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 +1298 -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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| 7 |
+
- stanford-dogs
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| 8 |
+
metrics:
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| 9 |
+
- accuracy
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| 10 |
+
- f1
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| 11 |
+
- precision
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| 12 |
+
- recall
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| 13 |
+
model-index:
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| 14 |
+
- name: google-siglip-base-patch16-224-batch64-lr5e-05-standford-dogs
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| 15 |
+
results:
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| 16 |
+
- task:
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| 17 |
+
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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| 22 |
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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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| 25 |
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metrics:
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| 26 |
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- name: Accuracy
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| 27 |
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type: accuracy
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| 28 |
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value: 0.8364917395529641
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| 29 |
+
- name: F1
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| 30 |
+
type: f1
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| 31 |
+
value: 0.8328749982143954
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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: 0.8377481660081763
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| 35 |
+
- name: Recall
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| 36 |
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type: recall
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| 37 |
+
value: 0.8330663170433035
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| 38 |
+
---
|
| 39 |
+
|
| 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 |
+
|
| 43 |
+
# google-siglip-base-patch16-224-batch64-lr5e-05-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 |
+
- Loss: 0.5612
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| 48 |
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- Accuracy: 0.8365
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| 49 |
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- F1: 0.8329
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| 50 |
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- Precision: 0.8377
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| 51 |
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- Recall: 0.8331
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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 |
+
More information needed
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| 56 |
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| 57 |
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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 |
+
- train_batch_size: 64
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| 72 |
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- eval_batch_size: 64
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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: 256
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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 |
+
|
| 82 |
+
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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| 83 |
+
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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| 84 |
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| 4.822 | 0.1550 | 10 | 4.2549 | 0.0782 | 0.0493 | 0.0987 | 0.0726 |
|
| 85 |
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| 4.236 | 0.3101 | 20 | 3.5279 | 0.1907 | 0.1507 | 0.2201 | 0.1830 |
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| 86 |
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| 3.5066 | 0.4651 | 30 | 2.5316 | 0.3319 | 0.2941 | 0.4180 | 0.3205 |
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| 87 |
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| 2.8064 | 0.6202 | 40 | 2.1243 | 0.4361 | 0.4090 | 0.5324 | 0.4282 |
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| 88 |
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| 2.441 | 0.7752 | 50 | 1.5798 | 0.5510 | 0.5250 | 0.6242 | 0.5438 |
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| 89 |
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| 2.0985 | 0.9302 | 60 | 1.4242 | 0.5843 | 0.5577 | 0.6400 | 0.5768 |
|
| 90 |
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| 1.8689 | 1.0853 | 70 | 1.1481 | 0.6625 | 0.6456 | 0.7143 | 0.6565 |
|
| 91 |
