mujerry
/

swin-tiny-patch4-window7-224-finetuned-papsmear

@@ -1,80 +1,178 @@
----
-license: apache-2.0
-base_model: microsoft/swin-tiny-patch4-window7-224
-tags:
-- generated_from_trainer
-datasets:
-- imagefolder
-metrics:
-- accuracy
-model-index:
-- name: swin-tiny-patch4-window7-224-finetuned-papsmear
-  results:
-  - task:
-      name: Image Classification
-      type: image-classification
-    dataset:
-      name: imagefolder
-      type: imagefolder
-      config: default
-      split: train
-      args: default
-    metrics:
-    - name: Accuracy
-      type: accuracy
-      value: 0.5882352941176471
----
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-# swin-tiny-patch4-window7-224-finetuned-papsmear
-This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.1058
-- Accuracy: 0.5882
-## Model description
-More information needed
-## Intended uses & limitations
-More information needed
-## Training and evaluation data
-More information needed
-## Training procedure
-### Training hyperparameters
-The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 32
-- eval_batch_size: 32
-- seed: 42
-- gradient_accumulation_steps: 4
-- total_train_batch_size: 128
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 3
-### Training results
-| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
-|:-------------:|:------:|:----:|:---------------:|:--------:|
-| No log        | 0.9231 | 9    | 1.4240          | 0.3529   |
-| 1.5837        | 1.9487 | 19   | 1.1058          | 0.5882   |
-| 1.245         | 2.7692 | 27   | 1.0397          | 0.5882   |
-### Framework versions
-- Transformers 4.42.4
-- Pytorch 2.4.0+cu121
-- Datasets 2.21.0
-- Tokenizers 0.19.1

