End of training
Browse files- README.md +191 -0
- config.json +268 -0
- config.toml +27 -0
- model.safetensors +3 -0
- preprocessor_config.json +36 -0
- train.ipynb +0 -0
- training_args.bin +3 -0
README.md
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| 1 |
+
---
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| 2 |
+
license: apache-2.0
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| 3 |
+
base_model: google/vit-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-vit-base-patch16-224-batch32-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 |
+
type: image-classification
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| 19 |
+
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 |
+
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: 0.8865403304178814
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| 29 |
+
- name: F1
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| 30 |
+
type: f1
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| 31 |
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value: 0.8829055367708631
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| 32 |
+
- name: Precision
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| 33 |
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type: precision
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| 34 |
+
value: 0.8892817099907323
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| 35 |
+
- name: Recall
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| 36 |
+
type: recall
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| 37 |
+
value: 0.8836513270735221
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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 |
+
# google-vit-base-patch16-224-batch32-lr5e-05-standford-dogs
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| 44 |
+
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| 45 |
+
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-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: 0.4497
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| 48 |
+
- Accuracy: 0.8865
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| 49 |
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- F1: 0.8829
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| 50 |
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- Precision: 0.8893
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| 51 |
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- Recall: 0.8837
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| 52 |
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| 53 |
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## Model description
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| 54 |
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| 55 |
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More information needed
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| 56 |
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| 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 |
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## Training procedure
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| 66 |