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| 1.6588 | 1.2403 | 80 | 1.1937 | 0.6465 | 0.6361 | 0.7062 | 0.6439 |
|
| 92 |
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| 1.5807 | 1.3953 | 90 | 0.9818 | 0.7058 | 0.6890 | 0.7438 | 0.6981 |
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| 93 |
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| 1.4851 | 1.5504 | 100 | 1.0181 | 0.7000 | 0.6839 | 0.7373 | 0.6959 |
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| 94 |
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| 1.5033 | 1.7054 | 110 | 1.0169 | 0.6914 | 0.6845 | 0.7490 | 0.6883 |
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| 95 |
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| 1.3022 | 1.8605 | 120 | 0.9087 | 0.7276 | 0.7170 | 0.7643 | 0.7222 |
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| 96 |
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| 1.3106 | 2.0155 | 130 | 0.8385 | 0.7432 | 0.7352 | 0.7667 | 0.7363 |
|
| 97 |
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| 1.1721 | 2.1705 | 140 | 0.8957 | 0.7128 | 0.7026 | 0.7592 | 0.7075 |
|
| 98 |
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| 1.131 | 2.3256 | 150 | 0.8730 | 0.7259 | 0.7149 | 0.7687 | 0.7196 |
|
| 99 |
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| 1.1223 | 2.4806 | 160 | 0.8132 | 0.7546 | 0.7457 | 0.7855 | 0.7482 |
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| 100 |
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| 1.0688 | 2.6357 | 170 | 0.7485 | 0.7704 | 0.7601 | 0.7863 | 0.7631 |
|
| 101 |
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| 1.0686 | 2.7907 | 180 | 0.7559 | 0.7651 | 0.7587 | 0.7920 | 0.7609 |
|
| 102 |
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| 0.9733 | 2.9457 | 190 | 0.7779 | 0.7553 | 0.7458 | 0.7797 | 0.7521 |
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| 103 |
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| 0.9287 | 3.1008 | 200 | 0.7048 | 0.7818 | 0.7756 | 0.7981 | 0.7756 |
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| 104 |
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| 0.8746 | 3.2558 | 210 | 0.6848 | 0.7867 | 0.7774 | 0.8034 | 0.7822 |
|
| 105 |
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| 0.7982 | 3.4109 | 220 | 0.6930 | 0.7884 | 0.7796 | 0.8025 | 0.7846 |
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| 106 |
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| 0.823 | 3.5659 | 230 | 0.7041 | 0.7804 | 0.7717 | 0.7975 | 0.7752 |
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| 107 |
+
| 0.8713 | 3.7209 | 240 | 0.7418 | 0.7755 | 0.7646 | 0.8053 | 0.7711 |
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| 108 |
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| 0.8651 | 3.8760 | 250 | 0.6847 | 0.7828 | 0.7773 | 0.8048 | 0.7782 |
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| 109 |
+
| 0.784 | 4.0310 | 260 | 0.6662 | 0.7923 | 0.7841 | 0.8097 | 0.7860 |
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| 110 |
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| 0.6894 | 4.1860 | 270 | 0.6980 | 0.7843 | 0.7781 | 0.8024 | 0.7779 |
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| 111 |
+
| 0.7727 | 4.3411 | 280 | 0.6629 | 0.7833 | 0.7804 | 0.8030 | 0.7798 |
|
| 112 |
+
| 0.6978 | 4.4961 | 290 | 0.6820 | 0.7845 | 0.7800 | 0.8011 | 0.7820 |
|
| 113 |
+
| 0.7032 | 4.6512 | 300 | 0.6148 | 0.8032 | 0.7969 | 0.8094 | 0.7985 |
|
| 114 |
+
| 0.6978 | 4.8062 | 310 | 0.6457 | 0.7940 | 0.7872 | 0.8085 | 0.7892 |
|
| 115 |
+
| 0.66 | 4.9612 | 320 | 0.6242 | 0.8088 | 0.8033 | 0.8246 | 0.8058 |
|
| 116 |
+
| 0.5706 | 5.1163 | 330 | 0.6404 | 0.7966 | 0.7905 | 0.8097 | 0.7928 |
|
| 117 |
+
| 0.5456 | 5.2713 | 340 | 0.7147 | 0.7872 | 0.7767 | 0.8060 | 0.7819 |
|
| 118 |
+
| 0.5869 | 5.4264 | 350 | 0.6267 | 0.8066 | 0.8016 | 0.8188 | 0.8025 |
|
| 119 |
+
| 0.6022 | 5.5814 | 360 | 0.6197 | 0.8061 | 0.8028 | 0.8209 | 0.8027 |
|
| 120 |
+
| 0.5676 | 5.7364 | 370 | 0.6061 | 0.8059 | 0.8005 | 0.8140 | 0.8024 |
|
| 121 |
+
| 0.5456 | 5.8915 | 380 | 0.6018 | 0.8069 | 0.8006 | 0.8254 | 0.8033 |
|
| 122 |
+
| 0.56 | 6.0465 | 390 | 0.6126 | 0.8090 | 0.8037 | 0.8206 | 0.8045 |
|
| 123 |
+
| 0.4582 | 6.2016 | 400 | 0.6122 | 0.8115 | 0.8062 | 0.8196 | 0.8061 |
|
| 124 |
+
| 0.4594 | 6.3566 | 410 | 0.6058 | 0.8122 | 0.8081 | 0.8235 | 0.8082 |
|
| 125 |
+
| 0.4868 | 6.5116 | 420 | 0.5890 | 0.8195 | 0.8131 | 0.8300 | 0.8141 |
|
| 126 |
+
| 0.4841 | 6.6667 | 430 | 0.5909 | 0.8175 | 0.8119 | 0.8250 | 0.8133 |
|
| 127 |
+
| 0.4537 | 6.8217 | 440 | 0.5889 | 0.8195 | 0.8153 | 0.8261 | 0.8164 |
|
| 128 |
+
| 0.4807 | 6.9767 | 450 | 0.6105 | 0.8144 | 0.8104 | 0.8300 | 0.8106 |
|
| 129 |
+
| 0.4051 | 7.1318 | 460 | 0.5917 | 0.8171 | 0.8103 | 0.8217 | 0.8131 |
|
| 130 |
+
| 0.3727 | 7.2868 | 470 | 0.6037 | 0.8166 | 0.8116 | 0.8262 | 0.8125 |
|
| 131 |
+
| 0.4034 | 7.4419 | 480 | 0.6407 | 0.8032 | 0.8003 | 0.8146 | 0.8015 |
|
| 132 |
+
| 0.3684 | 7.5969 | 490 | 0.6205 | 0.8061 | 0.7997 | 0.8176 | 0.8008 |
|
| 133 |
+
| 0.416 | 7.7519 | 500 | 0.5855 | 0.8258 | 0.8207 | 0.8364 | 0.8211 |
|
| 134 |
+
| 0.3947 | 7.9070 | 510 | 0.5802 | 0.8214 | 0.8179 | 0.8283 | 0.8179 |
|
| 135 |
+
| 0.3731 | 8.0620 | 520 | 0.5870 | 0.8239 | 0.8191 | 0.8324 | 0.8188 |
|
| 136 |
+
| 0.3203 | 8.2171 | 530 | 0.5783 | 0.8265 | 0.8211 | 0.8302 | 0.8216 |
