End of training
Browse files- README.md +191 -0
- config.json +303 -0
- config.toml +27 -0
- model.safetensors +3 -0
- preprocessor_config.json +36 -0
- train.ipynb +2084 -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: microsoft/swinv2-base-patch4-window16-256
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| 4 |
+
tags:
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+
- 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: microsoft-swinv2-base-patch4-window16-256-batch32-lr0.0005-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 |
+
dataset:
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| 20 |
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name: stanford-dogs
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| 21 |
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type: stanford-dogs
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| 22 |
+
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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| 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: 0.9429057337220602
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| 29 |
+
- name: F1
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| 30 |
+
type: f1
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| 31 |
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value: 0.9410841953165723
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| 32 |
+
- name: Precision
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| 33 |
+
type: precision
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| 34 |
+
value: 0.9431724455914652
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| 35 |
+
- name: Recall
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| 36 |
+
type: recall
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| 37 |
+
value: 0.9417046971391595
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| 38 |
+
---
|
| 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 |
+
|
| 43 |
+
# microsoft-swinv2-base-patch4-window16-256-batch32-lr0.0005-standford-dogs
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| 44 |
+
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| 45 |
+
This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window16-256](https://huggingface.co/microsoft/swinv2-base-patch4-window16-256) 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: 0.1810
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| 48 |
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- Accuracy: 0.9429
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| 49 |
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- F1: 0.9411
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| 50 |
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- Precision: 0.9432
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| 51 |
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- Recall: 0.9417
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| 52 |
+
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| 53 |
+
## 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 |
+
## 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 |
+
## 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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| 70 |
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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 |
+
- seed: 42
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| 74 |
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- gradient_accumulation_steps: 4
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| 75 |
+
- total_train_batch_size: 128
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| 76 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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| 77 |
+
- lr_scheduler_type: linear
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| 78 |
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- training_steps: 1000
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| 79 |
+
|
| 80 |
+
### Training results
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| 81 |
+
|
| 82 |
+
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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| 83 |
+
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
|
| 84 |
+
| 4.7518 | 0.0777 | 10 | 4.6391 | 0.0741 | 0.0533 | 0.0667 | 0.0705 |
|
| 85 |
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| 4.5585 | 0.1553 | 20 | 4.3463 | 0.1919 | 0.1445 | 0.1900 | 0.1794 |
|
| 86 |
+
| 4.2377 | 0.2330 | 30 | 3.8243 | 0.3525 | 0.3100 | 0.4154 | 0.3382 |
|
| 87 |
+
| 3.6654 | 0.3107 | 40 | 2.9276 | 0.6409 | 0.6111 | 0.6994 | 0.6300 |
|
| 88 |
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| 2.7617 | 0.3883 | 50 | 1.7703 | 0.8248 | 0.8042 | 0.8361 | 0.8182 |
|
| 89 |
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| 1.9475 | 0.4660 | 60 | 1.0440 | 0.8863 | 0.8781 | 0.8924 | 0.8821 |
|
| 90 |
+
| 1.3629 | 0.5437 | 70 | 0.6490 | 0.9099 | 0.9031 | 0.9191 | 0.9062 |
|
| 91 |
+
| 1.0488 | 0.6214 | 80 | 0.4485 | 0.9150 | 0.9075 | 0.9147 | 0.9118 |
|
| 92 |
+
| 0.8477 | 0.6990 | 90 | 0.3744 | 0.9206 | 0.9169 | 0.9294 | 0.9190 |
|
| 93 |
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| 0.7184 | 0.7767 | 100 | 0.3301 | 0.9259 | 0.9215 | 0.9283 | 0.9227 |
|
| 94 |
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| 0.7149 | 0.8544 | 110 | 0.2970 | 0.9186 | 0.9152 | 0.9227 | 0.9156 |
|
| 95 |
+
| 0.6429 | 0.9320 | 120 | 0.2675 | 0.9286 | 0.9238 | 0.9301 | 0.9256 |
|
| 96 |
+
| 0.5864 | 1.0097 | 130 | 0.2609 | 0.9291 | 0.9258 | 0.9338 | 0.9272 |
|
| 97 |
+
| 0.5414 | 1.0874 | 140 | 0.2644 | 0.9162 | 0.9122 | 0.9212 | 0.9156 |
|
| 98 |
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| 0.5323 | 1.1650 | 150 | 0.2454 | 0.9281 | 0.9225 | 0.9362 | 0.9256 |
|
| 99 |
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| 0.5061 | 1.2427 | 160 | 0.2481 | 0.9269 | 0.9235 | 0.9308 | 0.9251 |
|
| 100 |
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| 0.5898 | 1.3204 | 170 | 0.2306 | 0.9346 | 0.9324 | 0.9389 | 0.9331 |
|
| 101 |
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| 0.5277 | 1.3981 | 180 | 0.2192 | 0.9368 | 0.9327 | 0.9384 | 0.9350 |
|
| 102 |
+
| 0.4824 | 1.4757 | 190 | 0.2171 | 0.9337 | 0.9297 | 0.9375 | 0.9311 |
|
| 103 |
+
| 0.4632 | 1.5534 | 200 | 0.2244 | 0.9346 | 0.9315 | 0.9379 | 0.9326 |
|
| 104 |
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| 0.4882 | 1.6311 | 210 | 0.2237 | 0.9361 | 0.9323 | 0.9404 | 0.9345 |