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| 67 |
+
### 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 |
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- seed: 42
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| 74 |
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- gradient_accumulation_steps: 4
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| 75 |
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- total_train_batch_size: 128
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| 76 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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| 77 |
+
- lr_scheduler_type: linear
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| 78 |
+
- training_steps: 1000
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| 79 |
+
|
| 80 |
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### Training results
|
| 81 |
+
|
| 82 |
+
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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| 83 |
+
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
|
| 84 |
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| 4.7916 | 0.0777 | 10 | 4.5904 | 0.0328 | 0.0240 | 0.0321 | 0.0343 |
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| 85 |
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| 4.5526 | 0.1553 | 20 | 4.2901 | 0.1118 | 0.0891 | 0.1068 | 0.1134 |
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| 86 |
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| 4.2946 | 0.2330 | 30 | 3.9659 | 0.2602 | 0.2124 | 0.2287 | 0.2522 |
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| 87 |
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| 3.9673 | 0.3107 | 40 | 3.6288 | 0.4351 | 0.3666 | 0.4093 | 0.4189 |
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| 88 |
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| 3.69 | 0.3883 | 50 | 3.3225 | 0.5394 | 0.4751 | 0.5232 | 0.5244 |
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| 89 |
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| 3.4705 | 0.4660 | 60 | 3.0343 | 0.6261 | 0.5750 | 0.6563 | 0.6139 |
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| 90 |
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| 3.2239 | 0.5437 | 70 | 2.7671 | 0.6842 | 0.6503 | 0.7272 | 0.6743 |
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| 91 |
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| 2.9986 | 0.6214 | 80 | 2.5191 | 0.7262 | 0.6971 | 0.7601 | 0.7161 |
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| 92 |
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| 2.7575 | 0.6990 | 90 | 2.2953 | 0.7430 | 0.7162 | 0.7735 | 0.7333 |
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| 93 |
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| 2.5923 | 0.7767 | 100 | 2.1008 | 0.7694 | 0.7470 | 0.7956 | 0.7600 |
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| 94 |
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| 2.4265 | 0.8544 | 110 | 1.9250 | 0.7949 | 0.7762 | 0.8094 | 0.7863 |
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| 95 |
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| 2.3049 | 0.9320 | 120 | 1.7636 | 0.8054 | 0.7861 | 0.8173 | 0.7971 |