|
| 137 |
+
| 0.337 | 8.3721 | 540 | 0.5836 | 0.8200 | 0.8162 | 0.8247 | 0.8166 |
|
| 138 |
+
| 0.3396 | 8.5271 | 550 | 0.5992 | 0.8156 | 0.8121 | 0.8253 | 0.8115 |
|
| 139 |
+
| 0.3355 | 8.6822 | 560 | 0.5755 | 0.8229 | 0.8182 | 0.8281 | 0.8187 |
|
| 140 |
+
| 0.3273 | 8.8372 | 570 | 0.5819 | 0.8246 | 0.8194 | 0.8268 | 0.8208 |
|
| 141 |
+
| 0.3181 | 8.9922 | 580 | 0.5840 | 0.8205 | 0.8174 | 0.8279 | 0.8168 |
|
| 142 |
+
| 0.2855 | 9.1473 | 590 | 0.5997 | 0.8144 | 0.8098 | 0.8213 | 0.8103 |
|
| 143 |
+
| 0.254 | 9.3023 | 600 | 0.5863 | 0.8183 | 0.8132 | 0.8251 | 0.8133 |
|
| 144 |
+
| 0.2781 | 9.4574 | 610 | 0.5779 | 0.8224 | 0.8169 | 0.8275 | 0.8195 |
|
| 145 |
+
| 0.2691 | 9.6124 | 620 | 0.5816 | 0.8219 | 0.8177 | 0.8257 | 0.8186 |
|
| 146 |
+
| 0.3018 | 9.7674 | 630 | 0.5814 | 0.8297 | 0.8250 | 0.8370 | 0.8253 |
|
| 147 |
+
| 0.2615 | 9.9225 | 640 | 0.5761 | 0.8299 | 0.8261 | 0.8377 | 0.8262 |
|
| 148 |
+
| 0.2707 | 10.0775 | 650 | 0.5640 | 0.8326 | 0.8283 | 0.8385 | 0.8284 |
|
| 149 |
+
| 0.2482 | 10.2326 | 660 | 0.5685 | 0.8246 | 0.8206 | 0.8284 | 0.8218 |
|
| 150 |
+
| 0.2493 | 10.3876 | 670 | 0.5717 | 0.8241 | 0.8208 | 0.8311 | 0.8199 |
|
| 151 |
+
| 0.2167 | 10.5426 | 680 | 0.5741 | 0.8246 | 0.8204 | 0.8273 | 0.8204 |
|
| 152 |
+
| 0.2628 | 10.6977 | 690 | 0.5791 | 0.8248 | 0.8205 | 0.8281 | 0.8216 |
|
| 153 |
+
| 0.2316 | 10.8527 | 700 | 0.5770 | 0.8321 | 0.8272 | 0.8348 | 0.8284 |
|
| 154 |
+
| 0.2326 | 11.0078 | 710 | 0.5755 | 0.8280 | 0.8249 | 0.8348 | 0.8249 |
|
| 155 |
+
| 0.2001 | 11.1628 | 720 | 0.5783 | 0.8336 | 0.8299 | 0.8354 | 0.8310 |
|
| 156 |
+
| 0.1759 | 11.3178 | 730 | 0.5804 | 0.8345 | 0.8302 | 0.8367 | 0.8311 |
|
| 157 |
+
| 0.202 | 11.4729 | 740 | 0.5820 | 0.8316 | 0.8278 | 0.8353 | 0.8280 |
|
| 158 |
+
| 0.2191 | 11.6279 | 750 | 0.5724 | 0.8324 | 0.8279 | 0.8341 | 0.8287 |
|
| 159 |
+
| 0.1955 | 11.7829 | 760 | 0.5957 | 0.8226 | 0.8181 | 0.8268 | 0.8198 |
|
| 160 |
+
| 0.1972 | 11.9380 | 770 | 0.5722 | 0.8294 | 0.8254 | 0.8318 | 0.8263 |
|
| 161 |
+
| 0.1848 | 12.0930 | 780 | 0.5731 | 0.8311 | 0.8269 | 0.8339 | 0.8281 |
|
| 162 |
+
| 0.1613 | 12.2481 | 790 | 0.5682 | 0.8382 | 0.8344 | 0.8397 | 0.8356 |
|
| 163 |
+
| 0.1665 | 12.4031 | 800 | 0.5565 | 0.8350 | 0.8325 | 0.8365 | 0.8325 |
|
| 164 |
+
| 0.1739 | 12.5581 | 810 | 0.5738 | 0.8360 | 0.8328 | 0.8395 | 0.8326 |
|
| 165 |
+
| 0.1744 | 12.7132 | 820 | 0.5628 | 0.8360 | 0.8327 | 0.8387 | 0.8328 |
|
| 166 |
+
| 0.1737 | 12.8682 | 830 | 0.5712 | 0.8355 | 0.8320 | 0.8395 | 0.8324 |
|
| 167 |
+
| 0.1635 | 13.0233 | 840 | 0.5745 | 0.8309 | 0.8256 | 0.8328 | 0.8269 |
|
| 168 |
+
| 0.1689 | 13.1783 | 850 | 0.5781 | 0.8326 | 0.8288 | 0.8358 | 0.8294 |
|
| 169 |
+
| 0.1611 | 13.3333 | 860 | 0.5740 | 0.8328 | 0.8280 | 0.8349 | 0.8289 |
|
| 170 |
+
| 0.1624 | 13.4884 | 870 | 0.5656 | 0.8324 | 0.8279 | 0.8328 | 0.8287 |
|
| 171 |
+
| 0.1635 | 13.6434 | 880 | 0.5618 | 0.8319 | 0.8276 | 0.8328 | 0.8280 |
|
| 172 |
+
| 0.1395 | 13.7984 | 890 | 0.5648 | 0.8350 | 0.8311 | 0.8368 | 0.8312 |
|
| 173 |
+
| 0.1489 | 13.9535 | 900 | 0.5666 | 0.8341 | 0.8304 | 0.8370 | 0.8304 |
|
| 174 |
+
| 0.1174 | 14.1085 | 910 | 0.5700 | 0.8358 | 0.8321 | 0.8400 | 0.8320 |
|
| 175 |
+
| 0.1274 | 14.2636 | 920 | 0.5720 | 0.8331 | 0.8295 | 0.8366 | 0.8295 |
|
| 176 |
+
| 0.134 | 14.4186 | 930 | 0.5657 | 0.8353 | 0.8311 | 0.8369 | 0.8317 |
|
| 177 |
+
| 0.1327 | 14.5736 | 940 | 0.5662 | 0.8343 | 0.8308 | 0.8367 | 0.8307 |
|
| 178 |
+
| 0.1165 | 14.7287 | 950 | 0.5654 | 0.8341 | 0.8301 | 0.8355 | 0.8303 |
|
| 179 |
+
| 0.1277 | 14.8837 | 960 | 0.5661 | 0.8345 | 0.8308 | 0.8360 | 0.8310 |
|
| 180 |
+
| 0.1221 | 15.0388 | 970 | 0.5615 | 0.8370 | 0.8335 | 0.8388 | 0.8335 |
|
| 181 |
+
| 0.1194 | 15.1938 | 980 | 0.5632 | 0.8353 | 0.8318 | 0.8369 | 0.8319 |
|
| 182 |
+
| 0.1126 | 15.3488 | 990 | 0.5616 | 0.8362 | 0.8326 | 0.8376 | 0.8327 |
|
| 183 |
+
| 0.1256 | 15.5039 | 1000 | 0.5612 | 0.8365 | 0.8329 | 0.8377 | 0.8331 |
|
| 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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|
|
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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 sha256:1e7e157ca5975cd1e5ead2a93dfb08575ef6c00e75f75cc082dcd1c0f6b18d51
|
| 3 |
+
size 371930976
|
preprocessor_config.json
ADDED
|
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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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
|
@@ -0,0 +1,1298 @@
|
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|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "markdown",
|
| 5 |
+
"metadata": {},
|
| 6 |
+
"source": [
|
| 7 |
+
"# Install"
|
| 8 |
+
]
|
| 9 |
+
},
|
| 10 |
+
{
|
| 11 |
+
"cell_type": "code",
|
| 12 |
+
"execution_count": 2,
|
| 13 |
+
"metadata": {},
|
| 14 |
+
"outputs": [
|
| 15 |
+
{
|
| 16 |
+
"name": "stdout",
|
| 17 |
+
"output_type": "stream",
|
| 18 |
+
"text": [
|
| 19 |
+
"Requirement already satisfied: uv in /Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages (0.1.42)\n",
|
| 20 |
+
"Note: you may need to restart the kernel to use updated packages.\n"
|
| 21 |
+
]
|
| 22 |
+
}
|
| 23 |
+
],
|
| 24 |
+
"source": [
|
| 25 |
+
"%pip install uv"
|
| 26 |
+
]
|
| 27 |
+
},
|
| 28 |
+
{
|
| 29 |
+
"cell_type": "code",
|
| 30 |
+
