Model save

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: google/vit-base-patch16-224
+tags:
+- generated_from_trainer
+datasets:
+- webdataset
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: frost-vision-v2-google_vit-base-patch16-224
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: webdataset
+      type: webdataset
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9423188405797102
+    - name: F1
+      type: f1
+      value: 0.8589652728561304
+    - name: Precision
+      type: precision
+      value: 0.8795355587808418
+    - name: Recall
+      type: recall
+      value: 0.8393351800554016
+---
+
+<!-- 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. -->
+
+# frost-vision-v2-google_vit-base-patch16-224
+
+This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the webdataset dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1664
+- Accuracy: 0.9423
+- F1: 0.8590
+- Precision: 0.8795
+- Recall: 0.8393
+
+## 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: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 30
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.3416        | 1.1494  | 100  | 0.3273          | 0.8771   | 0.6124 | 0.9005    | 0.4640 |
+| 0.2215        | 2.2989  | 200  | 0.2187          | 0.9183   | 0.7902 | 0.8537    | 0.7355 |
+| 0.1753        | 3.4483  | 300  | 0.1899          | 0.9238   | 0.8098 | 0.8472    | 0.7756 |
+| 0.1656        | 4.5977  | 400  | 0.1732          | 0.9272   | 0.8175 | 0.8606    | 0.7784 |
+| 0.1288        | 5.7471  | 500  | 0.1562          | 0.9359   | 0.8381 | 0.8896    | 0.7922 |
+| 0.1323        | 6.8966  | 600  | 0.1597          | 0.9322   | 0.8326 | 0.8609    | 0.8061 |
+| 0.1004        | 8.0460  | 700  | 0.1613          | 0.9316   | 0.8324 | 0.8542    | 0.8116 |
+| 0.0956        | 9.1954  | 800  | 0.1612          | 0.9336   | 0.8368 | 0.8620    | 0.8130 |
+| 0.0841        | 10.3448 | 900  | 0.1621          | 0.9345   | 0.8383 | 0.8669    | 0.8116 |
+| 0.0764        | 11.4943 | 1000 | 0.1586          | 0.9359   | 0.8438 | 0.8615    | 0.8269 |
+| 0.0726        | 12.6437 | 1100 | 0.1546          | 0.9420   | 0.8594 | 0.8729    | 0.8463 |
+| 0.0732        | 13.7931 | 1200 | 0.1529          | 0.9409   | 0.8565 | 0.87      | 0.8435 |
+| 0.0626        | 14.9425 | 1300 | 0.1589          | 0.9377   | 0.8485 | 0.8637    | 0.8338 |
+| 0.0481        | 16.0920 | 1400 | 0.1612          | 0.9394   | 0.8510 | 0.8767    | 0.8269 |
+| 0.0507        | 17.2414 | 1500 | 0.1679          | 0.9339   | 0.8394 | 0.8539    | 0.8255 |
+| 0.0446        | 18.3908 | 1600 | 0.1623          | 0.9417   | 0.8597 | 0.8664    | 0.8532 |
+| 0.0498        | 19.5402 | 1700 | 0.1625          | 0.9417   | 0.8601 | 0.8643    | 0.8560 |
+| 0.0458        | 20.6897 | 1800 | 0.1601          | 0.9397   | 0.8533 | 0.8693    | 0.8380 |
+| 0.0307        | 21.8391 | 1900 | 0.1626          | 0.9432   | 0.8637 | 0.8673    | 0.8601 |
+| 0.0334        | 22.9885 | 2000 | 0.1621          | 0.9443   | 0.8642 | 0.8829    | 0.8463 |
+| 0.0339        | 24.1379 | 2100 | 0.1680          | 0.9435   | 0.8645 | 0.8675    | 0.8615 |
+| 0.0222        | 25.2874 | 2200 | 0.1656          | 0.9394   | 0.8537 | 0.8628    | 0.8449 |
+| 0.026         | 26.4368 | 2300 | 0.1687          | 0.9386   | 0.8515 | 0.8612    | 0.8421 |
+| 0.0353        | 27.5862 | 2400 | 0.1666          | 0.9403   | 0.8555 | 0.8665    | 0.8449 |
+| 0.0294        | 28.7356 | 2500 | 0.1660          | 0.9429   | 0.8614 | 0.8755    | 0.8476 |
+| 0.0243        | 29.8851 | 2600 | 0.1664          | 0.9423   | 0.8590 | 0.8795    | 0.8393 |
+
+
+### Framework versions
+
+- Transformers 4.46.2
+- Pytorch 2.5.1+cu121
+- Datasets 3.1.0
+- Tokenizers 0.20.3

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