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| 96 |
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| 2.1243 | 1.0097 | 130 | 1.6290 | 0.8200 | 0.8056 | 0.8382 | 0.8125 |
|
| 97 |
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| 1.9721 | 1.0874 | 140 | 1.5121 | 0.8226 | 0.8084 | 0.8396 | 0.8149 |
|
| 98 |
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| 1.848 | 1.1650 | 150 | 1.4282 | 0.8163 | 0.8002 | 0.8362 | 0.8083 |
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| 99 |
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| 1.775 | 1.2427 | 160 | 1.3034 | 0.8304 | 0.8171 | 0.8438 | 0.8238 |
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| 100 |
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| 1.717 | 1.3204 | 170 | 1.2343 | 0.8275 | 0.8126 | 0.8460 | 0.8207 |
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| 101 |
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| 1.6203 | 1.3981 | 180 | 1.1554 | 0.8387 | 0.8259 | 0.8552 | 0.8323 |
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| 102 |
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| 1.5739 | 1.4757 | 190 | 1.0944 | 0.8484 | 0.8384 | 0.8593 | 0.8420 |
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| 103 |
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| 1.5508 | 1.5534 | 200 | 1.0400 | 0.8484 | 0.8394 | 0.8574 | 0.8431 |
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| 104 |
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| 1.4549 | 1.6311 | 210 | 0.9943 | 0.8452 | 0.8340 | 0.8497 | 0.8399 |
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| 105 |
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| 1.3907 | 1.7087 | 220 | 0.9427 | 0.8596 | 0.8480 | 0.8627 | 0.8542 |
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| 106 |
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| 1.3497 | 1.7864 | 230 | 0.8936 | 0.8569 | 0.8461 | 0.8647 | 0.8516 |
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| 107 |
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| 1.2618 | 1.8641 | 240 | 0.8619 | 0.8613 | 0.8503 | 0.8671 | 0.8560 |
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| 108 |
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| 1.3014 | 1.9417 | 250 | 0.8324 | 0.8603 | 0.8508 | 0.8737 | 0.8553 |
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| 109 |
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| 1.2209 | 2.0194 | 260 | 0.8015 | 0.8591 | 0.8503 | 0.8645 | 0.8537 |
|
| 110 |
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| 1.2139 | 2.0971 | 270 | 0.7824 | 0.8596 | 0.8517 | 0.8656 | 0.8544 |
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| 111 |
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| 1.1364 | 2.1748 | 280 | 0.7544 | 0.8603 | 0.8513 | 0.8611 | 0.8556 |
|
| 112 |
+
| 1.1811 | 2.2524 | 290 | 0.7283 | 0.8683 | 0.8605 | 0.8785 | 0.8637 |
|
| 113 |
+
| 1.1316 | 2.3301 | 300 | 0.7169 | 0.8635 | 0.8550 | 0.8653 | 0.8590 |
|
| 114 |
+
| 1.1246 | 2.4078 | 310 | 0.6900 | 0.8686 | 0.8610 | 0.8739 | 0.8645 |
|
| 115 |
+
| 1.1027 | 2.4854 | 320 | 0.6862 | 0.8627 | 0.8548 | 0.8730 | 0.8582 |