"execution_count": null,
|
| 31 |
+
"metadata": {},
|
| 32 |
+
"outputs": [],
|
| 33 |
+
"source": [
|
| 34 |
+
"!uv pip install dagshub setuptools accelerate toml torch torchvision transformers mlflow datasets ipywidgets python-dotenv evaluate"
|
| 35 |
+
]
|
| 36 |
+
},
|
| 37 |
+
{
|
| 38 |
+
"cell_type": "markdown",
|
| 39 |
+
"metadata": {},
|
| 40 |
+
"source": [
|
| 41 |
+
"# Setup"
|
| 42 |
+
]
|
| 43 |
+
},
|
| 44 |
+
{
|
| 45 |
+
"cell_type": "code",
|
| 46 |
+
"execution_count": 1,
|
| 47 |
+
"metadata": {},
|
| 48 |
+
"outputs": [
|
| 49 |
+
{
|
| 50 |
+
"data": {
|
| 51 |
+
"text/html": [
|
| 52 |
+
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Initialized MLflow to track repo <span style=\"color: #008000; text-decoration-color: #008000\">\"amaye15/CanineNet\"</span>\n",
|
| 53 |
+
"</pre>\n"
|
| 54 |
+
],
|
| 55 |
+
"text/plain": [
|
| 56 |
+
"Initialized MLflow to track repo \u001b[32m\"amaye15/CanineNet\"\u001b[0m\n"
|
| 57 |
+
]
|
| 58 |
+
},
|
| 59 |
+
"metadata": {},
|
| 60 |
+
"output_type": "display_data"
|
| 61 |
+
},
|
| 62 |
+
{
|
| 63 |
+
"data": {
|
| 64 |
+
"text/html": [
|
| 65 |
+
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Repository amaye15/CanineNet initialized!\n",
|
| 66 |
+
"</pre>\n"
|
| 67 |
+
],
|
| 68 |
+
"text/plain": [
|
| 69 |
+
"Repository amaye15/CanineNet initialized!\n"
|
| 70 |
+
]
|
| 71 |
+
},
|
| 72 |
+
"metadata": {},
|
| 73 |
+
"output_type": "display_data"
|
| 74 |
+
}
|
| 75 |
+
],
|
| 76 |
+
"source": [
|
| 77 |
+
"import os\n",
|
| 78 |
+
"import toml\n",
|
| 79 |
+
"import torch\n",
|
| 80 |
+
"import mlflow\n",
|
| 81 |
+
"import dagshub\n",
|
| 82 |
+
"import datasets\n",
|
| 83 |
+
"import evaluate\n",
|
| 84 |
+
"from dotenv import load_dotenv\n",
|
| 85 |
+
"from torchvision.transforms import v2\n",
|
| 86 |
+
"from transformers import AutoImageProcessor, AutoModelForImageClassification, TrainingArguments, Trainer\n",
|
| 87 |
+
"\n",
|
| 88 |
+
"ENV_PATH = \"/Users/andrewmayes/Openclassroom/CanineNet/.env\"\n",
|
| 89 |
+
"CONFIG_PATH = \"/Users/andrewmayes/Openclassroom/CanineNet/code/config.toml\"\n",
|
| 90 |
+
"CONFIG = toml.load(CONFIG_PATH)\n",
|
| 91 |
+
"\n",
|
| 92 |
+
"load_dotenv(ENV_PATH)\n",
|
| 93 |
+
"\n",
|
| 94 |
+
"dagshub.init(repo_name=os.environ['MLFLOW_TRACKING_PROJECTNAME'], repo_owner=os.environ['MLFLOW_TRACKING_USERNAME'], mlflow=True, dvc=True)\n",
|
| 95 |
+
"\n",
|
| 96 |
+
"os.environ['MLFLOW_TRACKING_USERNAME'] = \"amaye15\"\n",
|
| 97 |
+
"\n",
|
| 98 |
+
"mlflow.set_tracking_uri(f'https://dagshub.com/' + os.environ['MLFLOW_TRACKING_USERNAME']\n",
|
| 99 |
+
" + '/' + os.environ['MLFLOW_TRACKING_PROJECTNAME'] + '.mlflow')\n",
|
| 100 |
+
"\n",
|
| 101 |
+
"CREATE_DATASET = True\n",
|
| 102 |
+
"ORIGINAL_DATASET = \"Alanox/stanford-dogs\"\n",
|
| 103 |
+
"MODIFIED_DATASET = \"amaye15/stanford-dogs\"\n",
|
| 104 |
+
"REMOVE_COLUMNS = [\"name\", \"annotations\"]\n",
|
| 105 |
+
"RENAME_COLUMNS = {\"image\":\"pixel_values\", \"target\":\"label\"}\n",
|
| 106 |
+
"SPLIT = 0.2\n",
|
| 107 |
+
"\n",
|
| 108 |
+
"METRICS = [\"accuracy\", \"f1\", \"precision\", \"recall\"]\n",
|
| 109 |
+
"# MODELS = 'google/vit-base-patch16-224'\n",
|
| 110 |
+
"# MODELS = \"google/siglip-base-patch16-224\"\n",
|
| 111 |
+
"\n"
|
| 112 |
+
]
|
| 113 |
+
},
|
| 114 |
+
{
|
| 115 |
+
"cell_type": "markdown",
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| 116 |
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"metadata": {},
|
| 117 |
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"source": [
|
| 118 |
+
"# Dataset"
|
| 119 |
+
]
|
| 120 |
+
},
|
| 121 |
+
{
|
| 122 |
+
"cell_type": "code",
|
| 123 |
+
"execution_count": 2,
|
| 124 |
+
"metadata": {},
|
| 125 |
+
"outputs": [
|
| 126 |
+
{
|
| 127 |
+
"name": "stdout",
|
| 128 |
+
"output_type": "stream",
|
| 129 |
+
"text": [
|
| 130 |
+
"Affenpinscher: 0\n",
|
| 131 |
+
"Afghan Hound: 1\n",
|
| 132 |
+
"African Hunting Dog: 2\n",
|
| 133 |
+
"Airedale: 3\n",
|
| 134 |
+
"American Staffordshire Terrier: 4\n",
|
| 135 |
+
"Appenzeller: 5\n",
|
| 136 |
+
"Australian Terrier: 6\n",
|
| 137 |
+
"Basenji: 7\n",
|
| 138 |
+
"Basset: 8\n",
|
| 139 |
+
"Beagle: 9\n",
|
| 140 |
+
"Bedlington Terrier: 10\n",
|
| 141 |
+
"Bernese Mountain Dog: 11\n",
|
| 142 |
+
"Black And Tan Coonhound: 12\n",
|
| 143 |
+
"Blenheim Spaniel: 13\n",
|
| 144 |
+
"Bloodhound: 14\n",
|
| 145 |
+
"Bluetick: 15\n",
|
| 146 |
+
"Border Collie: 16\n",
|
| 147 |
+
"Border Terrier: 17\n",
|
| 148 |
+
"Borzoi: 18\n",
|
| 149 |
+
"Boston Bull: 19\n",
|
| 150 |
+
"Bouvier Des Flandres: 20\n",
|
| 151 |
+
"Boxer: 21\n",
|
| 152 |
+
"Brabancon Griffon: 22\n",
|
| 153 |
+
"Briard: 23\n",
|
| 154 |
+
"Brittany Spaniel: 24\n",
|
| 155 |
+
"Bull Mastiff: 25\n",
|
| 156 |
+
"Cairn: 26\n",
|
| 157 |
+
"Cardigan: 27\n",
|
| 158 |
+
"Chesapeake Bay Retriever: 28\n",
|
| 159 |
+
"Chihuahua: 29\n",
|
| 160 |
+
"Chow: 30\n",
|
| 161 |
+
"Clumber: 31\n",
|
| 162 |
+
"Cocker Spaniel: 32\n",
|
| 163 |
+
"Collie: 33\n",
|
| 164 |
+
"Curly Coated Retriever: 34\n",
|
| 165 |
+
"Dandie Dinmont: 35\n",
|
| 166 |
+
"Dhole: 36\n",
|
| 167 |
+
"Dingo: 37\n",
|
| 168 |
+
"Doberman: 38\n",
|
| 169 |
+
"English Foxhound: 39\n",
|
| 170 |
+
"English Setter: 40\n",
|
| 171 |
+
"English Springer: 41\n",
|
| 172 |
+