|
| 105 |
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| 0.4583 | 1.7087 | 220 | 0.2228 | 0.9327 | 0.9289 | 0.9373 | 0.9304 |
|
| 106 |
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| 0.4692 | 1.7864 | 230 | 0.2098 | 0.9354 | 0.9316 | 0.9370 | 0.9332 |
|
| 107 |
+
| 0.5407 | 1.8641 | 240 | 0.2102 | 0.9356 | 0.9342 | 0.9375 | 0.9351 |
|
| 108 |
+
| 0.4629 | 1.9417 | 250 | 0.2045 | 0.9378 | 0.9349 | 0.9396 | 0.9367 |
|
| 109 |
+
| 0.4363 | 2.0194 | 260 | 0.2023 | 0.9373 | 0.9346 | 0.9398 | 0.9355 |
|
| 110 |
+
| 0.4328 | 2.0971 | 270 | 0.2063 | 0.9354 | 0.9320 | 0.9360 | 0.9343 |
|
| 111 |
+
| 0.3554 | 2.1748 | 280 | 0.1948 | 0.9439 | 0.9398 | 0.9475 | 0.9418 |
|
| 112 |
+
| 0.4024 | 2.2524 | 290 | 0.1985 | 0.9388 | 0.9372 | 0.9397 | 0.9377 |
|
| 113 |
+
| 0.4006 | 2.3301 | 300 | 0.2153 | 0.9334 | 0.9275 | 0.9420 | 0.9311 |
|
| 114 |
+
| 0.3935 | 2.4078 | 310 | 0.2021 | 0.9393 | 0.9346 | 0.9416 | 0.9368 |
|
| 115 |
+
| 0.3591 | 2.4854 | 320 | 0.2126 | 0.9346 | 0.9311 | 0.9403 | 0.9333 |
|
| 116 |
+
| 0.4058 | 2.5631 | 330 | 0.2020 | 0.9378 | 0.9357 | 0.9393 | 0.9358 |
|
| 117 |
+
| 0.396 | 2.6408 | 340 | 0.2038 | 0.9371 | 0.9339 | 0.9410 | 0.9357 |
|
| 118 |
+
| 0.4157 | 2.7184 | 350 | 0.2091 | 0.9332 | 0.9288 | 0.9352 | 0.9308 |
|
| 119 |
+
| 0.4222 | 2.7961 | 360 | 0.1933 | 0.9393 | 0.9372 | 0.9399 | 0.9378 |
|
| 120 |
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| 0.3521 | 2.8738 | 370 | 0.1984 | 0.9397 | 0.9381 | 0.9430 | 0.9388 |
|
| 121 |
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| 0.3925 | 2.9515 | 380 | 0.1874 | 0.9383 | 0.9347 | 0.9390 | 0.9358 |
|
| 122 |
+
| 0.3475 | 3.0291 | 390 | 0.1994 | 0.9383 | 0.9364 | 0.9410 | 0.9376 |
|
| 123 |
+
| 0.3526 | 3.1068 | 400 | 0.1941 | 0.9390 | 0.9352 | 0.9402 | 0.9373 |
|
| 124 |
+
| 0.351 | 3.1845 | 410 | 0.1893 | 0.9417 | 0.9403 | 0.9438 | 0.9410 |
|
| 125 |
+
| 0.3549 | 3.2621 | 420 | 0.1960 | 0.9390 | 0.9370 | 0.9410 | 0.9381 |
|
| 126 |
+
| 0.3291 | 3.3398 | 430 | 0.1948 | 0.9397 | 0.9358 | 0.9387 | 0.9374 |
|
| 127 |
+
| 0.3153 | 3.4175 | 440 | 0.1992 | 0.9441 | 0.9415 | 0.9453 | 0.9427 |
|
| 128 |
+
| 0.3116 | 3.4951 | 450 | 0.2005 | 0.9417 | 0.9389 | 0.9432 | 0.9404 |
|
| 129 |
+
| 0.3053 | 3.5728 | 460 | 0.1974 | 0.9412 | 0.9372 | 0.9424 | 0.9394 |
|
| 130 |
+
| 0.3141 | 3.6505 | 470 | 0.1941 | 0.9405 | 0.9386 | 0.9420 | 0.9395 |
|
| 131 |
+
| 0.3275 | 3.7282 | 480 | 0.2182 | 0.9334 | 0.9301 | 0.9374 | 0.9321 |
|
| 132 |
+
| 0.2997 | 3.8058 | 490 | 0.2029 | 0.9376 | 0.9343 | 0.9392 | 0.9360 |
|
| 133 |
+
| 0.3242 | 3.8835 | 500 | 0.1996 | 0.9380 | 0.9344 | 0.9399 | 0.9361 |
|
| 134 |
+
| 0.3585 | 3.9612 | 510 | 0.1935 | 0.9405 | 0.9378 | 0.9421 | 0.9389 |
|
| 135 |
+
| 0.2942 | 4.0388 | 520 | 0.2028 | 0.9368 | 0.9341 | 0.9428 | 0.9367 |
|
| 136 |
+
| 0.3233 | 4.1165 | 530 | 0.2029 | 0.9378 | 0.9353 | 0.9406 | 0.9364 |
|
| 137 |
+
| 0.2942 | 4.1942 | 540 | 0.1959 | 0.9385 | 0.9368 | 0.9395 | 0.9372 |
|
| 138 |
+
| 0.3079 | 4.2718 | 550 | 0.1941 | 0.9371 | 0.9349 | 0.9373 | 0.9354 |
|
| 139 |
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| 0.2931 | 4.3495 | 560 | 0.1871 | 0.9414 | 0.9388 | 0.9410 | 0.9394 |
|
| 140 |
+
| 0.3058 | 4.4272 | 570 | 0.1879 | 0.9419 | 0.9403 | 0.9430 | 0.9407 |
|
| 141 |
+
| 0.3402 | 4.5049 | 580 | 0.1833 | 0.9434 | 0.9409 | 0.9435 | 0.9420 |
|
| 142 |
+
| 0.3169 | 4.5825 | 590 | 0.1882 | 0.9412 | 0.9391 | 0.9425 | 0.9402 |
|
| 143 |
+
| 0.3071 | 4.6602 | 600 | 0.1821 | 0.9448 | 0.9425 | 0.9460 | 0.9431 |
|
| 144 |
+
| 0.313 | 4.7379 | 610 | 0.1879 | 0.9429 | 0.9401 | 0.9441 | 0.9413 |
|
| 145 |
+
| 0.3338 | 4.8155 | 620 | 0.1843 | 0.9456 | 0.9424 | 0.9469 | 0.9439 |
|
| 146 |
+
| 0.2468 | 4.8932 | 630 | 0.1866 | 0.9436 | 0.9412 | 0.9441 | 0.9426 |
|
| 147 |
+
| 0.2567 | 4.9709 | 640 | 0.1882 | 0.9405 | 0.9387 | 0.9417 | 0.9393 |
|
| 148 |
+
| 0.2792 | 5.0485 | 650 | 0.1914 | 0.9429 | 0.9407 | 0.9442 | 0.9418 |
|
| 149 |
+
| 0.2985 | 5.1262 | 660 | 0.1880 | 0.9429 | 0.9393 | 0.9442 | 0.9411 |
|
| 150 |
+
| 0.2744 | 5.2039 | 670 | 0.1865 | 0.9410 | 0.9378 | 0.9420 | 0.9390 |
|
| 151 |
+
| 0.2662 | 5.2816 | 680 | 0.1877 | 0.9419 | 0.9400 | 0.9423 | 0.9407 |
|
| 152 |
+
| 0.2613 | 5.3592 | 690 | 0.1890 | 0.9393 | 0.9369 | 0.9401 | 0.9378 |
|
| 153 |
+
| 0.2698 | 5.4369 | 700 | 0.1849 | 0.9429 | 0.9409 | 0.9441 | 0.9417 |
|
| 154 |
+
| 0.2592 | 5.5146 | 710 | 0.1854 | 0.9429 | 0.9414 | 0.9439 | 0.9425 |
|
| 155 |
+
| 0.2819 | 5.5922 | 720 | 0.1868 | 0.9429 | 0.9414 | 0.9443 | 0.9418 |
|
| 156 |
+
| 0.2625 | 5.6699 | 730 | 0.1832 | 0.9434 | 0.9417 | 0.9438 | 0.9422 |
|
| 157 |
+
| 0.273 | 5.7476 | 740 | 0.1862 | 0.9439 | 0.9408 | 0.9445 | 0.9424 |
|
| 158 |
+
| 0.2718 | 5.8252 | 750 | 0.1838 | 0.9441 | 0.9417 | 0.9443 | 0.9428 |
|
| 159 |
+
| 0.3055 | 5.9029 | 760 | 0.1852 | 0.9422 | 0.9396 | 0.9426 | 0.9407 |
|
| 160 |
+
| 0.276 | 5.9806 | 770 | 0.1843 | 0.9424 | 0.9409 | 0.9434 | 0.9415 |
|
| 161 |
+
| 0.2614 | 6.0583 | 780 | 0.1839 | 0.9429 | 0.9403 | 0.9431 | 0.9411 |
|
| 162 |
+
| 0.2452 | 6.1359 | 790 | 0.1858 | 0.9407 | 0.9384 | 0.9414 | 0.9390 |
|
| 163 |
+
| 0.2608 | 6.2136 | 800 | 0.1851 | 0.9429 | 0.9411 | 0.9437 | 0.9417 |
|
| 164 |
+
| 0.2639 | 6.2913 | 810 | 0.1842 | 0.9453 | 0.9432 | 0.9463 | 0.9438 |
|
| 165 |
+
| 0.2696 | 6.3689 | 820 | 0.1812 | 0.9424 | 0.9406 | 0.9425 | 0.9412 |
|
| 166 |
+
| 0.2524 | 6.4466 | 830 | 0.1830 | 0.9427 | 0.9411 | 0.9433 | 0.9417 |
|
| 167 |
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| 0.2673 | 6.5243 | 840 | 0.1823 | 0.9451 | 0.9436 | 0.9464 | 0.9442 |
|
| 168 |
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| 0.2991 | 6.6019 | 850 | 0.1837 | 0.9429 | 0.9408 | 0.9431 | 0.9419 |
|
| 169 |
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| 0.2704 | 6.6796 | 860 | 0.1833 | 0.9439 | 0.9424 | 0.9446 | 0.9431 |
|
| 170 |
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| 0.2437 | 6.7573 | 870 | 0.1857 | 0.9424 | 0.9410 | 0.9434 | 0.9416 |
|
| 171 |
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| 0.2266 | 6.8350 | 880 | 0.1846 | 0.9431 | 0.9416 | 0.9436 | 0.9423 |
|
| 172 |
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| 0.2276 | 6.9126 | 890 | 0.1825 | 0.9441 | 0.9426 | 0.9448 | 0.9433 |
|
| 173 |
+
| 0.2249 | 6.9903 | 900 | 0.1813 | 0.9436 | 0.9419 | 0.9441 | 0.9425 |
|
| 174 |
+
| 0.2559 | 7.0680 | 910 | 0.1813 | 0.9444 | 0.9425 | 0.9448 | 0.9431 |
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| 175 |
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| 0.2616 | 7.1456 | 920 | 0.1813 | 0.9441 | 0.9421 | 0.9443 | 0.9428 |