|
| 116 |
+
| 1.0911 | 2.5631 | 330 | 0.6667 | 0.8693 | 0.8632 | 0.8730 | 0.8653 |
|
| 117 |
+
| 1.0158 | 2.6408 | 340 | 0.6544 | 0.8695 | 0.8628 | 0.8751 | 0.8651 |
|
| 118 |
+
| 1.0805 | 2.7184 | 350 | 0.6342 | 0.8703 | 0.8634 | 0.8733 | 0.8663 |
|
| 119 |
+
| 1.0679 | 2.7961 | 360 | 0.6276 | 0.8754 | 0.8689 | 0.8797 | 0.8713 |
|
| 120 |
+
| 1.0611 | 2.8738 | 370 | 0.6223 | 0.8746 | 0.8692 | 0.8807 | 0.8705 |
|
| 121 |
+
| 0.9996 | 2.9515 | 380 | 0.6055 | 0.8724 | 0.8661 | 0.8758 | 0.8683 |
|
| 122 |
+
| 1.0838 | 3.0291 | 390 | 0.6039 | 0.8715 | 0.8652 | 0.8769 | 0.8677 |
|
| 123 |
+
| 0.9396 | 3.1068 | 400 | 0.5946 | 0.8737 | 0.8676 | 0.8791 | 0.8699 |
|
| 124 |
+
| 0.8466 | 3.1845 | 410 | 0.5810 | 0.8717 | 0.8653 | 0.8775 | 0.8673 |
|
| 125 |
+
| 0.9588 | 3.2621 | 420 | 0.5819 | 0.8710 | 0.8651 | 0.8766 | 0.8671 |
|
| 126 |
+
| 0.9784 | 3.3398 | 430 | 0.5742 | 0.8754 | 0.8684 | 0.8788 | 0.8716 |
|
| 127 |
+
| 0.9289 | 3.4175 | 440 | 0.5667 | 0.8768 | 0.8703 | 0.8792 | 0.8731 |
|
| 128 |
+
| 0.8917 | 3.4951 | 450 | 0.5615 | 0.8724 | 0.8672 | 0.8762 | 0.8690 |
|
| 129 |
+
| 0.8646 | 3.5728 | 460 | 0.5537 | 0.8737 | 0.8681 | 0.8761 | 0.8702 |
|
| 130 |
+
| 0.9029 | 3.6505 | 470 | 0.5538 | 0.8732 | 0.8694 | 0.8771 | 0.8698 |
|
| 131 |
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| 0.9551 | 3.7282 | 480 | 0.5440 | 0.8766 | 0.8720 | 0.8809 | 0.8735 |
|
| 132 |
+
| 0.8787 | 3.8058 | 490 | 0.5448 | 0.8751 | 0.8704 | 0.8791 | 0.8712 |
|
| 133 |
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| 0.9128 | 3.8835 | 500 | 0.5354 | 0.8751 | 0.8701 | 0.8799 | 0.8712 |
|
| 134 |
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| 0.8566 | 3.9612 | 510 | 0.5262 | 0.8776 | 0.8715 | 0.8846 | 0.8738 |
|
| 135 |
+
| 0.8624 | 4.0388 | 520 | 0.5252 | 0.8754 | 0.8692 | 0.8840 | 0.8715 |
|
| 136 |
+
| 0.799 | 4.1165 | 530 | 0.5197 | 0.8763 | 0.8702 | 0.8817 | 0.8723 |
|
| 137 |
+
| 0.7912 | 4.1942 | 540 | 0.5213 | 0.8751 | 0.8695 | 0.8815 | 0.8709 |
|
| 138 |
+
| 0.874 | 4.2718 | 550 | 0.5142 | 0.8778 | 0.8730 | 0.8862 | 0.8742 |
|
| 139 |
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| 0.766 | 4.3495 | 560 | 0.5019 | 0.8817 | 0.8770 | 0.8864 | 0.8783 |
|
| 140 |
+
| 0.8902 | 4.4272 | 570 | 0.5011 | 0.8831 | 0.8785 | 0.8887 | 0.8798 |
|
| 141 |
+
| 0.8038 | 4.5049 | 580 | 0.5014 | 0.8800 | 0.8742 | 0.8878 | 0.8762 |
|
| 142 |
+
| 0.8893 | 4.5825 | 590 | 0.5062 | 0.8797 | 0.8744 | 0.8851 | 0.8759 |
|
| 143 |
+
| 0.7868 | 4.6602 | 600 | 0.4926 | 0.8827 | 0.8785 | 0.8867 | 0.8791 |
|
| 144 |
+
| 0.7733 | 4.7379 | 610 | 0.4957 | 0.8783 | 0.8749 | 0.8816 | 0.8755 |
|
| 145 |
+
| 0.8275 | 4.8155 | 620 | 0.4871 | 0.8817 | 0.8781 | 0.8847 | 0.8785 |
|
| 146 |
+
| 0.7944 | 4.8932 | 630 | 0.4855 | 0.8858 | 0.8823 | 0.8880 | 0.8829 |
|
| 147 |
+
| 0.8483 | 4.9709 | 640 | 0.4849 | 0.8836 | 0.8797 | 0.8858 | 0.8803 |
|
| 148 |
+
| 0.7297 | 5.0485 | 650 | 0.4833 | 0.8814 | 0.8779 | 0.8845 | 0.8784 |
|
| 149 |
+
| 0.754 | 5.1262 | 660 | 0.4824 | 0.8814 | 0.8775 | 0.8844 | 0.8782 |
|
| 150 |
+
| 0.698 | 5.2039 | 670 | 0.4806 | 0.8851 | 0.8818 | 0.8878 | 0.8821 |
|
| 151 |
+
| 0.7515 | 5.2816 | 680 | 0.4777 | 0.8824 | 0.8791 | 0.8855 | 0.8796 |