"Entlebucher: 42\n",
|
| 173 |
+
"Eskimo Dog: 43\n",
|
| 174 |
+
"Flat Coated Retriever: 44\n",
|
| 175 |
+
"French Bulldog: 45\n",
|
| 176 |
+
"German Shepherd: 46\n",
|
| 177 |
+
"German Short Haired Pointer: 47\n",
|
| 178 |
+
"Giant Schnauzer: 48\n",
|
| 179 |
+
"Golden Retriever: 49\n",
|
| 180 |
+
"Gordon Setter: 50\n",
|
| 181 |
+
"Great Dane: 51\n",
|
| 182 |
+
"Great Pyrenees: 52\n",
|
| 183 |
+
"Greater Swiss Mountain Dog: 53\n",
|
| 184 |
+
"Groenendael: 54\n",
|
| 185 |
+
"Ibizan Hound: 55\n",
|
| 186 |
+
"Irish Setter: 56\n",
|
| 187 |
+
"Irish Terrier: 57\n",
|
| 188 |
+
"Irish Water Spaniel: 58\n",
|
| 189 |
+
"Irish Wolfhound: 59\n",
|
| 190 |
+
"Italian Greyhound: 60\n",
|
| 191 |
+
"Japanese Spaniel: 61\n",
|
| 192 |
+
"Keeshond: 62\n",
|
| 193 |
+
"Kelpie: 63\n",
|
| 194 |
+
"Kerry Blue Terrier: 64\n",
|
| 195 |
+
"Komondor: 65\n",
|
| 196 |
+
"Kuvasz: 66\n",
|
| 197 |
+
"Labrador Retriever: 67\n",
|
| 198 |
+
"Lakeland Terrier: 68\n",
|
| 199 |
+
"Leonberg: 69\n",
|
| 200 |
+
"Lhasa: 70\n",
|
| 201 |
+
"Malamute: 71\n",
|
| 202 |
+
"Malinois: 72\n",
|
| 203 |
+
"Maltese Dog: 73\n",
|
| 204 |
+
"Mexican Hairless: 74\n",
|
| 205 |
+
"Miniature Pinscher: 75\n",
|
| 206 |
+
"Miniature Poodle: 76\n",
|
| 207 |
+
"Miniature Schnauzer: 77\n",
|
| 208 |
+
"Newfoundland: 78\n",
|
| 209 |
+
"Norfolk Terrier: 79\n",
|
| 210 |
+
"Norwegian Elkhound: 80\n",
|
| 211 |
+
"Norwich Terrier: 81\n",
|
| 212 |
+
"Old English Sheepdog: 82\n",
|
| 213 |
+
"Otterhound: 83\n",
|
| 214 |
+
"Papillon: 84\n",
|
| 215 |
+
"Pekinese: 85\n",
|
| 216 |
+
"Pembroke: 86\n",
|
| 217 |
+
"Pomeranian: 87\n",
|
| 218 |
+
"Pug: 88\n",
|
| 219 |
+
"Redbone: 89\n",
|
| 220 |
+
"Rhodesian Ridgeback: 90\n",
|
| 221 |
+
"Rottweiler: 91\n",
|
| 222 |
+
"Saint Bernard: 92\n",
|
| 223 |
+
"Saluki: 93\n",
|
| 224 |
+
"Samoyed: 94\n",
|
| 225 |
+
"Schipperke: 95\n",
|
| 226 |
+
"Scotch Terrier: 96\n",
|
| 227 |
+
"Scottish Deerhound: 97\n",
|
| 228 |
+
"Sealyham Terrier: 98\n",
|
| 229 |
+
"Shetland Sheepdog: 99\n",
|
| 230 |
+
"Shih Tzu: 100\n",
|
| 231 |
+
"Siberian Husky: 101\n",
|
| 232 |
+
"Silky Terrier: 102\n",
|
| 233 |
+
"Soft Coated Wheaten Terrier: 103\n",
|
| 234 |
+
"Staffordshire Bullterrier: 104\n",
|
| 235 |
+
"Standard Poodle: 105\n",
|
| 236 |
+
"Standard Schnauzer: 106\n",
|
| 237 |
+
"Sussex Spaniel: 107\n",
|
| 238 |
+
"Tibetan Mastiff: 108\n",
|
| 239 |
+
"Tibetan Terrier: 109\n",
|
| 240 |
+
"Toy Poodle: 110\n",
|
| 241 |
+
"Toy Terrier: 111\n",
|
| 242 |
+
"Vizsla: 112\n",
|
| 243 |
+
"Walker Hound: 113\n",
|
| 244 |
+
"Weimaraner: 114\n",
|
| 245 |
+
"Welsh Springer Spaniel: 115\n",
|
| 246 |
+
"West Highland White Terrier: 116\n",
|
| 247 |
+
"Whippet: 117\n",
|
| 248 |
+
"Wire Haired Fox Terrier: 118\n",
|
| 249 |
+
"Yorkshire Terrier: 119\n"
|
| 250 |
+
]
|
| 251 |
+
}
|
| 252 |
+
],
|
| 253 |
+
"source": [
|
| 254 |
+
"if CREATE_DATASET:\n",
|
| 255 |
+
" ds = datasets.load_dataset(ORIGINAL_DATASET, token=os.getenv(\"HF_TOKEN\"), split=\"full\", trust_remote_code=True)\n",
|
| 256 |
+
" ds = ds.remove_columns(REMOVE_COLUMNS).rename_columns(RENAME_COLUMNS)\n",
|
| 257 |
+
"\n",
|
| 258 |
+
" labels = ds.select_columns(\"label\").to_pandas().sort_values(\"label\").get(\"label\").unique().tolist()\n",
|
| 259 |
+
" numbers = range(len(labels))\n",
|
| 260 |
+
" label2int = dict(zip(labels, numbers))\n",
|
| 261 |
+
" int2label = dict(zip(numbers, labels))\n",
|
| 262 |
+
"\n",
|
| 263 |
+
" for key, val in label2int.items():\n",
|
| 264 |
+
" print(f\"{key}: {val}\")\n",
|
| 265 |
+
"\n",
|
| 266 |
+
" ds = ds.class_encode_column(\"label\")\n",
|
| 267 |
+
" ds = ds.align_labels_with_mapping(label2int, \"label\")\n",
|
| 268 |
+
"\n",
|
| 269 |
+
" ds = ds.train_test_split(test_size=SPLIT, stratify_by_column = \"label\")\n",
|
| 270 |
+
" #ds.push_to_hub(MODIFIED_DATASET, token=os.getenv(\"HF_TOKEN\"))\n",
|
| 271 |
+
"\n",
|
| 272 |
+
" CONFIG[\"label2int\"] = str(label2int)\n",
|
| 273 |
+
" CONFIG[\"int2label\"] = str(int2label)\n",
|
| 274 |
+
"\n",
|
| 275 |
+
" # with open(\"output.toml\", \"w\") as toml_file:\n",
|
| 276 |
+
" # toml.dump(toml.dumps(CONFIG), toml_file)\n",
|
| 277 |
+
"\n",
|
| 278 |
+
" #ds = datasets.load_dataset(MODIFIED_DATASET, token=os.getenv(\"HF_TOKEN\"), trust_remote_code=True, streaming=True)"
|
| 279 |
+
]
|
| 280 |
+
},
|
| 281 |
+
{
|
| 282 |
+
"cell_type": "code",
|
| 283 |
+
"execution_count": 3,
|
| 284 |
+
"metadata": {},
|
| 285 |
+
"outputs": [
|
| 286 |
+
{
|
| 287 |
+
"name": "stderr",
|
| 288 |
+
"output_type": "stream",
|
| 289 |
+
"text": [
|
| 290 |
+
"/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
|
| 291 |
+
" warnings.warn(\n",
|
| 292 |
+
"Some weights of SiglipForImageClassification were not initialized from the model checkpoint at google/siglip-base-patch16-224 and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
|
| 293 |
+
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
|
| 294 |
+
"max_steps is given, it will override any value given in num_train_epochs\n"
|
| 295 |
+
]
|
| 296 |
+
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| 312 |
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| 313 |
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| 314 |
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"/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