|
| 176 |
+
| 0.2247 | 7.2233 | 930 | 0.1813 | 0.9439 | 0.9421 | 0.9442 | 0.9426 |
|
| 177 |
+
| 0.2471 | 7.3010 | 940 | 0.1813 | 0.9448 | 0.9430 | 0.9453 | 0.9436 |
|
| 178 |
+
| 0.2446 | 7.3786 | 950 | 0.1817 | 0.9444 | 0.9427 | 0.9450 | 0.9432 |
|
| 179 |
+
| 0.2262 | 7.4563 | 960 | 0.1819 | 0.9434 | 0.9417 | 0.9439 | 0.9423 |
|
| 180 |
+
| 0.2632 | 7.5340 | 970 | 0.1818 | 0.9439 | 0.9422 | 0.9444 | 0.9427 |
|
| 181 |
+
| 0.2258 | 7.6117 | 980 | 0.1815 | 0.9434 | 0.9416 | 0.9439 | 0.9422 |
|
| 182 |
+
| 0.2404 | 7.6893 | 990 | 0.1811 | 0.9429 | 0.9410 | 0.9432 | 0.9416 |
|
| 183 |
+
| 0.2379 | 7.7670 | 1000 | 0.1810 | 0.9429 | 0.9411 | 0.9432 | 0.9417 |
|
| 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 +1,303 @@
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "microsoft/swinv2-base-patch4-window16-256",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"Swinv2ForImageClassification"
|
| 5 |
+
],
|
| 6 |
+
"attention_probs_dropout_prob": 0.0,
|
| 7 |
+
"depths": [
|
| 8 |
+
2,
|
| 9 |
+
2,
|
| 10 |
+
18,
|
| 11 |
+
2
|
| 12 |
+
],
|
| 13 |
+
"drop_path_rate": 0.1,
|
| 14 |
+
"embed_dim": 128,
|
| 15 |
+
"encoder_stride": 32,
|
| 16 |
+
"hidden_act": "gelu",
|
| 17 |
+
"hidden_dropout_prob": 0.0,
|
| 18 |
+
"hidden_size": 1024,
|
| 19 |
+
"id2label": {
|
| 20 |
+
"0": "Affenpinscher",
|
| 21 |
+
"1": "Afghan Hound",
|
| 22 |
+
"2": "African Hunting Dog",
|
| 23 |
+
"3": "Airedale",
|
| 24 |
+
"4": "American Staffordshire Terrier",
|
| 25 |
+
"5": "Appenzeller",
|
| 26 |
+
"6": "Australian Terrier",
|
| 27 |
+
"7": "Basenji",
|
| 28 |
+
"8": "Basset",
|
| 29 |
+
"9": "Beagle",
|
| 30 |
+
"10": "Bedlington Terrier",
|
| 31 |
+
"11": "Bernese Mountain Dog",
|
| 32 |
+
"12": "Black And Tan Coonhound",
|
| 33 |
+
"13": "Blenheim Spaniel",
|
| 34 |
+
"14": "Bloodhound",
|
| 35 |
+
"15": "Bluetick",
|
| 36 |
+
"16": "Border Collie",
|
| 37 |
+
"17": "Border Terrier",
|
| 38 |
+
"18": "Borzoi",
|
| 39 |
+
"19": "Boston Bull",
|
| 40 |
+
"20": "Bouvier Des Flandres",
|
| 41 |
+
"21": "Boxer",
|
| 42 |
+
"22": "Brabancon Griffon",
|
| 43 |
+
"23": "Briard",
|
| 44 |
+
"24": "Brittany Spaniel",
|
| 45 |
+
"25": "Bull Mastiff",
|
| 46 |
+
"26": "Cairn",
|
| 47 |
+
"27": "Cardigan",
|
| 48 |
+
"28": "Chesapeake Bay Retriever",
|
| 49 |
+
"29": "Chihuahua",
|
| 50 |
+
"30": "Chow",
|
| 51 |
+
"31": "Clumber",
|
| 52 |
+
"32": "Cocker Spaniel",
|
| 53 |
+
"33": "Collie",
|
| 54 |
+
"34": "Curly Coated Retriever",
|
| 55 |
+
"35": "Dandie Dinmont",
|
| 56 |
+
"36": "Dhole",
|
| 57 |
+
"37": "Dingo",
|
| 58 |
+
"38": "Doberman",
|
| 59 |
+
"39": "English Foxhound",
|
| 60 |
+
"40": "English Setter",
|
| 61 |
+
"41": "English Springer",
|
| 62 |
+
"42": "Entlebucher",
|
| 63 |
+
"43": "Eskimo Dog",
|
| 64 |
+
"44": "Flat Coated Retriever",
|
| 65 |
+
"45": "French Bulldog",
|
| 66 |
+
"46": "German Shepherd",
|
| 67 |
+
"47": "German Short Haired Pointer",
|
| 68 |
+
"48": "Giant Schnauzer",
|
| 69 |
+
"49": "Golden Retriever",
|
| 70 |
+
"50": "Gordon Setter",
|
| 71 |
+
"51": "Great Dane",
|
| 72 |
+
"52": "Great Pyrenees",
|
| 73 |
+
"53": "Greater Swiss Mountain Dog",
|
| 74 |
+
"54": "Groenendael",
|
| 75 |
+
"55": "Ibizan Hound",
|
| 76 |
+
"56": "Irish Setter",
|
| 77 |
+
"57": "Irish Terrier",
|
| 78 |
+
"58": "Irish Water Spaniel",
|
| 79 |
+
"59": "Irish Wolfhound",
|
| 80 |
+
"60": "Italian Greyhound",
|
| 81 |
+
"61": "Japanese Spaniel",
|
| 82 |
+
"62": "Keeshond",
|
| 83 |
+
"63": "Kelpie",
|
| 84 |
+
"64": "Kerry Blue Terrier",
|
| 85 |
+
"65": "Komondor",
|
| 86 |
+
"66": "Kuvasz",
|
| 87 |
+
"67": "Labrador Retriever",
|
| 88 |
+
"68": "Lakeland Terrier",
|
| 89 |
+
"69": "Leonberg",
|
| 90 |
+
"70": "Lhasa",
|
| 91 |
+
"71": "Malamute",
|
| 92 |
+
"72": "Malinois",
|
| 93 |
+
"73": "Maltese Dog",
|
| 94 |
+
"74": "Mexican Hairless",
|
| 95 |
+
"75": "Miniature Pinscher",
|
| 96 |
+
"76": "Miniature Poodle",
|
| 97 |
+
"77": "Miniature Schnauzer",
|
| 98 |
+
"78": "Newfoundland",
|
| 99 |
+
"79": "Norfolk Terrier",
|
| 100 |
+
"80": "Norwegian Elkhound",
|
| 101 |
+
"81": "Norwich Terrier",
|
| 102 |
+
"82": "Old English Sheepdog",
|
| 103 |
+
"83": "Otterhound",
|
| 104 |
+
"84": "Papillon",
|
| 105 |
+
"85": "Pekinese",
|
| 106 |
+
"86": "Pembroke",
|
| 107 |
+
"87": "Pomeranian",
|
| 108 |
+
"88": "Pug",
|
| 109 |
+
"89": "Redbone",
|
| 110 |
+
"90": "Rhodesian Ridgeback",
|
| 111 |
+
"91": "Rottweiler",
|
| 112 |
+
"92": "Saint Bernard",
|
| 113 |
+
"93": "Saluki",
|
| 114 |
+
"94": "Samoyed",
|
| 115 |
+
"95": "Schipperke",
|
| 116 |
+
"96": "Scotch Terrier",
|
| 117 |
+
"97": "Scottish Deerhound",
|
| 118 |
+
"98": "Sealyham Terrier",
|
| 119 |
+
"99": "Shetland Sheepdog",
|
| 120 |
+
"100": "Shih Tzu",
|
| 121 |
+
"101": "Siberian Husky",
|
| 122 |
+
"102": "Silky Terrier",
|
| 123 |
+
"103": "Soft Coated Wheaten Terrier",
|
| 124 |
+
"104": "Staffordshire Bullterrier",
|
| 125 |
+
"105": "Standard Poodle",
|
| 126 |
+
"106": "Standard Schnauzer",
|
| 127 |
+
"107": "Sussex Spaniel",
|
| 128 |
+
"108": "Tibetan Mastiff",
|
| 129 |
+
"109": "Tibetan Terrier",
|
| 130 |
+
"110": "Toy Poodle",
|
| 131 |
+
"111": "Toy Terrier",
|
| 132 |
+
"112": "Vizsla",
|
| 133 |
+
"113": "Walker Hound",
|
| 134 |
+
"114": "Weimaraner",
|
| 135 |
+
"115": "Welsh Springer Spaniel",
|
| 136 |
+
"116": "West Highland White Terrier",
|
| 137 |
+
"117": "Whippet",
|
| 138 |
+
"118": "Wire Haired Fox Terrier",
|
| 139 |
+
"119": "Yorkshire Terrier"
|
| 140 |
+
},
|
| 141 |
+
"image_size": 256,
|
| 142 |
+
"initializer_range": 0.02,
|
| 143 |
+
"label2id": {
|
| 144 |
+
"Affenpinscher": 0,
|
| 145 |
+
"Afghan Hound": 1,
|
| 146 |
+
"African Hunting Dog": 2,
|
| 147 |
+
"Airedale": 3,
|
| 148 |
+
"American Staffordshire Terrier": 4,
|
| 149 |
+
"Appenzeller": 5,
|
| 150 |
+
"Australian Terrier": 6,
|
| 151 |
+
"Basenji": 7,
|
| 152 |
+
"Basset": 8,
|
| 153 |
+
"Beagle": 9,
|
| 154 |
+
"Bedlington Terrier": 10,
|
| 155 |
+
"Bernese Mountain Dog": 11,
|
| 156 |
+
"Black And Tan Coonhound": 12,
|
| 157 |
+
"Blenheim Spaniel": 13,
|
| 158 |
+
"Bloodhound": 14,
|
| 159 |
+
"Bluetick": 15,
|
| 160 |
+
"Border Collie": 16,
|
| 161 |
+
"Border Terrier": 17,
|
| 162 |
+
"Borzoi": 18,
|
| 163 |
+
"Boston Bull": 19,
|
| 164 |
+
"Bouvier Des Flandres": 20,
|
| 165 |
+
"Boxer": 21,
|
| 166 |
+
"Brabancon Griffon": 22,
|
| 167 |
+
"Briard": 23,
|
| 168 |
+
"Brittany Spaniel": 24,
|
| 169 |
+
"Bull Mastiff": 25,
|
| 170 |
+
"Cairn": 26,
|
| 171 |
+
"Cardigan": 27,
|
| 172 |
+