+---
+library_name: transformers
+license: apache-2.0
+base_model: microsoft/swin-tiny-patch4-window7-224
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: swin-tiny-patch4-window7-224-finetuned-papsmear
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9779411764705882
+---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# swin-tiny-patch4-window7-224-finetuned-papsmear
+This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3036
+- Accuracy: 0.9779
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 100
+### Training results
+| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|
+| 1.7081        | 0.9935  | 38   | 1.6642          | 0.2868   |
+| 1.4025        | 1.9869  | 76   | 1.3761          | 0.4632   |
+| 1.0918        | 2.9804  | 114  | 1.0276          | 0.5515   |
+| 0.8051        | 4.0     | 153  | 0.7679          | 0.6691   |
+| 0.635         | 4.9935  | 191  | 0.5928          | 0.7868   |
+| 0.6051        | 5.9869  | 229  | 0.6957          | 0.75     |
+| 0.5539        | 6.9804  | 267  | 0.5016          | 0.7941   |
+| 0.4683        | 8.0     | 306  | 0.4733          | 0.8235   |
+| 0.4153        | 8.9935  | 344  | 0.4835          | 0.8529   |
+| 0.3954        | 9.9869  | 382  | 0.5431          | 0.8309   |
+| 0.3524        | 10.9804 | 420  | 0.4061          | 0.8235   |
+| 0.3546        | 12.0    | 459  | 0.4925          | 0.8382   |
+| 0.2922        | 12.9935 | 497  | 0.3637          | 0.875    |
+| 0.2342        | 13.9869 | 535  | 0.3286          | 0.8971   |
+| 0.2083        | 14.9804 | 573  | 0.3271          | 0.8824   |
+| 0.2704        | 16.0    | 612  | 0.3700          | 0.8824   |
+| 0.1871        | 16.9935 | 650  | 0.3447          | 0.8971   |
+| 0.226         | 17.9869 | 688  | 0.4280          | 0.8603   |
+| 0.245         | 18.9804 | 726  | 0.6445          | 0.8088   |
+| 0.1545        | 20.0    | 765  | 0.4180          | 0.8603   |
+| 0.0981        | 20.9935 | 803  | 0.3208          | 0.9044   |
+| 0.1455        | 21.9869 | 841  | 0.4256          | 0.8603   |
+| 0.2405        | 22.9804 | 879  | 0.3474          | 0.8971   |
+| 0.1549        | 24.0    | 918  | 0.3940          | 0.9044   |
+| 0.1721        | 24.9935 | 956  | 0.4279          | 0.8824   |
+| 0.1378        | 25.9869 | 994  | 0.3871          | 0.9044   |
+| 0.0924        | 26.9804 | 1032 | 0.7301          | 0.8456   |
+| 0.1325        | 28.0    | 1071 | 0.3712          | 0.9044   |
+| 0.1426        | 28.9935 | 1109 | 0.4400          | 0.8603   |
+| 0.0866        | 29.9869 | 1147 | 0.2779          | 0.9412   |
+| 0.0659        | 30.9804 | 1185 | 0.3207          | 0.9412   |
+| 0.1175        | 32.0    | 1224 | 0.4339          | 0.9044   |
+| 0.0455        | 32.9935 | 1262 | 0.4537          | 0.9265   |
+| 0.1006        | 33.9869 | 1300 | 0.6521          | 0.875    |
+| 0.033         | 34.9804 | 1338 | 0.5616          | 0.9044   |
+| 0.0979        | 36.0    | 1377 | 0.3718          | 0.9191   |
+| 0.1045        | 36.9935 | 1415 | 0.2529          | 0.9632   |
+| 0.0815        | 37.9869 | 1453 | 0.3511          | 0.9338   |
+| 0.0761        | 38.9804 | 1491 | 0.3114          | 0.9338   |
+| 0.0747        | 40.0    | 1530 | 0.2837          | 0.9338   |
+| 0.0545        | 40.9935 | 1568 | 0.4269          | 0.9412   |
+| 0.0796        | 41.9869 | 1606 | 0.2331          | 0.9412   |
+| 0.055         | 42.9804 | 1644 | 0.2900          | 0.9485   |
+| 0.0706        | 44.0    | 1683 | 0.3368          | 0.9632   |
+| 0.0505        | 44.9935 | 1721 | 0.3780          | 0.9485   |
+| 0.0698        | 45.9869 | 1759 | 0.4822          | 0.9191   |
+| 0.0275        | 46.9804 | 1797 | 0.3434          | 0.9632   |
+| 0.0641        | 48.0    | 1836 | 0.3387          | 0.9706   |
+| 0.0484        | 48.9935 | 1874 | 0.5350          | 0.9191   |
+| 0.0388        | 49.9869 | 1912 | 0.3826          | 0.9118   |
+| 0.0347        | 50.9804 | 1950 | 0.3739          | 0.9559   |
+| 0.1046        | 52.0    | 1989 | 0.3075          | 0.9118   |
+| 0.0298        | 52.9935 | 2027 | 0.3558          | 0.9559   |
+| 0.0478        | 53.9869 | 2065 | 0.3056          | 0.9706   |
+| 0.0285        | 54.9804 | 2103 | 0.2851          | 0.9632   |
+| 0.0407        | 56.0    | 2142 | 0.3223          | 0.9559   |
+| 0.0459        | 56.9935 | 2180 | 0.4575          | 0.9485   |
+| 0.0409        | 57.9869 | 2218 | 0.2930          | 0.9632   |
+| 0.0743        | 58.9804 | 2256 | 0.4032          | 0.9485   |
+| 0.0346        | 60.0    | 2295 | 0.3738          | 0.9412   |
+| 0.0302        | 60.9935 | 2333 | 0.3597          | 0.9485   |
+| 0.0488        | 61.9869 | 2371 | 0.2595          | 0.9559   |
+| 0.0562        | 62.9804 | 2409 | 0.3764          | 0.9412   |
+| 0.0216        | 64.0    | 2448 | 0.2644          | 0.9779   |
+| 0.0219        | 64.9935 | 2486 | 0.3092          | 0.9632   |
+| 0.0272        | 65.9869 | 2524 | 0.2898          | 0.9632   |
+| 0.027         | 66.9804 | 2562 | 0.2693          | 0.9632   |
+| 0.0397        | 68.0    | 2601 | 0.3843          | 0.9412   |
+| 0.0154        | 68.9935 | 2639 | 0.3051          | 0.9485   |
+| 0.0004        | 69.9869 | 2677 | 0.3909          | 0.9412   |
+| 0.0651        | 70.9804 | 2715 | 0.2977          | 0.9485   |
+| 0.016         | 72.0    | 2754 | 0.2695          | 0.9632   |
+| 0.0351        | 72.9935 | 2792 | 0.2720          | 0.9706   |
+| 0.0206        | 73.9869 | 2830 | 0.2549          | 0.9706   |
+| 0.0109        | 74.9804 | 2868 | 0.2412          | 0.9706   |
+| 0.0012        | 76.0    | 2907 | 0.3494          | 0.9779   |
+| 0.0418        | 76.9935 | 2945 | 0.3729          | 0.9632   |
+| 0.0165        | 77.9869 | 2983 | 0.3471          | 0.9632   |
+| 0.0163        | 78.9804 | 3021 | 0.2973          | 0.9706   |
+| 0.0202        | 80.0    | 3060 | 0.3730          | 0.9559   |
+| 0.0368        | 80.9935 | 3098 | 0.2877          | 0.9706   |
+| 0.0374        | 81.9869 | 3136 | 0.4143          | 0.9632   |
+| 0.0296        | 82.9804 | 3174 | 0.2895          | 0.9779   |
+| 0.0405        | 84.0    | 3213 | 0.2927          | 0.9559   |
+| 0.0097        | 84.9935 | 3251 | 0.3179          | 0.9632   |
+| 0.0182        | 85.9869 | 3289 | 0.3047          | 0.9706   |
+| 0.0207        | 86.9804 | 3327 | 0.3018          | 0.9779   |
+| 0.0207        | 88.0    | 3366 | 0.3321          | 0.9632   |
+| 0.003         | 88.9935 | 3404 | 0.3086          | 0.9706   |
+| 0.0157        | 89.9869 | 3442 | 0.2948          | 0.9706   |
+| 0.0428        | 90.9804 | 3480 | 0.3175          | 0.9706   |
+| 0.0189        | 92.0    | 3519 | 0.3240          | 0.9632   |
+| 0.0046        | 92.9935 | 3557 | 0.3414          | 0.9632   |
+| 0.0057        | 93.9869 | 3595 | 0.3329          | 0.9632   |
+| 0.0165        | 94.9804 | 3633 | 0.3240          | 0.9632   |
+| 0.006         | 96.0    | 3672 | 0.3180          | 0.9706   |
+| 0.0172        | 96.9935 | 3710 | 0.3103          | 0.9779   |
+| 0.0109        | 97.9869 | 3748 | 0.3035          | 0.9779   |
+| 0.0172        | 98.9804 | 3786 | 0.3034          | 0.9779   |
+| 0.0219        | 99.3464 | 3800 | 0.3036          | 0.9779   |
+### Framework versions
+- Transformers 4.44.2
+- Pytorch 2.4.0+cu118
+- Datasets 2.21.0
+- Tokenizers 0.19.1

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