|
| 316 |
+
" warnings.warn(\n"
|
| 317 |
+
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|
| 318 |
+
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| 319 |
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| 320 |
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"{'loss': 4.822, 'grad_norm': 11.180054664611816, 'learning_rate': 4.9500000000000004e-05, 'epoch': 0.16}\n"
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"/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
|
| 345 |
+
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
|
| 346 |
+
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|
| 347 |
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| 348 |
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| 349 |
+
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| 350 |
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+
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| 352 |
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"{'eval_loss': 4.254875183105469, 'eval_accuracy': 0.0782312925170068, 'eval_f1': 0.04927852996179247, 'eval_precision': 0.09874043278607707, 'eval_recall': 0.07264375052644872, 'eval_runtime': 55.3923, 'eval_samples_per_second': 74.306, 'eval_steps_per_second': 1.173, 'epoch': 0.16}\n"
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"/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
|
| 360 |
+
" warnings.warn(\n"
|
| 361 |
+
]
|
| 362 |
+
},
|
| 363 |
+
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| 364 |
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| 365 |
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"/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
|
| 389 |
+
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
|
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"{'eval_loss': 3.5278525352478027, 'eval_accuracy': 0.19071914480077745, 'eval_f1': 0.15072037668109098, 'eval_precision': 0.22011886017337598, 'eval_recall': 0.18300531751649823, 'eval_runtime': 55.5125, 'eval_samples_per_second': 74.145, 'eval_steps_per_second': 1.171, 'epoch': 0.31}\n"
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"/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
|
| 404 |
+
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|
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+
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"metadata": {},
|
| 1030 |
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"output_type": "display_data"
|
| 1031 |
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{
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| 1033 |
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"name": "stdout",
|
| 1034 |
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"output_type": "stream",
|
| 1035 |
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"text": [
|
| 1036 |
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"{'eval_loss': 0.7559003233909607, 'eval_accuracy': 0.7650631681243926, 'eval_f1': 0.7586751052497263, 'eval_precision': 0.7920018070685718, 'eval_recall': 0.7609226412898984, 'eval_runtime': 53.1768, 'eval_samples_per_second': 77.402, 'eval_steps_per_second': 1.222, 'epoch': 2.79}\n"
|
| 1037 |
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]
|
| 1038 |
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},
|
| 1039 |
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{
|
| 1040 |
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"name": "stderr",
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"output_type": "stream",
|
| 1042 |
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"text": [
|
| 1043 |
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"/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
|
| 1044 |
+
" warnings.warn(\n"
|
| 1045 |
+
]
|
| 1046 |
+
},
|
| 1047 |
+
{
|
| 1048 |
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"name": "stdout",
|
| 1049 |
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"output_type": "stream",
|
| 1050 |
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"text": [
|
| 1051 |
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"{'loss': 0.9733, 'grad_norm': 14.432697296142578, 'learning_rate': 4.05e-05, 'epoch': 2.95}\n"
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| 1052 |
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| 1053 |
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|
| 1054 |
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{
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| 1055 |
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"data": {
|
| 1056 |
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"application/vnd.jupyter.widget-view+json": {
|
| 1057 |
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"model_id": "35ef518721bb4ff29f1d1d6286fc5f75",
|
| 1058 |
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"version_major": 2,
|
| 1059 |
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"version_minor": 0
|
| 1060 |
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"text/plain": [
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"metadata": {},
|
| 1066 |
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"output_type": "display_data"
|
| 1067 |
