"torch_dtype": "float32",
|
| 257 |
+
"transformers_version": "4.40.2",
|
| 258 |
+
"vision_config": {
|
| 259 |
+
"model_type": "siglip_vision_model"
|
| 260 |
+
}
|
| 261 |
+
}
|
config.toml
ADDED
|
@@ -0,0 +1,27 @@
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|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
| 1 |
+
[training_args]
|
| 2 |
+
output_dir="/Users/andrewmayes/Openclassroom/CanineNet/code/"
|
| 3 |
+
evaluation_strategy="steps"
|
| 4 |
+
save_strategy="steps"
|
| 5 |
+
learning_rate=5e-5
|
| 6 |
+
#per_device_train_batch_size=32 # 512
|
| 7 |
+
#per_device_eval_batch_size=32 # 512
|
| 8 |
+
# num_train_epochs=5,
|
| 9 |
+
eval_delay=0 # 50
|
| 10 |
+
eval_steps=0.01
|
| 11 |
+
#eval_accumulation_steps
|
| 12 |
+
gradient_accumulation_steps=4
|
| 13 |
+
gradient_checkpointing=true
|
| 14 |
+
optim="adafactor"
|
| 15 |
+
max_steps=1000 # 100
|
| 16 |
+
#logging_dir=""
|
| 17 |
+
#log_level="error"
|
| 18 |
+
load_best_model_at_end=true
|
| 19 |
+
metric_for_best_model="f1"
|
| 20 |
+
greater_is_better=true
|
| 21 |
+
#use_mps_device=true
|
| 22 |
+
logging_steps=0.01
|
| 23 |
+
save_steps=0.01
|
| 24 |
+
#auto_find_batch_size=true
|
| 25 |
+
report_to="mlflow"
|
| 26 |
+
save_total_limit=2
|
| 27 |
+
#hub_model_id="amaye15/SwinV2-Base-Document-Classifier"
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:dad239e61801b79bf3b82e4ed993efc8c403be2dadcfeeef45396800c1012393
|
| 3 |
+
size 371930976
|
preprocessor_config.json
ADDED
|
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_valid_processor_keys": [
|
| 3 |
+
"images",
|
| 4 |
+
"do_resize",
|
| 5 |
+
"size",
|
| 6 |
+
"resample",
|
| 7 |
+
"do_rescale",
|
| 8 |
+
"rescale_factor",
|
| 9 |
+
"do_normalize",
|
| 10 |
+
"image_mean",
|
| 11 |
+
"image_std",
|
| 12 |
+
"return_tensors",
|
| 13 |
+
"data_format",
|
| 14 |
+
"input_data_format"
|
| 15 |
+
],
|
| 16 |
+
"do_normalize": true,
|
| 17 |
+
"do_rescale": true,
|
| 18 |
+
"do_resize": true,
|
| 19 |
+
"image_mean": [
|
| 20 |
+
0.5,
|
| 21 |
+
0.5,
|
| 22 |
+
0.5
|
| 23 |
+
],
|
| 24 |
+
"image_processor_type": "SiglipImageProcessor",
|
| 25 |
+
"image_std": [
|
| 26 |
+
0.5,
|
| 27 |
+
0.5,
|
| 28 |
+
0.5
|
| 29 |
+
],
|
| 30 |
+
"processor_class": "SiglipProcessor",
|
| 31 |
+
"resample": 3,
|
| 32 |
+
"rescale_factor": 0.00392156862745098,
|
| 33 |
+
"size": {
|
| 34 |
+
"height": 224,
|
| 35 |
+
"width": 224
|
| 36 |
+
}
|
| 37 |
+
}
|
train.ipynb
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:24f1016240c1d1936589d86a7277d429cefa1660c376cc392f942fd63cc41c46
|
| 3 |
+
size 5112
|