Model save

README.md

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+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224-in21k
+tags:
+- generated_from_trainer
+datasets:
+- webdataset
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: vit-base-patch16-224-in21k-finetuned_v2024-7-24-frost
+  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: 0.9530973451327434
+    - name: F1
+      type: f1
+      value: 0.8798185941043084
+    - name: Precision
+      type: precision
+      value: 0.8858447488584474
+    - name: Recall
+      type: recall
+      value: 0.8738738738738738
+---
+
+<!-- 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. -->
+
+# vit-base-patch16-224-in21k-finetuned_v2024-7-24-frost
+
+This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the webdataset dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1391
+- Accuracy: 0.9531
+- F1: 0.8798
+- Precision: 0.8858
+- Recall: 0.8739
+
+## 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: 0.0002
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 17
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.3281        | 1.5625  | 100  | 0.3177          | 0.9009   | 0.6957 | 0.8767    | 0.5766 |
+| 0.2532        | 3.125   | 200  | 0.2424          | 0.9177   | 0.7832 | 0.8116    | 0.7568 |
+| 0.1762        | 4.6875  | 300  | 0.1849          | 0.9407   | 0.8453 | 0.8673    | 0.8243 |
+| 0.1525        | 6.25    | 400  | 0.1834          | 0.9257   | 0.8056 | 0.8286    | 0.7838 |
+| 0.1447        | 7.8125  | 500  | 0.1612          | 0.9416   | 0.8472 | 0.8714    | 0.8243 |
+| 0.1114        | 9.375   | 600  | 0.1522          | 0.9434   | 0.8545 | 0.8624    | 0.8468 |
+| 0.1004        | 10.9375 | 700  | 0.1525          | 0.9451   | 0.8571 | 0.8774    | 0.8378 |
+| 0.0831        | 12.5    | 800  | 0.1442          | 0.9513   | 0.8741 | 0.8884    | 0.8604 |
+| 0.0654        | 14.0625 | 900  | 0.1378          | 0.9496   | 0.8690 | 0.8873    | 0.8514 |
+| 0.0583        | 15.625  | 1000 | 0.1391          | 0.9531   | 0.8798 | 0.8858    | 0.8739 |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.3.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

model.safetensors

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