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},
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| 1068 |
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{
|
| 1069 |
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"name": "stderr",
|
| 1070 |
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"output_type": "stream",
|
| 1071 |
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"text": [
|
| 1072 |
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"/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
|
| 1073 |
+
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
|
| 1074 |
+
]
|
| 1075 |
+
},
|
| 1076 |
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{
|
| 1077 |
+
"name": "stdout",
|
| 1078 |
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"output_type": "stream",
|
| 1079 |
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"text": [
|
| 1080 |
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"{'eval_loss': 0.7778576612472534, 'eval_accuracy': 0.7553449951409135, 'eval_f1': 0.7458481776644565, 'eval_precision': 0.7797152587168623, 'eval_recall': 0.7521461869682271, 'eval_runtime': 54.2043, 'eval_samples_per_second': 75.935, 'eval_steps_per_second': 1.199, 'epoch': 2.95}\n"
|
| 1081 |
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]
|
| 1082 |
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},
|
| 1083 |
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{
|
| 1084 |
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
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| 1087 |
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"/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
|
| 1088 |
+
" warnings.warn(\n"
|
| 1089 |
+
]
|
| 1090 |
+
}
|
| 1091 |
+
],
|
| 1092 |
+
"source": [
|
| 1093 |
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"metrics = {metric: evaluate.load(metric) for metric in METRICS}\n",
|
| 1094 |
+
"\n",
|
| 1095 |
+
"\n",
|
| 1096 |
+
"# for lr in [5e-3, 5e-4, 5e-5]: # 5e-5\n",
|
| 1097 |
+
"# for batch in [64]: # 32\n",
|
| 1098 |
+
"# for model_name in [\"google/vit-base-patch16-224\", \"microsoft/swinv2-base-patch4-window16-256\", \"google/siglip-base-patch16-224\"]: # \"facebook/dinov2-base\"\n",
|
| 1099 |
+
"\n",
|
| 1100 |
+
"lr = 5e-5\n",
|
| 1101 |
+
"batch = 64\n",
|
| 1102 |
+
"model_name = \"google/siglip-base-patch16-224\"\n",
|
| 1103 |
+
"\n",
|
| 1104 |
+
"image_processor = AutoImageProcessor.from_pretrained(model_name)\n",
|
| 1105 |
+
"model = AutoModelForImageClassification.from_pretrained(\n",
|
| 1106 |
+
"model_name,\n",
|
| 1107 |
+
"num_labels=len(label2int),\n",
|
| 1108 |
+
"id2label=int2label,\n",
|
| 1109 |
+
"label2id=label2int,\n",
|
| 1110 |
+
"ignore_mismatched_sizes=True,\n",
|
| 1111 |
+
")\n",
|
| 1112 |
+
"\n",
|
| 1113 |
+
"# Then, in your transformations:\n",
|
| 1114 |
+
"def train_transform(examples, num_ops=10, magnitude=9, num_magnitude_bins=31):\n",
|
| 1115 |
+
"\n",
|
| 1116 |
+
" transformation = v2.Compose(\n",
|
| 1117 |
+
" [\n",
|
| 1118 |
+
" v2.RandAugment(\n",
|
| 1119 |
+
" num_ops=num_ops,\n",
|
| 1120 |
+
" magnitude=magnitude,\n",
|
| 1121 |
+
" num_magnitude_bins=num_magnitude_bins,\n",
|
| 1122 |
+
" )\n",
|
| 1123 |
+
" ]\n",
|
| 1124 |
+
" )\n",
|
| 1125 |
+
" # Ensure each image has three dimensions (in this case, ensure it's RGB)\n",
|
| 1126 |
+
" examples[\"pixel_values\"] = [\n",
|
| 1127 |
+
" image.convert(\"RGB\") for image in examples[\"pixel_values\"]\n",
|
| 1128 |
+
" ]\n",
|
| 1129 |
+
" # Apply transformations\n",
|
| 1130 |
+
" examples[\"pixel_values\"] = [\n",
|
| 1131 |
+
" image_processor(transformation(image), return_tensors=\"pt\")[\n",
|
| 1132 |
+
" \"pixel_values\"\n",
|
| 1133 |
+
" ].squeeze()\n",
|
| 1134 |
+
" for image in examples[\"pixel_values\"]\n",
|
| 1135 |
+
" ]\n",
|
| 1136 |
+
" return examples\n",
|
| 1137 |
+
"\n",
|
| 1138 |
+
"\n",
|
| 1139 |
+
"def test_transform(examples):\n",
|
| 1140 |
+
" # Ensure each image is RGB\n",
|
| 1141 |
+
" examples[\"pixel_values\"] = [\n",
|
| 1142 |
+
" image.convert(\"RGB\") for image in examples[\"pixel_values\"]\n",
|
| 1143 |
+
" ]\n",
|
| 1144 |
+
" # Apply processing\n",
|
| 1145 |
+
" examples[\"pixel_values\"] = [\n",
|
| 1146 |
+
" image_processor(image, return_tensors=\"pt\")[\"pixel_values\"].squeeze()\n",
|
| 1147 |
+
" for image in examples[\"pixel_values\"]\n",
|
| 1148 |
+
" ]\n",
|
| 1149 |
+
" return examples\n",
|
| 1150 |
+
"\n",
|
| 1151 |
+
"\n",
|
| 1152 |
+
"def compute_metrics(eval_pred):\n",
|
| 1153 |
+
" predictions, labels = eval_pred\n",
|
| 1154 |
+
" # predictions = np.argmax(logits, axis=-1)\n",
|
| 1155 |
+
" results = {}\n",
|
| 1156 |
+
" for key, val in metrics.items():\n",
|
| 1157 |
+
" if \"accuracy\" == key:\n",
|
| 1158 |
+
" result = next(\n",
|
| 1159 |
+
" iter(val.compute(predictions=predictions, references=labels).items())\n",
|
| 1160 |
+
" )\n",
|
| 1161 |
+
" if \"accuracy\" != key:\n",
|
| 1162 |
+
" result = next(\n",
|
| 1163 |
+
" iter(\n",
|
| 1164 |
+
" val.compute(\n",
|
| 1165 |
+
" predictions=predictions, references=labels, average=\"macro\"\n",
|
| 1166 |
+
" ).items()\n",
|
| 1167 |
+
" )\n",
|
| 1168 |
+
" )\n",
|
| 1169 |
+
" results[result[0]] = result[1]\n",
|
| 1170 |
+
" return results\n",
|
| 1171 |
+
"\n",
|