|
| 152 |
+
| 0.7527 | 5.3592 | 690 | 0.4711 | 0.8841 | 0.8806 | 0.8869 | 0.8808 |
|
| 153 |
+
| 0.7287 | 5.4369 | 700 | 0.4718 | 0.8853 | 0.8819 | 0.8873 | 0.8824 |
|
| 154 |
+
| 0.8134 | 5.5146 | 710 | 0.4680 | 0.8856 | 0.8826 | 0.8885 | 0.8828 |
|
| 155 |
+
| 0.7655 | 5.5922 | 720 | 0.4688 | 0.8836 | 0.8795 | 0.8862 | 0.8800 |
|
| 156 |
+
| 0.7904 | 5.6699 | 730 | 0.4671 | 0.8878 | 0.8841 | 0.8901 | 0.8846 |
|
| 157 |
+
| 0.7257 | 5.7476 | 740 | 0.4704 | 0.8824 | 0.8790 | 0.8872 | 0.8796 |
|
| 158 |
+
| 0.7342 | 5.8252 | 750 | 0.4641 | 0.8841 | 0.8802 | 0.8889 | 0.8810 |
|
| 159 |
+
| 0.7075 | 5.9029 | 760 | 0.4654 | 0.8824 | 0.8782 | 0.8865 | 0.8791 |
|
| 160 |
+
| 0.7924 | 5.9806 | 770 | 0.4619 | 0.8868 | 0.8829 | 0.8899 | 0.8839 |
|
| 161 |
+
| 0.7176 | 6.0583 | 780 | 0.4597 | 0.8861 | 0.8815 | 0.8889 | 0.8829 |
|
| 162 |
+
| 0.6768 | 6.1359 | 790 | 0.4595 | 0.8858 | 0.8820 | 0.8910 | 0.8827 |
|
| 163 |
+
| 0.722 | 6.2136 | 800 | 0.4605 | 0.8836 | 0.8796 | 0.8882 | 0.8803 |
|
| 164 |
+
| 0.7429 | 6.2913 | 810 | 0.4594 | 0.8865 | 0.8823 | 0.8912 | 0.8833 |
|
| 165 |
+
| 0.6904 | 6.3689 | 820 | 0.4611 | 0.8856 | 0.8821 | 0.8892 | 0.8825 |
|
| 166 |
+
| 0.7617 | 6.4466 | 830 | 0.4592 | 0.8856 | 0.8816 | 0.8879 | 0.8826 |
|
| 167 |
+
| 0.7285 | 6.5243 | 840 | 0.4576 | 0.8863 | 0.8822 | 0.8895 | 0.8832 |
|
| 168 |
+
| 0.686 | 6.6019 | 850 | 0.4561 | 0.8875 | 0.8834 | 0.8923 | 0.8844 |
|
| 169 |
+
| 0.6546 | 6.6796 | 860 | 0.4561 | 0.8865 | 0.8824 | 0.8903 | 0.8835 |
|
| 170 |
+
| 0.6526 | 6.7573 | 870 | 0.4543 | 0.8875 | 0.8830 | 0.8917 | 0.8844 |
|
| 171 |
+
| 0.7534 | 6.8350 | 880 | 0.4537 | 0.8885 | 0.8845 | 0.8927 | 0.8855 |
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| 172 |
+
| 0.7065 | 6.9126 | 890 | 0.4535 | 0.8870 | 0.8831 | 0.8912 | 0.8841 |
|
| 173 |
+
| 0.774 | 6.9903 | 900 | 0.4528 | 0.8878 | 0.8842 | 0.8924 | 0.8849 |
|
| 174 |
+
| 0.7185 | 7.0680 | 910 | 0.4516 | 0.8880 | 0.8840 | 0.8913 | 0.8849 |
|
| 175 |
+
| 0.6321 | 7.1456 | 920 | 0.4526 | 0.8868 | 0.8830 | 0.8900 | 0.8838 |
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| 176 |
+
| 0.6957 | 7.2233 | 930 | 0.4517 | 0.8865 | 0.8825 | 0.8901 | 0.8834 |
|
| 177 |
+
| 0.6774 | 7.3010 | 940 | 0.4523 | 0.8863 | 0.8823 | 0.8895 | 0.8833 |
|
| 178 |
+
| 0.6915 | 7.3786 | 950 | 0.4528 | 0.8853 | 0.8814 | 0.8890 | 0.8822 |
|
| 179 |
+
| 0.6738 | 7.4563 | 960 | 0.4520 | 0.8868 | 0.8829 | 0.8901 | 0.8838 |
|
| 180 |
+
| 0.7021 | 7.5340 | 970 | 0.4510 | 0.8863 | 0.8826 | 0.8897 | 0.8834 |
|
| 181 |
+
| 0.7053 | 7.6117 | 980 | 0.4501 | 0.8863 | 0.8827 | 0.8885 | 0.8835 |
|
| 182 |
+
| 0.7241 | 7.6893 | 990 | 0.4498 | 0.8865 | 0.8829 | 0.8893 | 0.8837 |
|
| 183 |
+
| 0.703 | 7.7670 | 1000 | 0.4497 | 0.8865 | 0.8829 | 0.8893 | 0.8837 |
|
| 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 +1,268 @@
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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/vit-base-patch16-224",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"ViTForImageClassification"
|
| 5 |
+
],
|
| 6 |
+
"attention_probs_dropout_prob": 0.0,
|
| 7 |
+
"encoder_stride": 16,
|
| 8 |
+
"hidden_act": "gelu",
|
| 9 |
+
"hidden_dropout_prob": 0.0,