"Chesapeake Bay Retriever": 28,
|
| 173 |
+
"Chihuahua": 29,
|
| 174 |
+
"Chow": 30,
|
| 175 |
+
"Clumber": 31,
|
| 176 |
+
"Cocker Spaniel": 32,
|
| 177 |
+
"Collie": 33,
|
| 178 |
+
"Curly Coated Retriever": 34,
|
| 179 |
+
"Dandie Dinmont": 35,
|
| 180 |
+
"Dhole": 36,
|
| 181 |
+
"Dingo": 37,
|
| 182 |
+
"Doberman": 38,
|
| 183 |
+
"English Foxhound": 39,
|
| 184 |
+
"English Setter": 40,
|
| 185 |
+
"English Springer": 41,
|
| 186 |
+
"Entlebucher": 42,
|
| 187 |
+
"Eskimo Dog": 43,
|
| 188 |
+
"Flat Coated Retriever": 44,
|
| 189 |
+
"French Bulldog": 45,
|
| 190 |
+
"German Shepherd": 46,
|
| 191 |
+
"German Short Haired Pointer": 47,
|
| 192 |
+
"Giant Schnauzer": 48,
|
| 193 |
+
"Golden Retriever": 49,
|
| 194 |
+
"Gordon Setter": 50,
|
| 195 |
+
"Great Dane": 51,
|
| 196 |
+
"Great Pyrenees": 52,
|
| 197 |
+
"Greater Swiss Mountain Dog": 53,
|
| 198 |
+
"Groenendael": 54,
|
| 199 |
+
"Ibizan Hound": 55,
|
| 200 |
+
"Irish Setter": 56,
|
| 201 |
+
"Irish Terrier": 57,
|
| 202 |
+
"Irish Water Spaniel": 58,
|
| 203 |
+
"Irish Wolfhound": 59,
|
| 204 |
+
"Italian Greyhound": 60,
|
| 205 |
+
"Japanese Spaniel": 61,
|
| 206 |
+
"Keeshond": 62,
|
| 207 |
+
"Kelpie": 63,
|
| 208 |
+
"Kerry Blue Terrier": 64,
|
| 209 |
+
"Komondor": 65,
|
| 210 |
+
"Kuvasz": 66,
|
| 211 |
+
"Labrador Retriever": 67,
|
| 212 |
+
"Lakeland Terrier": 68,
|
| 213 |
+
"Leonberg": 69,
|
| 214 |
+
"Lhasa": 70,
|
| 215 |
+
"Malamute": 71,
|
| 216 |
+
"Malinois": 72,
|
| 217 |
+
"Maltese Dog": 73,
|
| 218 |
+
"Mexican Hairless": 74,
|
| 219 |
+
"Miniature Pinscher": 75,
|
| 220 |
+
"Miniature Poodle": 76,
|
| 221 |
+
"Miniature Schnauzer": 77,
|
| 222 |
+
"Newfoundland": 78,
|
| 223 |
+
"Norfolk Terrier": 79,
|
| 224 |
+
"Norwegian Elkhound": 80,
|
| 225 |
+
"Norwich Terrier": 81,
|
| 226 |
+
"Old English Sheepdog": 82,
|
| 227 |
+
"Otterhound": 83,
|
| 228 |
+
"Papillon": 84,
|
| 229 |
+
"Pekinese": 85,
|
| 230 |
+
"Pembroke": 86,
|
| 231 |
+
"Pomeranian": 87,
|
| 232 |
+
"Pug": 88,
|
| 233 |
+
"Redbone": 89,
|
| 234 |
+
"Rhodesian Ridgeback": 90,
|
| 235 |
+
"Rottweiler": 91,
|
| 236 |
+
"Saint Bernard": 92,
|
| 237 |
+
"Saluki": 93,
|
| 238 |
+
"Samoyed": 94,
|
| 239 |
+
"Schipperke": 95,
|
| 240 |
+
"Scotch Terrier": 96,
|
| 241 |
+
"Scottish Deerhound": 97,
|
| 242 |
+
"Sealyham Terrier": 98,
|
| 243 |
+
"Shetland Sheepdog": 99,
|
| 244 |
+
"Shih Tzu": 100,
|
| 245 |
+
"Siberian Husky": 101,
|
| 246 |
+
"Silky Terrier": 102,
|
| 247 |
+
"Soft Coated Wheaten Terrier": 103,
|
| 248 |
+
"Staffordshire Bullterrier": 104,
|
| 249 |
+
"Standard Poodle": 105,
|
| 250 |
+
"Standard Schnauzer": 106,
|
| 251 |
+
"Sussex Spaniel": 107,
|
| 252 |
+
"Tibetan Mastiff": 108,
|
| 253 |
+
"Tibetan Terrier": 109,
|
| 254 |
+
"Toy Poodle": 110,
|
| 255 |
+
"Toy Terrier": 111,
|
| 256 |
+
"Vizsla": 112,
|
| 257 |
+
"Walker Hound": 113,
|
| 258 |
+
"Weimaraner": 114,
|
| 259 |
+
"Welsh Springer Spaniel": 115,
|
| 260 |
+
"West Highland White Terrier": 116,
|
| 261 |
+
"Whippet": 117,
|
| 262 |
+
"Wire Haired Fox Terrier": 118,
|
| 263 |
+
"Yorkshire Terrier": 119
|
| 264 |
+
},
|
| 265 |
+
"layer_norm_eps": 1e-05,
|
| 266 |
+
"mlp_ratio": 4.0,
|
| 267 |
+
"model_type": "swinv2",
|
| 268 |
+
"num_channels": 3,
|
| 269 |
+
"num_heads": [
|
| 270 |
+
4,
|
| 271 |
+
8,
|
| 272 |
+
16,
|
| 273 |
+
32
|
| 274 |
+
],
|
| 275 |
+
"num_layers": 4,
|
| 276 |
+
"out_features": [
|
| 277 |
+
"stage4"
|
| 278 |
+
],
|
| 279 |
+
"out_indices": [
|
| 280 |
+
4
|
| 281 |
+
],
|
| 282 |
+
"patch_size": 4,
|
| 283 |
+
"path_norm": true,
|
| 284 |
+
"pretrained_window_sizes": [
|
| 285 |
+
0,
|
| 286 |
+
0,
|
| 287 |
+
0,
|
| 288 |
+
0
|
| 289 |
+
],
|
| 290 |
+
"problem_type": "single_label_classification",
|
| 291 |
+
"qkv_bias": true,
|
| 292 |
+
"stage_names": [
|
| 293 |
+
"stem",
|
| 294 |
+
"stage1",
|
| 295 |
+
"stage2",
|
| 296 |
+
"stage3",
|
| 297 |
+
"stage4"
|
| 298 |
+
],
|
| 299 |
+
"torch_dtype": "float32",
|
| 300 |
+
"transformers_version": "4.40.2",
|
| 301 |
+
"use_absolute_embeddings": false,
|
| 302 |
+
"window_size": 16
|
| 303 |
+
}
|
config.toml
ADDED
|
@@ -0,0 +1,27 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 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:bfa4144ca46aaf446d9067e8b320acfe81723f93d0a51fab6f4f3824a1306e92
|
| 3 |
+
size 348129336
|
preprocessor_config.json
ADDED
|
@@ -0,0 +1,36 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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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 |
+
0.485,
|
| 21 |
+
0.456,
|
| 22 |
+
0.406
|
| 23 |
+
],
|
| 24 |
+
"image_processor_type": "ViTImageProcessor",
|
| 25 |
+
"image_std": [
|
| 26 |
+
0.229,
|
| 27 |
+
0.224,
|
| 28 |
+
0.225
|
| 29 |
+
],
|
| 30 |
+
"resample": 3,
|
| 31 |
+
"rescale_factor": 0.00392156862745098,
|
| 32 |
+
"size": {
|
| 33 |
+
"height": 256,
|
| 34 |
+
"width": 256
|
| 35 |
+
}
|
| 36 |
+
}
|
train.ipynb
ADDED
|
@@ -0,0 +1,2084 @@
|
|
|
|
|
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|
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|
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|
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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": null,
|
| 13 |
+
"metadata": {},
|
| 14 |
+
"outputs": [],
|
| 15 |
+
"source": [
|
| 16 |
+
"%pip install uv"
|
| 17 |
+
]
|
| 18 |
+
},
|
| 19 |
+
{
|
| 20 |
+
"cell_type": "code",
|
| 21 |
+
"execution_count": null,
|
| 22 |
+
"metadata": {},
|
| 23 |
+
"outputs": [],
|
| 24 |
+
"source": [
|
| 25 |
+
"!uv pip install dagshub setuptools accelerate toml torch torchvision transformers mlflow datasets ipywidgets python-dotenv evaluate"
|
| 26 |
+
]
|
| 27 |
+
},
|
| 28 |
+
{
|
| 29 |
+
"cell_type": "markdown",
|
| 30 |
+
"metadata": {},
|
| 31 |
+
"source": [
|
| 32 |
+
"# Setup"
|
| 33 |
+
]
|
| 34 |
+
},
|
| 35 |
+
{
|
| 36 |
+
"cell_type": "code",
|
| 37 |
+
"execution_count": 1,
|
| 38 |
+
"metadata": {},
|
| 39 |
+
"outputs": [
|
| 40 |
+
{
|
| 41 |
+
"data": {
|
| 42 |
+
"text/html": [
|
| 43 |
+
"<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",
|
| 44 |
+
"</pre>\n"
|
| 45 |
+
],
|
| 46 |
+
"text/plain": [
|
| 47 |
+
"Initialized MLflow to track repo \u001b[32m\"amaye15/CanineNet\"\u001b[0m\n"
|
| 48 |
+
]
|
| 49 |
+
},
|
| 50 |
+
"metadata": {},
|
| 51 |
+
"output_type": "display_data"
|
| 52 |
+
},
|
| 53 |
+
{
|
| 54 |
+
"data": {
|
| 55 |
+
"text/html": [
|
| 56 |
+
"<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",
|
| 57 |
+
"</pre>\n"
|
| 58 |
+
],