| 1172 |
+
"\n",
|
| 1173 |
+
"def collate_fn(examples):\n",
|
| 1174 |
+
" pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n",
|
| 1175 |
+
" labels = torch.tensor([example[\"label\"] for example in examples])\n",
|
| 1176 |
+
" return {\"pixel_values\": pixel_values, \"labels\": labels}\n",
|
| 1177 |
+
"\n",
|
| 1178 |
+
"\n",
|
| 1179 |
+
"def preprocess_logits_for_metrics(logits, labels):\n",
|
| 1180 |
+
" \"\"\"\n",
|
| 1181 |
+
" Original Trainer may have a memory leak.\n",
|
| 1182 |
+
" This is a workaround to avoid storing too many tensors that are not needed.\n",
|
| 1183 |
+
" \"\"\"\n",
|
| 1184 |
+
" pred_ids = torch.argmax(logits, dim=-1)\n",
|
| 1185 |
+
" return pred_ids\n",
|
| 1186 |
+
"\n",
|
| 1187 |
+
"ds[\"train\"].set_transform(train_transform)\n",
|
| 1188 |
+
"ds[\"test\"].set_transform(test_transform)\n",
|
| 1189 |
+
"\n",
|
| 1190 |
+
"training_args = TrainingArguments(**CONFIG[\"training_args\"])\n",
|
| 1191 |
+
"training_args.per_device_train_batch_size = batch\n",
|
| 1192 |
+
"training_args.per_device_eval_batch_size = batch\n",
|
| 1193 |
+
"training_args.hub_model_id = f\"amaye15/{model_name.replace('/','-')}-batch{batch}-lr{lr}-standford-dogs\"\n",
|
| 1194 |
+
"\n",
|
| 1195 |
+
"mlflow.start_run(run_name=f\"{model_name.replace('/','-')}-batch{batch}-lr{lr}\")\n",
|
| 1196 |
+
"\n",
|
| 1197 |
+
"trainer = Trainer(\n",
|
| 1198 |
+
" model=model,\n",
|
| 1199 |
+
" args=training_args,\n",
|
| 1200 |
+
" train_dataset=ds[\"train\"],\n",
|
| 1201 |
+
" eval_dataset=ds[\"test\"],\n",
|
| 1202 |
+
" tokenizer=image_processor,\n",
|
| 1203 |
+
" data_collator=collate_fn,\n",
|
| 1204 |
+
" compute_metrics=compute_metrics,\n",
|
| 1205 |
+
" # callbacks=[early_stopping_callback],\n",
|
| 1206 |
+
" preprocess_logits_for_metrics=preprocess_logits_for_metrics,\n",
|
| 1207 |
+
")\n",
|
| 1208 |
+
"\n",
|
| 1209 |
+
"# Train the model\n",
|
| 1210 |
+
"trainer.train()\n",
|
| 1211 |
+
"\n",
|
| 1212 |
+
"trainer.push_to_hub()\n",
|
| 1213 |
+
"\n",
|
| 1214 |
+
"mlflow.end_run()"
|
| 1215 |
+
]
|
| 1216 |
+
},
|
| 1217 |
+
{
|
| 1218 |
+
"cell_type": "code",
|
| 1219 |
+
"execution_count": null,
|
| 1220 |
+
"metadata": {},
|
| 1221 |
+
"outputs": [
|
| 1222 |
+
{
|
| 1223 |
+
"ename": "NameError",
|
| 1224 |
+
"evalue": "name 'mlflow' is not defined",
|
| 1225 |
+
"output_type": "error",
|
| 1226 |
+
"traceback": [
|
| 1227 |
+
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
| 1228 |
+
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
| 1229 |
+
"Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmlflow\u001b[49m\u001b[38;5;241m.\u001b[39mend_run()\n",
|
| 1230 |
+
"\u001b[0;31mNameError\u001b[0m: name 'mlflow' is not defined"
|
| 1231 |
+
]
|
| 1232 |
+
}
|
| 1233 |
+
],
|
| 1234 |
+
"source": [
|
| 1235 |
+
"mlflow.end_run()"
|
| 1236 |
+
]
|
| 1237 |
+
},
|
| 1238 |
+
{
|
| 1239 |
+
"cell_type": "code",
|
| 1240 |
+
"execution_count": null,
|
| 1241 |
+
"metadata": {},
|
| 1242 |
+
"outputs": [],
|
| 1243 |
+
"source": [
|
| 1244 |
+
"# training_args = TrainingArguments(**CONFIG[\"training_args\"])\n",
|
| 1245 |
+
"\n",
|
| 1246 |
+
"# image_processor = AutoImageProcessor.from_pretrained(MODELS)\n",
|
| 1247 |
+
"# model = AutoModelForImageClassification.from_pretrained(\n",
|
| 1248 |
+
"# MODELS,\n",
|
| 1249 |
+
"# num_labels=len(CONFIG[\"label2int\"]),\n",
|
| 1250 |
+
"# id2label=CONFIG[\"label2int\"],\n",
|
| 1251 |
+
"# label2id=CONFIG[\"int2label\"],\n",
|
| 1252 |
+
"# ignore_mismatched_sizes=True,\n",
|
| 1253 |
+
"# )\n",
|
| 1254 |
+
"\n",
|
| 1255 |
+
"\n",
|
| 1256 |
+
"# training_args = TrainingArguments(**CONFIG[\"training_args\"])\n",
|
| 1257 |
+
"\n",
|
| 1258 |
+
"# trainer = Trainer(\n",
|
| 1259 |
+
"# model=model,\n",
|
| 1260 |
+
"# args=training_args,\n",
|
| 1261 |
+
"# train_dataset=ds[\"train\"],\n",
|
| 1262 |
+
"# eval_dataset=ds[\"test\"],\n",
|
| 1263 |
+
"# tokenizer=image_processor,\n",
|
| 1264 |
+
"# data_collator=collate_fn,\n",
|
| 1265 |
+
"# compute_metrics=compute_metrics,\n",
|
| 1266 |
+
"# # callbacks=[early_stopping_callback],\n",
|
| 1267 |
+
"# preprocess_logits_for_metrics=preprocess_logits_for_metrics,\n",
|
| 1268 |
+
"# )\n",
|
| 1269 |
+
"\n",
|
| 1270 |
+
"# # Train the model\n",
|
| 1271 |
+
"# trainer.train()\n",
|
| 1272 |
+
"\n",
|
| 1273 |
+
"# mlflow.end_run()"
|
| 1274 |
+
]
|
| 1275 |
+
}
|
| 1276 |
+
],
|
| 1277 |
+
"metadata": {
|
| 1278 |
+
"kernelspec": {
|
| 1279 |
+
"display_name": "env",
|
| 1280 |
+
"language": "python",
|
| 1281 |
+
"name": "python3"
|
| 1282 |
+
},
|
| 1283 |
+
"language_info": {
|
| 1284 |
+
"codemirror_mode": {
|
| 1285 |
+
"name": "ipython",
|
| 1286 |
+
"version": 3
|
| 1287 |
+
},
|
| 1288 |
+
"file_extension": ".py",
|
| 1289 |
+
"mimetype": "text/x-python",
|
| 1290 |
+
"name": "python",
|
| 1291 |
+
"nbconvert_exporter": "python",
|
| 1292 |
+
"pygments_lexer": "ipython3",
|
| 1293 |
+
"version": "3.12.3"
|
| 1294 |
+
}
|
| 1295 |
+
},
|
| 1296 |
+
"nbformat": 4,
|
| 1297 |
+
"nbformat_minor": 2
|
| 1298 |
+
}
|
training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:b1e2adea2ad13837e172adc6cc3a084bf72cd54a57bc6b1cc0cd252cea258e77
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| 3 |
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size 5112
|