|
| 10 |
+
"hidden_size": 768,
|
| 11 |
+
"id2label": {
|
| 12 |
+
"0": "Affenpinscher",
|
| 13 |
+
"1": "Afghan Hound",
|
| 14 |
+
"2": "African Hunting Dog",
|
| 15 |
+
"3": "Airedale",
|
| 16 |
+
"4": "American Staffordshire Terrier",
|
| 17 |
+
"5": "Appenzeller",
|
| 18 |
+
"6": "Australian Terrier",
|
| 19 |
+
"7": "Basenji",
|
| 20 |
+
"8": "Basset",
|
| 21 |
+
"9": "Beagle",
|
| 22 |
+
"10": "Bedlington Terrier",
|
| 23 |
+
"11": "Bernese Mountain Dog",
|
| 24 |
+
"12": "Black And Tan Coonhound",
|
| 25 |
+
"13": "Blenheim Spaniel",
|
| 26 |
+
"14": "Bloodhound",
|
| 27 |
+
"15": "Bluetick",
|
| 28 |
+
"16": "Border Collie",
|
| 29 |
+
"17": "Border Terrier",
|
| 30 |
+
"18": "Borzoi",
|
| 31 |
+
"19": "Boston Bull",
|
| 32 |
+
"20": "Bouvier Des Flandres",
|
| 33 |
+
"21": "Boxer",
|
| 34 |
+
"22": "Brabancon Griffon",
|
| 35 |
+
"23": "Briard",
|
| 36 |
+
"24": "Brittany Spaniel",
|
| 37 |
+
"25": "Bull Mastiff",
|
| 38 |
+
"26": "Cairn",
|
| 39 |
+
"27": "Cardigan",
|
| 40 |
+
"28": "Chesapeake Bay Retriever",
|
| 41 |
+
"29": "Chihuahua",
|
| 42 |
+
"30": "Chow",
|
| 43 |
+
"31": "Clumber",
|
| 44 |
+
"32": "Cocker Spaniel",
|
| 45 |
+
"33": "Collie",
|
| 46 |
+
"34": "Curly Coated Retriever",
|
| 47 |
+
"35": "Dandie Dinmont",
|
| 48 |
+
"36": "Dhole",
|
| 49 |
+
"37": "Dingo",
|
| 50 |
+
"38": "Doberman",
|
| 51 |
+
"39": "English Foxhound",
|
| 52 |
+
"40": "English Setter",
|
| 53 |
+
"41": "English Springer",
|
| 54 |
+
"42": "Entlebucher",
|
| 55 |
+
"43": "Eskimo Dog",
|
| 56 |
+
"44": "Flat Coated Retriever",
|
| 57 |
+
"45": "French Bulldog",
|
| 58 |
+
"46": "German Shepherd",
|
| 59 |
+
"47": "German Short Haired Pointer",
|
| 60 |
+
"48": "Giant Schnauzer",
|
| 61 |
+
"49": "Golden Retriever",
|
| 62 |
+
"50": "Gordon Setter",
|
| 63 |
+
"51": "Great Dane",
|
| 64 |
+
"52": "Great Pyrenees",
|
| 65 |
+
"53": "Greater Swiss Mountain Dog",
|
| 66 |
+
"54": "Groenendael",
|
| 67 |
+
"55": "Ibizan Hound",
|
| 68 |
+
"56": "Irish Setter",
|
| 69 |
+
"57": "Irish Terrier",
|
| 70 |
+
"58": "Irish Water Spaniel",
|
| 71 |
+
"59": "Irish Wolfhound",
|
| 72 |
+
"60": "Italian Greyhound",
|
| 73 |
+
"61": "Japanese Spaniel",
|
| 74 |
+
"62": "Keeshond",
|
| 75 |
+
"63": "Kelpie",
|
| 76 |
+
"64": "Kerry Blue Terrier",
|
| 77 |
+
"65": "Komondor",
|
| 78 |
+
"66": "Kuvasz",
|
| 79 |
+
"67": "Labrador Retriever",
|
| 80 |
+
"68": "Lakeland Terrier",
|
| 81 |
+
"69": "Leonberg",
|
| 82 |
+
"70": "Lhasa",
|
| 83 |
+
"71": "Malamute",
|
| 84 |
+
"72": "Malinois",
|
| 85 |
+
"73": "Maltese Dog",
|
| 86 |
+
"74": "Mexican Hairless",
|
| 87 |
+
"75": "Miniature Pinscher",
|
| 88 |
+
"76": "Miniature Poodle",
|
| 89 |
+
"77": "Miniature Schnauzer",
|
| 90 |
+
"78": "Newfoundland",
|
| 91 |
+
"79": "Norfolk Terrier",
|
| 92 |
+
"80": "Norwegian Elkhound",
|
| 93 |
+
"81": "Norwich Terrier",
|
| 94 |
+
"82": "Old English Sheepdog",
|
| 95 |
+
"83": "Otterhound",
|
| 96 |
+
"84": "Papillon",
|
| 97 |
+
"85": "Pekinese",
|
| 98 |
+
"86": "Pembroke",
|
| 99 |
+
"87": "Pomeranian",
|
| 100 |
+
"88": "Pug",
|
| 101 |
+
"89": "Redbone",
|
| 102 |
+
"90": "Rhodesian Ridgeback",
|
| 103 |
+
"91": "Rottweiler",
|
| 104 |
+