|
| 59 |
+
"text/plain": [
|
| 60 |
+
"Repository amaye15/CanineNet initialized!\n"
|
| 61 |
+
]
|
| 62 |
+
},
|
| 63 |
+
"metadata": {},
|
| 64 |
+
"output_type": "display_data"
|
| 65 |
+
}
|
| 66 |
+
],
|
| 67 |
+
"source": [
|
| 68 |
+
"import os\n",
|
| 69 |
+
"import toml\n",
|
| 70 |
+
"import torch\n",
|
| 71 |
+
"import mlflow\n",
|
| 72 |
+
"import dagshub\n",
|
| 73 |
+
"import datasets\n",
|
| 74 |
+
"import evaluate\n",
|
| 75 |
+
"from dotenv import load_dotenv\n",
|
| 76 |
+
"from torchvision.transforms import v2\n",
|
| 77 |
+
"from transformers import AutoImageProcessor, AutoModelForImageClassification, TrainingArguments, Trainer\n",
|
| 78 |
+
"\n",
|
| 79 |
+
"ENV_PATH = \"/Users/andrewmayes/Openclassroom/CanineNet/.env\"\n",
|
| 80 |
+
"CONFIG_PATH = \"/Users/andrewmayes/Openclassroom/CanineNet/code/config.toml\"\n",
|
| 81 |
+
"CONFIG = toml.load(CONFIG_PATH)\n",
|
| 82 |
+
"\n",
|
| 83 |
+
"load_dotenv(ENV_PATH)\n",
|
| 84 |
+
"\n",
|
| 85 |
+
"dagshub.init(repo_name=os.environ['MLFLOW_TRACKING_PROJECTNAME'], repo_owner=os.environ['MLFLOW_TRACKING_USERNAME'], mlflow=True, dvc=True)\n",
|
| 86 |
+
"\n",
|
| 87 |
+
"os.environ['MLFLOW_TRACKING_USERNAME'] = \"amaye15\"\n",
|
| 88 |
+
"\n",
|
| 89 |
+
"mlflow.set_tracking_uri(f'https://dagshub.com/' + os.environ['MLFLOW_TRACKING_USERNAME']\n",
|
| 90 |
+
" + '/' + os.environ['MLFLOW_TRACKING_PROJECTNAME'] + '.mlflow')\n",
|
| 91 |
+
"\n",
|
| 92 |
+
"CREATE_DATASET = True\n",
|
| 93 |
+
"ORIGINAL_DATASET = \"Alanox/stanford-dogs\"\n",
|
| 94 |
+
"MODIFIED_DATASET = \"amaye15/stanford-dogs\"\n",
|
| 95 |
+
"REMOVE_COLUMNS = [\"name\", \"annotations\"]\n",
|
| 96 |
+
"RENAME_COLUMNS = {\"image\":\"pixel_values\", \"target\":\"label\"}\n",
|
| 97 |
+
"SPLIT = 0.2\n",
|
| 98 |
+
"\n",
|
| 99 |
+
"METRICS = [\"accuracy\", \"f1\", \"precision\", \"recall\"]\n",
|
| 100 |
+
"# MODELS = 'google/vit-base-patch16-224'\n",
|
| 101 |
+
"# MODELS = \"google/siglip-base-patch16-224\"\n",
|
| 102 |
+
"\n"
|
| 103 |
+
]
|
| 104 |
+
},
|
| 105 |
+
{
|
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+
"cell_type": "markdown",
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"metadata": {},
|
| 108 |
+
"source": [
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+
"# Dataset"
|
| 110 |
+
]
|
| 111 |
+
},
|
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+
{
|
| 113 |
+
"cell_type": "code",
|
| 114 |
+
"execution_count": 2,
|
| 115 |
+
"metadata": {},
|
| 116 |
+
"outputs": [
|
| 117 |
+
{
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| 118 |
+
"name": "stdout",
|
| 119 |
+
"output_type": "stream",
|
| 120 |
+
"text": [
|
| 121 |
+
"Affenpinscher: 0\n",
|
| 122 |
+
"Afghan Hound: 1\n",
|
| 123 |
+
"African Hunting Dog: 2\n",
|
| 124 |
+
"Airedale: 3\n",
|
| 125 |
+
"American Staffordshire Terrier: 4\n",
|
| 126 |
+
"Appenzeller: 5\n",
|
| 127 |
+
"Australian Terrier: 6\n",
|
| 128 |
+
"Basenji: 7\n",
|
| 129 |
+
"Basset: 8\n",
|
| 130 |
+
"Beagle: 9\n",
|
| 131 |
+
"Bedlington Terrier: 10\n",
|
| 132 |
+
"Bernese Mountain Dog: 11\n",
|
| 133 |
+
"Black And Tan Coonhound: 12\n",
|
| 134 |
+
"Blenheim Spaniel: 13\n",
|
| 135 |
+
"Bloodhound: 14\n",
|
| 136 |
+
"Bluetick: 15\n",
|
| 137 |
+
"Border Collie: 16\n",
|
| 138 |
+
"Border Terrier: 17\n",
|
| 139 |
+
"Borzoi: 18\n",
|
| 140 |
+
"Boston Bull: 19\n",
|
| 141 |
+
"Bouvier Des Flandres: 20\n",
|
| 142 |
+
"Boxer: 21\n",
|
| 143 |
+
"Brabancon Griffon: 22\n",
|
| 144 |
+
"Briard: 23\n",
|
| 145 |
+
"Brittany Spaniel: 24\n",
|
| 146 |
+
"Bull Mastiff: 25\n",
|
| 147 |
+
"Cairn: 26\n",
|
| 148 |
+
"Cardigan: 27\n",
|
| 149 |
+
"Chesapeake Bay Retriever: 28\n",
|
| 150 |
+
"Chihuahua: 29\n",
|
| 151 |
+
"Chow: 30\n",
|
| 152 |
+
"Clumber: 31\n",
|
| 153 |
+
"Cocker Spaniel: 32\n",
|
| 154 |
+
"Collie: 33\n",
|
| 155 |
+
"Curly Coated Retriever: 34\n",
|
| 156 |
+
"Dandie Dinmont: 35\n",
|
| 157 |
+
"Dhole: 36\n",
|
| 158 |
+
"Dingo: 37\n",
|
| 159 |
+
"Doberman: 38\n",
|
| 160 |
+
"English Foxhound: 39\n",
|
| 161 |
+
"English Setter: 40\n",
|
| 162 |
+
"English Springer: 41\n",
|
| 163 |
+
"Entlebucher: 42\n",
|
| 164 |
+
"Eskimo Dog: 43\n",
|
| 165 |
+
"Flat Coated Retriever: 44\n",
|
| 166 |
+
"French Bulldog: 45\n",
|
| 167 |
+
"German Shepherd: 46\n",
|
| 168 |
+
"German Short Haired Pointer: 47\n",
|
| 169 |
+
"Giant Schnauzer: 48\n",
|
| 170 |
+
"Golden Retriever: 49\n",
|
| 171 |
+
"Gordon Setter: 50\n",
|
| 172 |
+
"Great Dane: 51\n",
|
| 173 |
+
"Great Pyrenees: 52\n",
|
| 174 |
+
"Greater Swiss Mountain Dog: 53\n",
|
| 175 |
+
"Groenendael: 54\n",
|
| 176 |
+
"Ibizan Hound: 55\n",
|
| 177 |
+
"Irish Setter: 56\n",
|
| 178 |
+
"Irish Terrier: 57\n",
|
| 179 |
+
"Irish Water Spaniel: 58\n",
|
| 180 |
+
"Irish Wolfhound: 59\n",
|
| 181 |
+
"Italian Greyhound: 60\n",
|
| 182 |
+
"Japanese Spaniel: 61\n",
|
| 183 |
+
"Keeshond: 62\n",
|
| 184 |
+
"Kelpie: 63\n",
|
| 185 |
+
"Kerry Blue Terrier: 64\n",
|
| 186 |
+
"Komondor: 65\n",
|
| 187 |
+
"Kuvasz: 66\n",
|
| 188 |
+
"Labrador Retriever: 67\n",
|
| 189 |
+
"Lakeland Terrier: 68\n",
|
| 190 |
+
"Leonberg: 69\n",
|
| 191 |
+
"Lhasa: 70\n",
|
| 192 |
+
"Malamute: 71\n",
|
| 193 |
+
"Malinois: 72\n",
|
| 194 |
+
"Maltese Dog: 73\n",
|
| 195 |
+
"Mexican Hairless: 74\n",
|
| 196 |
+
"Miniature Pinscher: 75\n",
|
| 197 |
+
"Miniature Poodle: 76\n",
|
| 198 |
+
"Miniature Schnauzer: 77\n",
|
| 199 |
+
"Newfoundland: 78\n",
|
| 200 |
+
"Norfolk Terrier: 79\n",
|
| 201 |
+
"Norwegian Elkhound: 80\n",
|
| 202 |
+
"Norwich Terrier: 81\n",
|
| 203 |
+
"Old English Sheepdog: 82\n",
|
| 204 |
+
"Otterhound: 83\n",
|
| 205 |
+
"Papillon: 84\n",
|
| 206 |
+
"Pekinese: 85\n",
|
| 207 |
+
"Pembroke: 86\n",
|
| 208 |
+
"Pomeranian: 87\n",
|
| 209 |
+
"Pug: 88\n",
|
| 210 |
+
"Redbone: 89\n",
|
| 211 |
+
"Rhodesian Ridgeback: 90\n",
|
| 212 |
+
"Rottweiler: 91\n",
|
| 213 |
+
"Saint Bernard: 92\n",
|
| 214 |
+
"Saluki: 93\n",
|
| 215 |
+
"Samoyed: 94\n",
|
| 216 |
+
"Schipperke: 95\n",
|
| 217 |
+
"Scotch Terrier: 96\n",
|
| 218 |
+
"Scottish Deerhound: 97\n",
|
| 219 |
+
"Sealyham Terrier: 98\n",
|
| 220 |
+
"Shetland Sheepdog: 99\n",
|
| 221 |
+
"Shih Tzu: 100\n",
|
| 222 |
+
"Siberian Husky: 101\n",
|
| 223 |
+
"Silky Terrier: 102\n",
|
| 224 |
+
"Soft Coated Wheaten Terrier: 103\n",
|
| 225 |
+
"Staffordshire Bullterrier: 104\n",
|
| 226 |
+
"Standard Poodle: 105\n",
|
| 227 |
+
"Standard Schnauzer: 106\n",
|
| 228 |
+
"Sussex Spaniel: 107\n",
|
| 229 |
+
"Tibetan Mastiff: 108\n",
|
| 230 |
+
"Tibetan Terrier: 109\n",
|
| 231 |
+
