End of training

README.md

@@ -0,0 +1,101 @@
+---
+library_name: transformers
+license: apache-2.0
+base_model: google/vit-base-patch16-224-in21k
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: vit-emotion-classifier
+  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: 0.525
+---
+
+<!-- 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-emotion-classifier
+
+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 imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.3506
+- Accuracy: 0.525
+
+## 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: 2e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 25
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 1.0   | 20   | 2.0656          | 0.1938   |
+| No log        | 2.0   | 40   | 2.0408          | 0.2625   |
+| No log        | 3.0   | 60   | 1.9845          | 0.275    |
+| No log        | 4.0   | 80   | 1.8774          | 0.35     |
+| 1.9717        | 5.0   | 100  | 1.7409          | 0.45     |
+| 1.9717        | 6.0   | 120  | 1.6349          | 0.4437   |
+| 1.9717        | 7.0   | 140  | 1.5541          | 0.4437   |
+| 1.9717        | 8.0   | 160  | 1.5007          | 0.5188   |
+| 1.9717        | 9.0   | 180  | 1.4531          | 0.525    |
+| 1.4968        | 10.0  | 200  | 1.4263          | 0.5312   |
+| 1.4968        | 11.0  | 220  | 1.3975          | 0.5188   |
+| 1.4968        | 12.0  | 240  | 1.3915          | 0.525    |
+| 1.4968        | 13.0  | 260  | 1.3270          | 0.5375   |
+| 1.4968        | 14.0  | 280  | 1.3360          | 0.575    |
+| 1.2146        | 15.0  | 300  | 1.3185          | 0.5437   |
+| 1.2146        | 16.0  | 320  | 1.3288          | 0.55     |
+| 1.2146        | 17.0  | 340  | 1.3262          | 0.5563   |
+| 1.2146        | 18.0  | 360  | 1.3142          | 0.55     |
+| 1.2146        | 19.0  | 380  | 1.2982          | 0.5625   |
+| 1.0644        | 20.0  | 400  | 1.2704          | 0.5625   |
+| 1.0644        | 21.0  | 420  | 1.2862          | 0.55     |
+| 1.0644        | 22.0  | 440  | 1.2941          | 0.55     |
+| 1.0644        | 23.0  | 460  | 1.2876          | 0.5312   |
+| 1.0644        | 24.0  | 480  | 1.3066          | 0.5625   |
+| 1.0161        | 25.0  | 500  | 1.2734          | 0.55     |
+
+
+### Framework versions
+
+- Transformers 4.48.3
+- Pytorch 2.5.1+cu124
+- Datasets 3.3.2
+- Tokenizers 0.21.0

model.safetensors

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