"92": "Saint Bernard",
|
| 105 |
+
"93": "Saluki",
|
| 106 |
+
"94": "Samoyed",
|
| 107 |
+
"95": "Schipperke",
|
| 108 |
+
"96": "Scotch Terrier",
|
| 109 |
+
"97": "Scottish Deerhound",
|
| 110 |
+
"98": "Sealyham Terrier",
|
| 111 |
+
"99": "Shetland Sheepdog",
|
| 112 |
+
"100": "Shih Tzu",
|
| 113 |
+
"101": "Siberian Husky",
|
| 114 |
+
"102": "Silky Terrier",
|
| 115 |
+
"103": "Soft Coated Wheaten Terrier",
|
| 116 |
+
"104": "Staffordshire Bullterrier",
|
| 117 |
+
"105": "Standard Poodle",
|
| 118 |
+
"106": "Standard Schnauzer",
|
| 119 |
+
"107": "Sussex Spaniel",
|
| 120 |
+
"108": "Tibetan Mastiff",
|
| 121 |
+
"109": "Tibetan Terrier",
|
| 122 |
+
"110": "Toy Poodle",
|
| 123 |
+
"111": "Toy Terrier",
|
| 124 |
+
"112": "Vizsla",
|
| 125 |
+
"113": "Walker Hound",
|
| 126 |
+
"114": "Weimaraner",
|
| 127 |
+
"115": "Welsh Springer Spaniel",
|
| 128 |
+
"116": "West Highland White Terrier",
|
| 129 |
+
"117": "Whippet",
|
| 130 |
+
"118": "Wire Haired Fox Terrier",
|
| 131 |
+
"119": "Yorkshire Terrier"
|
| 132 |
+
},
|
| 133 |
+
"image_size": 224,
|
| 134 |
+
"initializer_range": 0.02,
|
| 135 |
+
"intermediate_size": 3072,
|
| 136 |
+
"label2id": {
|
| 137 |
+
"Affenpinscher": 0,
|
| 138 |
+
"Afghan Hound": 1,
|
| 139 |
+
"African Hunting Dog": 2,
|
| 140 |
+
"Airedale": 3,
|
| 141 |
+
"American Staffordshire Terrier": 4,
|
| 142 |
+
"Appenzeller": 5,
|
| 143 |
+
"Australian Terrier": 6,
|
| 144 |
+
"Basenji": 7,
|
| 145 |
+
"Basset": 8,
|
| 146 |
+
"Beagle": 9,
|
| 147 |
+
"Bedlington Terrier": 10,
|
| 148 |
+
"Bernese Mountain Dog": 11,
|
| 149 |
+
"Black And Tan Coonhound": 12,
|
| 150 |
+
"Blenheim Spaniel": 13,
|
| 151 |
+
"Bloodhound": 14,
|
| 152 |
+
"Bluetick": 15,
|
| 153 |
+
"Border Collie": 16,
|
| 154 |
+
"Border Terrier": 17,
|
| 155 |
+
"Borzoi": 18,
|
| 156 |
+
"Boston Bull": 19,
|
| 157 |
+
"Bouvier Des Flandres": 20,
|
| 158 |
+
"Boxer": 21,
|
| 159 |
+
"Brabancon Griffon": 22,
|
| 160 |
+
"Briard": 23,
|
| 161 |
+
"Brittany Spaniel": 24,
|
| 162 |
+
"Bull Mastiff": 25,
|
| 163 |
+
"Cairn": 26,
|
| 164 |
+
"Cardigan": 27,
|
| 165 |
+
"Chesapeake Bay Retriever": 28,
|
| 166 |
+
"Chihuahua": 29,
|
| 167 |
+
"Chow": 30,
|
| 168 |
+
"Clumber": 31,
|
| 169 |
+
"Cocker Spaniel": 32,
|
| 170 |
+
"Collie": 33,
|
| 171 |
+
"Curly Coated Retriever": 34,
|
| 172 |
+
"Dandie Dinmont": 35,
|
| 173 |
+
"Dhole": 36,
|
| 174 |
+
"Dingo": 37,
|
| 175 |
+
"Doberman": 38,
|
| 176 |
+
"English Foxhound": 39,
|
| 177 |
+
"English Setter": 40,
|
| 178 |
+
"English Springer": 41,
|
| 179 |
+
"Entlebucher": 42,
|
| 180 |
+
"Eskimo Dog": 43,
|
| 181 |
+
"Flat Coated Retriever": 44,
|
| 182 |
+
"French Bulldog": 45,
|
| 183 |
+
"German Shepherd": 46,
|
| 184 |
+
"German Short Haired Pointer": 47,
|
| 185 |
+
"Giant Schnauzer": 48,
|
| 186 |
+
"Golden Retriever": 49,
|
| 187 |
+
"Gordon Setter": 50,
|
| 188 |
+
"Great Dane": 51,
|
| 189 |
+
"Great Pyrenees": 52,
|
| 190 |
+
"Greater Swiss Mountain Dog": 53,
|
| 191 |
+
"Groenendael": 54,
|
| 192 |
+
"Ibizan Hound": 55,
|
| 193 |
+
"Irish Setter": 56,
|
| 194 |
+
"Irish Terrier": 57,
|
| 195 |
+
"Irish Water Spaniel": 58,
|
| 196 |
+
"Irish Wolfhound": 59,
|
| 197 |
+
"Italian Greyhound": 60,