"Toy Poodle: 110\n",
|
| 232 |
+
"Toy Terrier: 111\n",
|
| 233 |
+
"Vizsla: 112\n",
|
| 234 |
+
"Walker Hound: 113\n",
|
| 235 |
+
"Weimaraner: 114\n",
|
| 236 |
+
"Welsh Springer Spaniel: 115\n",
|
| 237 |
+
"West Highland White Terrier: 116\n",
|
| 238 |
+
"Whippet: 117\n",
|
| 239 |
+
"Wire Haired Fox Terrier: 118\n",
|
| 240 |
+
"Yorkshire Terrier: 119\n"
|
| 241 |
+
]
|
| 242 |
+
}
|
| 243 |
+
],
|
| 244 |
+
"source": [
|
| 245 |
+
"if CREATE_DATASET:\n",
|
| 246 |
+
" ds = datasets.load_dataset(ORIGINAL_DATASET, token=os.getenv(\"HF_TOKEN\"), split=\"full\", trust_remote_code=True)\n",
|
| 247 |
+
" ds = ds.remove_columns(REMOVE_COLUMNS).rename_columns(RENAME_COLUMNS)\n",
|
| 248 |
+
"\n",
|
| 249 |
+
" labels = ds.select_columns(\"label\").to_pandas().sort_values(\"label\").get(\"label\").unique().tolist()\n",
|
| 250 |
+
" numbers = range(len(labels))\n",
|
| 251 |
+
" label2int = dict(zip(labels, numbers))\n",
|
| 252 |
+
" int2label = dict(zip(numbers, labels))\n",
|
| 253 |
+
"\n",
|
| 254 |
+
" for key, val in label2int.items():\n",
|
| 255 |
+
" print(f\"{key}: {val}\")\n",
|
| 256 |
+
"\n",
|
| 257 |
+
" ds = ds.class_encode_column(\"label\")\n",
|
| 258 |
+
" ds = ds.align_labels_with_mapping(label2int, \"label\")\n",
|
| 259 |
+
"\n",
|
| 260 |
+
" ds = ds.train_test_split(test_size=SPLIT, stratify_by_column = \"label\")\n",
|
| 261 |
+
" #ds.push_to_hub(MODIFIED_DATASET, token=os.getenv(\"HF_TOKEN\"))\n",
|
| 262 |
+
"\n",
|
| 263 |
+
" CONFIG[\"label2int\"] = str(label2int)\n",
|
| 264 |
+
" CONFIG[\"int2label\"] = str(int2label)\n",
|
| 265 |
+
"\n",
|
| 266 |
+
" # with open(\"output.toml\", \"w\") as toml_file:\n",
|
| 267 |
+
" # toml.dump(toml.dumps(CONFIG), toml_file)\n",
|
| 268 |
+
"\n",
|
| 269 |
+
" #ds = datasets.load_dataset(MODIFIED_DATASET, token=os.getenv(\"HF_TOKEN\"), trust_remote_code=True, streaming=True)"
|
| 270 |
+
]
|
| 271 |
+
},
|
| 272 |
+
{
|
| 273 |
+
"cell_type": "code",
|
| 274 |
+
"execution_count": 3,
|
| 275 |
+
"metadata": {},
|
| 276 |
+
"outputs": [
|
| 277 |
+
{
|
| 278 |
+
"name": "stderr",
|
| 279 |
+
"output_type": "stream",
|
| 280 |
+
"text": [
|
| 281 |
+
"/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",
|
| 282 |
+
" warnings.warn(\n",
|
| 283 |
+
"Could not find image processor class in the image processor config or the model config. Loading based on pattern matching with the model's feature extractor configuration. Please open a PR/issue to update `preprocessor_config.json` to use `image_processor_type` instead of `feature_extractor_type`. This warning will be removed in v4.40.\n",
|
| 284 |
+
"Some weights of Swinv2ForImageClassification were not initialized from the model checkpoint at microsoft/swinv2-base-patch4-window16-256 and are newly initialized because the shapes did not match:\n",
|
| 285 |
+
"- classifier.weight: found shape torch.Size([1000, 1024]) in the checkpoint and torch.Size([120, 1024]) in the model instantiated\n",
|
| 286 |
+
"- classifier.bias: found shape torch.Size([1000]) in the checkpoint and torch.Size([120]) in the model instantiated\n",
|
| 287 |
+
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
|
| 288 |
+
"max_steps is given, it will override any value given in num_train_epochs\n"
|
| 289 |
+
]
|
| 290 |
+
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| 291 |
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|
| 295 |
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"version_major": 2,
|
| 296 |
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|
| 304 |
+
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|
| 305 |
+
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|
| 306 |
+
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|
| 307 |
+
"output_type": "stream",
|
| 308 |
+
"text": [
|
| 309 |
+
"/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",
|
| 310 |
+
" warnings.warn(\n"
|
| 311 |
+
]
|
| 312 |
+
},
|
| 313 |
+
{
|
| 314 |
+
"name": "stdout",
|
| 315 |
+
"output_type": "stream",
|
| 316 |
+
"text": [
|
| 317 |
+
"{'loss': 4.7518, 'grad_norm': 9.390625953674316, 'learning_rate': 4.9500000000000004e-05, 'epoch': 0.08}\n"
|
| 318 |
+
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|
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|
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| 334 |
+
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| 335 |
+
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|
| 336 |
+
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|
| 337 |
+
"text": [
|
| 338 |
+
"/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",
|
| 339 |
+
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
|
| 340 |
+
]
|
| 341 |
+
},
|
| 342 |
+
{
|
| 343 |
+
"name": "stdout",
|
| 344 |
+
"output_type": "stream",
|
| 345 |
+
"text": [
|
| 346 |
+
"{'eval_loss': 4.639115810394287, 'eval_accuracy': 0.07410106899902819, 'eval_f1': 0.053338767481424125, 'eval_precision': 0.06668646345851367, 'eval_recall': 0.07050091579227699, 'eval_runtime': 122.7601, 'eval_samples_per_second': 33.529, 'eval_steps_per_second': 1.051, 'epoch': 0.08}\n"
|
| 347 |
+
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|
| 348 |
+
},
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| 350 |
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|
| 351 |
+
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| 352 |
+
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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",
|
| 354 |
+
" warnings.warn(\n"
|
| 355 |
+
]
|
| 356 |
+
},
|
| 357 |
+
{
|
| 358 |
+
"name": "stdout",
|
| 359 |
+
"output_type": "stream",
|
| 360 |
+
"text": [
|
| 361 |
+
"{'loss': 4.5585, 'grad_norm': 16.583328247070312, 'learning_rate': 4.9e-05, 'epoch': 0.16}\n"
|
| 362 |
+
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|
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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",
|
| 383 |
+
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
|
| 384 |
+
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|
| 385 |
+
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+
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| 387 |
+
"name": "stdout",
|
| 388 |
+
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|
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+
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+