|
| 198 |
+
"Japanese Spaniel": 61,
|
| 199 |
+
"Keeshond": 62,
|
| 200 |
+
"Kelpie": 63,
|
| 201 |
+
"Kerry Blue Terrier": 64,
|
| 202 |
+
"Komondor": 65,
|
| 203 |
+
"Kuvasz": 66,
|
| 204 |
+
"Labrador Retriever": 67,
|
| 205 |
+
"Lakeland Terrier": 68,
|
| 206 |
+
"Leonberg": 69,
|
| 207 |
+
"Lhasa": 70,
|
| 208 |
+
"Malamute": 71,
|
| 209 |
+
"Malinois": 72,
|
| 210 |
+
"Maltese Dog": 73,
|
| 211 |
+
"Mexican Hairless": 74,
|
| 212 |
+
"Miniature Pinscher": 75,
|
| 213 |
+
"Miniature Poodle": 76,
|
| 214 |
+
"Miniature Schnauzer": 77,
|
| 215 |
+
"Newfoundland": 78,
|
| 216 |
+
"Norfolk Terrier": 79,
|
| 217 |
+
"Norwegian Elkhound": 80,
|
| 218 |
+
"Norwich Terrier": 81,
|
| 219 |
+
"Old English Sheepdog": 82,
|
| 220 |
+
"Otterhound": 83,
|
| 221 |
+
"Papillon": 84,
|
| 222 |
+
"Pekinese": 85,
|
| 223 |
+
"Pembroke": 86,
|
| 224 |
+
"Pomeranian": 87,
|
| 225 |
+
"Pug": 88,
|
| 226 |
+
"Redbone": 89,
|
| 227 |
+
"Rhodesian Ridgeback": 90,
|
| 228 |
+
"Rottweiler": 91,
|
| 229 |
+
"Saint Bernard": 92,
|
| 230 |
+
"Saluki": 93,
|
| 231 |
+
"Samoyed": 94,
|
| 232 |
+
"Schipperke": 95,
|
| 233 |
+
"Scotch Terrier": 96,
|
| 234 |
+
"Scottish Deerhound": 97,
|
| 235 |
+
"Sealyham Terrier": 98,
|
| 236 |
+
"Shetland Sheepdog": 99,
|
| 237 |
+
"Shih Tzu": 100,
|
| 238 |
+
"Siberian Husky": 101,
|
| 239 |
+
"Silky Terrier": 102,
|
| 240 |
+
"Soft Coated Wheaten Terrier": 103,
|
| 241 |
+
"Staffordshire Bullterrier": 104,
|
| 242 |
+
"Standard Poodle": 105,
|
| 243 |
+
"Standard Schnauzer": 106,
|
| 244 |
+
"Sussex Spaniel": 107,
|
| 245 |
+
"Tibetan Mastiff": 108,
|
| 246 |
+
"Tibetan Terrier": 109,
|
| 247 |
+
"Toy Poodle": 110,
|
| 248 |
+
"Toy Terrier": 111,
|
| 249 |
+
"Vizsla": 112,
|
| 250 |
+
"Walker Hound": 113,
|
| 251 |
+
"Weimaraner": 114,
|
| 252 |
+
"Welsh Springer Spaniel": 115,
|
| 253 |
+
"West Highland White Terrier": 116,
|
| 254 |
+
"Whippet": 117,
|
| 255 |
+
"Wire Haired Fox Terrier": 118,
|
| 256 |
+
"Yorkshire Terrier": 119
|
| 257 |
+
},
|
| 258 |
+
"layer_norm_eps": 1e-12,
|
| 259 |
+
"model_type": "vit",
|
| 260 |
+
"num_attention_heads": 12,
|
| 261 |
+
"num_channels": 3,
|
| 262 |
+
"num_hidden_layers": 12,
|
| 263 |
+
"patch_size": 16,
|
| 264 |
+
"problem_type": "single_label_classification",
|
| 265 |
+
"qkv_bias": true,
|
| 266 |
+
"torch_dtype": "float32",
|
| 267 |
+
"transformers_version": "4.40.2"
|
| 268 |
+
}
|
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 sha256:0fce687c74a59523ff91a72290d69b028ec25130a4d52172436f67f1c4c49b89
|
| 3 |
+
size 343586952
|
preprocessor_config.json
ADDED
|
@@ -0,0 +1,36 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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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": "ViTImageProcessor",
|
| 25 |
+
"image_std": [
|
| 26 |
+
0.5,
|
| 27 |
+
0.5,
|
| 28 |
+
0.5
|
| 29 |
+
],
|
| 30 |
+
"resample": 2,
|
| 31 |
+
"rescale_factor": 0.00392156862745098,
|
| 32 |
+
"size": {
|
| 33 |
+
"height": 224,
|
| 34 |
+
"width": 224
|
| 35 |
+
}
|
| 36 |
+
}
|
train.ipynb
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:90b7ad26803e9537f458be67001320104e28aeae19046ed6a9807527a1838748
|
| 3 |
+
size 5112
|