"{'eval_loss': 4.346254348754883, 'eval_accuracy': 0.19193391642371235, 'eval_f1': 0.14445541276201207, 'eval_precision': 0.18999966240710864, 'eval_recall': 0.1794198274691427, 'eval_runtime': 125.4121, 'eval_samples_per_second': 32.82, 'eval_steps_per_second': 1.029, '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",
|
| 398 |
+
" warnings.warn(\n"
|
| 399 |
+
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|
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+
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+
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"text": [
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+
"{'loss': 4.2377, 'grad_norm': 26.891368865966797, 'learning_rate': 4.85e-05, 'epoch': 0.23}\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",
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"{'eval_loss': 0.2970269024372101, 'eval_accuracy': 0.9186103012633625, 'eval_f1': 0.9152438416028247, 'eval_precision': 0.9226596717426415, 'eval_recall': 0.9156450507816004, 'eval_runtime': 118.1418, 'eval_samples_per_second': 34.839, 'eval_steps_per_second': 1.092, 'epoch': 0.85}\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",
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"{'eval_loss': 0.2454349249601364, 'eval_accuracy': 0.9280855199222546, 'eval_f1': 0.922547904522988, 'eval_precision': 0.9361557799152643, 'eval_recall': 0.9256003853911859, 'eval_runtime': 117.6285, 'eval_samples_per_second': 34.992, 'eval_steps_per_second': 1.097, 'epoch': 1.17}\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",
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"{'eval_loss': 0.2481379508972168, 'eval_accuracy': 0.9268707482993197, 'eval_f1': 0.9234504572819081, 'eval_precision': 0.9308003097280835, 'eval_recall': 0.9250780783392921, 'eval_runtime': 118.0847, 'eval_samples_per_second': 34.856, 'eval_steps_per_second': 1.092, 'epoch': 1.24}\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",
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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",
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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",
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"{'loss': 0.3475, 'grad_norm': 34.49732208251953, 'learning_rate': 3.05e-05, 'epoch': 3.03}\n"
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"{'eval_loss': 0.19942405819892883, 'eval_accuracy': 0.9382896015549077, 'eval_f1': 0.9364100359103614, 'eval_precision': 0.9410069422883035, 'eval_recall': 0.9376210497900824, 'eval_runtime': 115.7969, 'eval_samples_per_second': 35.545, 'eval_steps_per_second': 1.114, 'epoch': 3.03}\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",
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"{'eval_loss': 0.1941181868314743, 'eval_accuracy': 0.9390184645286687, 'eval_f1': 0.9351668156232781, 'eval_precision': 0.9402405362825343, 'eval_recall': 0.9372908916182126, 'eval_runtime': 112.8197, 'eval_samples_per_second': 36.483, 'eval_steps_per_second': 1.143, 'epoch': 3.11}\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",
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| 1840 |
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"{'eval_loss': 0.18932673335075378, 'eval_accuracy': 0.9416909620991254, 'eval_f1': 0.940318085045109, 'eval_precision': 0.9438062512440089, 'eval_recall': 0.9409543473093137, 'eval_runtime': 114.6924, 'eval_samples_per_second': 35.887, 'eval_steps_per_second': 1.125, 'epoch': 3.18}\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",
|
| 1850 |
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"text": [
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"{'loss': 0.3549, 'grad_norm': 26.349733352661133, 'learning_rate': 2.9e-05, 'epoch': 3.26}\n"
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| 1876 |
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"text": [
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"{'eval_loss': 0.19598565995693207, 'eval_accuracy': 0.9390184645286687, 'eval_f1': 0.936971760890017, 'eval_precision': 0.9409831330401592, 'eval_recall': 0.938080489222113, 'eval_runtime': 118.5544, 'eval_samples_per_second': 34.718, 'eval_steps_per_second': 1.088, 'epoch': 3.26}\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",
|
| 1886 |
+
" warnings.warn(\n"
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| 1887 |
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]
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| 1888 |
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}
|
| 1889 |
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],
|
| 1890 |
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"source": [
|
| 1891 |
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"metrics = {metric: evaluate.load(metric) for metric in METRICS}\n",
|
| 1892 |
+
"\n",
|
| 1893 |
+
"\n",
|
| 1894 |
+
"# for lr in [5e-3, 5e-4, 5e-5]: # 5e-5\n",
|
| 1895 |
+
"# for batch in [64]: # 32\n",
|
| 1896 |
+
"# for model_name in [\"google/vit-base-patch16-224\", \"microsoft/swinv2-base-patch4-window16-256\", \"google/siglip-base-patch16-224\"]: # \"facebook/dinov2-base\"\n",
|
| 1897 |
+
"\n",
|
| 1898 |
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"lr = 5e-4\n",
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| 1899 |
+
"batch = 32\n",
|
| 1900 |
+
"model_name = \"microsoft/swinv2-base-patch4-window16-256\"\n",
|
| 1901 |
+
"\n",
|
| 1902 |
+
"image_processor = AutoImageProcessor.from_pretrained(model_name)\n",
|
| 1903 |
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"model = AutoModelForImageClassification.from_pretrained(\n",
|
| 1904 |
+
"model_name,\n",
|
| 1905 |
+
"num_labels=len(label2int),\n",
|
| 1906 |
+
"id2label=int2label,\n",
|
| 1907 |
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"label2id=label2int,\n",
|
| 1908 |
+
"ignore_mismatched_sizes=True,\n",
|
| 1909 |
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")\n",
|
| 1910 |
+
"\n",
|
| 1911 |
+
"# Then, in your transformations:\n",
|
| 1912 |
+
"def train_transform(examples, num_ops=10, magnitude=9, num_magnitude_bins=31):\n",
|
| 1913 |
+
"\n",
|
| 1914 |
+
" transformation = v2.Compose(\n",
|
| 1915 |
+
" [\n",
|
| 1916 |
+
" v2.RandAugment(\n",
|
| 1917 |
+
" num_ops=num_ops,\n",
|
| 1918 |
+
" magnitude=magnitude,\n",
|
| 1919 |
+
" num_magnitude_bins=num_magnitude_bins,\n",
|
| 1920 |
+
" )\n",
|
| 1921 |
+
" ]\n",
|
| 1922 |
+
" )\n",
|
| 1923 |
+
" # Ensure each image has three dimensions (in this case, ensure it's RGB)\n",
|
| 1924 |
+
" examples[\"pixel_values\"] = [\n",
|
| 1925 |
+
" image.convert(\"RGB\") for image in examples[\"pixel_values\"]\n",
|
| 1926 |
+
" ]\n",
|
| 1927 |
+
" # Apply transformations\n",
|
| 1928 |
+
" examples[\"pixel_values\"] = [\n",
|
| 1929 |
+
" image_processor(transformation(image), return_tensors=\"pt\")[\n",
|
| 1930 |
+
" \"pixel_values\"\n",
|
| 1931 |
+
" ].squeeze()\n",
|
| 1932 |
+
" for image in examples[\"pixel_values\"]\n",
|
| 1933 |
+
" ]\n",
|
| 1934 |
+
" return examples\n",
|
| 1935 |
+
"\n",
|
| 1936 |
+
"\n",
|
| 1937 |
+
"def test_transform(examples):\n",
|
| 1938 |
+
" # Ensure each image is RGB\n",
|
| 1939 |
+
" examples[\"pixel_values\"] = [\n",
|
| 1940 |
+
" image.convert(\"RGB\") for image in examples[\"pixel_values\"]\n",
|
| 1941 |
+
" ]\n",
|
| 1942 |
+
" # Apply processing\n",
|
| 1943 |
+
" examples[\"pixel_values\"] = [\n",
|
| 1944 |
+
" image_processor(image, return_tensors=\"pt\")[\"pixel_values\"].squeeze()\n",
|
| 1945 |
+
" for image in examples[\"pixel_values\"]\n",
|
| 1946 |
+
" ]\n",
|
| 1947 |
+
" return examples\n",
|
| 1948 |
+
"\n",
|
| 1949 |
+
"\n",
|
| 1950 |
+
"def compute_metrics(eval_pred):\n",
|
| 1951 |
+
" predictions, labels = eval_pred\n",
|
| 1952 |
+
" # predictions = np.argmax(logits, axis=-1)\n",
|
| 1953 |
+
" results = {}\n",
|
| 1954 |
+
" for key, val in metrics.items():\n",
|
| 1955 |
+
" if \"accuracy\" == key:\n",
|
| 1956 |
+
" result = next(\n",
|
| 1957 |
+
" iter(val.compute(predictions=predictions, references=labels).items())\n",
|
| 1958 |
+
" )\n",
|
| 1959 |
+
" if \"accuracy\" != key:\n",
|
| 1960 |
+
" result = next(\n",
|
| 1961 |
+
" iter(\n",
|
| 1962 |
+
" val.compute(\n",
|
| 1963 |
+
" predictions=predictions, references=labels, average=\"macro\"\n",
|
| 1964 |
+
" ).items()\n",
|
| 1965 |
+
" )\n",
|
| 1966 |
+
" )\n",
|
| 1967 |
+
" results[result[0]] = result[1]\n",
|
| 1968 |
+
" return results\n",
|
| 1969 |
+
"\n",
|
| 1970 |
+
"\n",
|
| 1971 |
+
"def collate_fn(examples):\n",
|
| 1972 |
+
" pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n",
|
| 1973 |
+
" labels = torch.tensor([example[\"label\"] for example in examples])\n",
|
| 1974 |
+
" return {\"pixel_values\": pixel_values, \"labels\": labels}\n",
|
| 1975 |
+
"\n",
|
| 1976 |
+
"\n",
|
| 1977 |
+
"def preprocess_logits_for_metrics(logits, labels):\n",
|
| 1978 |
+
" \"\"\"\n",
|
| 1979 |
+
" Original Trainer may have a memory leak.\n",
|
| 1980 |
+
" This is a workaround to avoid storing too many tensors that are not needed.\n",
|
| 1981 |
+
" \"\"\"\n",
|
| 1982 |
+
" pred_ids = torch.argmax(logits, dim=-1)\n",
|
| 1983 |
+
" return pred_ids\n",
|
| 1984 |
+
"\n",
|
| 1985 |
+
"ds[\"train\"].set_transform(train_transform)\n",
|
| 1986 |
+
"ds[\"test\"].set_transform(test_transform)\n",
|
| 1987 |
+
"\n",
|
| 1988 |
+
"training_args = TrainingArguments(**CONFIG[\"training_args\"])\n",
|
| 1989 |
+
"training_args.per_device_train_batch_size = batch\n",
|
| 1990 |
+
"training_args.per_device_eval_batch_size = batch\n",
|
| 1991 |
+
"training_args.hub_model_id = f\"amaye15/{model_name.replace('/','-')}-batch{batch}-lr{lr}-standford-dogs\"\n",
|
| 1992 |
+
"\n",
|
| 1993 |
+
"mlflow.start_run(run_name=f\"{model_name.replace('/','-')}-batch{batch}-lr{lr}\")\n",
|
| 1994 |
+
"\n",
|
| 1995 |
+
"trainer = Trainer(\n",
|
| 1996 |
+
" model=model,\n",
|
| 1997 |
+
" args=training_args,\n",
|
| 1998 |
+
" train_dataset=ds[\"train\"],\n",
|
| 1999 |
+
" eval_dataset=ds[\"test\"],\n",
|
| 2000 |
+
" tokenizer=image_processor,\n",
|
| 2001 |
+
" data_collator=collate_fn,\n",
|
| 2002 |
+
" compute_metrics=compute_metrics,\n",
|
| 2003 |
+
" # callbacks=[early_stopping_callback],\n",
|
| 2004 |
+
" preprocess_logits_for_metrics=preprocess_logits_for_metrics,\n",
|
| 2005 |
+
")\n",
|
| 2006 |
+
"\n",
|
| 2007 |
+
"# Train the model\n",
|
| 2008 |
+
"trainer.train()\n",
|
| 2009 |
+
"\n",
|
| 2010 |
+
"trainer.push_to_hub()\n",
|
| 2011 |
+
"\n",
|
| 2012 |
+
"mlflow.end_run()"
|
| 2013 |
+
]
|
| 2014 |
+
},
|
| 2015 |
+
{
|
| 2016 |
+
"cell_type": "code",
|
| 2017 |
+
"execution_count": null,
|
| 2018 |
+
"metadata": {},
|
| 2019 |
+
"outputs": [],
|
| 2020 |
+
"source": [
|
| 2021 |
+
"mlflow.end_run()"
|
| 2022 |
+
]
|
| 2023 |
+
},
|
| 2024 |
+
{
|
| 2025 |
+
"cell_type": "code",
|
| 2026 |
+
"execution_count": null,
|
| 2027 |
+
"metadata": {},
|
| 2028 |
+
"outputs": [],
|
| 2029 |
+
"source": [
|
| 2030 |
+
"# training_args = TrainingArguments(**CONFIG[\"training_args\"])\n",
|
| 2031 |
+
"\n",
|
| 2032 |
+
"# image_processor = AutoImageProcessor.from_pretrained(MODELS)\n",
|
| 2033 |
+
"# model = AutoModelForImageClassification.from_pretrained(\n",
|
| 2034 |
+
"# MODELS,\n",
|
| 2035 |
+
"# num_labels=len(CONFIG[\"label2int\"]),\n",
|
| 2036 |
+
"# id2label=CONFIG[\"label2int\"],\n",
|
| 2037 |
+
"# label2id=CONFIG[\"int2label\"],\n",
|
| 2038 |
+
"# ignore_mismatched_sizes=True,\n",
|
| 2039 |
+
"# )\n",
|
| 2040 |
+
"\n",
|
| 2041 |
+
"\n",
|
| 2042 |
+
"# training_args = TrainingArguments(**CONFIG[\"training_args\"])\n",
|
| 2043 |
+
"\n",
|
| 2044 |
+
"# trainer = Trainer(\n",
|
| 2045 |
+
"# model=model,\n",
|
| 2046 |
+
"# args=training_args,\n",
|
| 2047 |
+
"# train_dataset=ds[\"train\"],\n",
|
| 2048 |
+
"# eval_dataset=ds[\"test\"],\n",
|
| 2049 |
+
"# tokenizer=image_processor,\n",
|
| 2050 |
+
"# data_collator=collate_fn,\n",
|
| 2051 |
+
"# compute_metrics=compute_metrics,\n",
|
| 2052 |
+
"# # callbacks=[early_stopping_callback],\n",
|
| 2053 |
+
"# preprocess_logits_for_metrics=preprocess_logits_for_metrics,\n",
|
| 2054 |
+
"# )\n",
|
| 2055 |
+
"\n",
|
| 2056 |
+
"# # Train the model\n",
|
| 2057 |
+
"# trainer.train()\n",
|
| 2058 |
+
"\n",
|
| 2059 |
+
"# mlflow.end_run()"
|
| 2060 |
+
]
|
| 2061 |
+
}
|
| 2062 |
+
],
|
| 2063 |
+
"metadata": {
|
| 2064 |
+
"kernelspec": {
|
| 2065 |
+
"display_name": "env",
|
| 2066 |
+
"language": "python",
|
| 2067 |
+
"name": "python3"
|
| 2068 |
+
},
|
| 2069 |
+
"language_info": {
|
| 2070 |
+
"codemirror_mode": {
|
| 2071 |
+
"name": "ipython",
|
| 2072 |
+
"version": 3
|
| 2073 |
+
},
|
| 2074 |
+
"file_extension": ".py",
|
| 2075 |
+
"mimetype": "text/x-python",
|
| 2076 |
+
"name": "python",
|
| 2077 |
+
"nbconvert_exporter": "python",
|
| 2078 |
+
"pygments_lexer": "ipython3",
|
| 2079 |
+
"version": "3.12.3"
|
| 2080 |
+
}
|
| 2081 |
+
},
|
| 2082 |
+
"nbformat": 4,
|
| 2083 |
+
"nbformat_minor": 2
|
| 2084 |
+
}
|
training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b49c485d331c8a810b7deb770daa71926b779ee6ba9b6b950b22ce9530991c6e
|
| 3 |
+
size 5112
|