End of training

README.md

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+---
+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: 1.4782
+- 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: 3e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 64
+- 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
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 2.0776        | 1.0   | 10   | 2.0731          | 0.1437   |
+| 2.0526        | 2.0   | 20   | 2.0567          | 0.1688   |
+| 1.9975        | 3.0   | 30   | 2.0160          | 0.2      |
+| 1.8977        | 4.0   | 40   | 1.9550          | 0.3      |
+| 1.778         | 5.0   | 50   | 1.8805          | 0.3625   |
+| 1.6549        | 6.0   | 60   | 1.8073          | 0.375    |
+| 1.5379        | 7.0   | 70   | 1.7428          | 0.4125   |
+| 1.4241        | 8.0   | 80   | 1.6957          | 0.4062   |
+| 1.3212        | 9.0   | 90   | 1.6550          | 0.45     |
+| 1.2245        | 10.0  | 100  | 1.6271          | 0.4437   |
+| 1.1336        | 11.0  | 110  | 1.5928          | 0.4562   |
+| 1.0483        | 12.0  | 120  | 1.5695          | 0.4688   |
+| 0.9669        | 13.0  | 130  | 1.5452          | 0.4875   |
+| 0.8889        | 14.0  | 140  | 1.5248          | 0.4875   |
+| 0.815         | 15.0  | 150  | 1.5063          | 0.5062   |
+| 0.7466        | 16.0  | 160  | 1.4909          | 0.4938   |
+| 0.6852        | 17.0  | 170  | 1.4782          | 0.525    |
+| 0.6308        | 18.0  | 180  | 1.4615          | 0.5      |
+| 0.5819        | 19.0  | 190  | 1.4541          | 0.5      |
+| 0.5392        | 20.0  | 200  | 1.4458          | 0.5125   |
+| 0.503         | 21.0  | 210  | 1.4393          | 0.5      |
+| 0.4718        | 22.0  | 220  | 1.4289          | 0.5188   |
+| 0.4458        | 23.0  | 230  | 1.4238          | 0.5188   |
+| 0.4234        | 24.0  | 240  | 1.4211          | 0.5125   |
+| 0.405         | 25.0  | 250  | 1.4182          | 0.5      |
+| 0.3905        | 26.0  | 260  | 1.4157          | 0.5062   |
+| 0.379         | 27.0  | 270  | 1.4125          | 0.5062   |
+| 0.3706        | 28.0  | 280  | 1.4119          | 0.5062   |
+| 0.3649        | 29.0  | 290  | 1.4115          | 0.5062   |
+| 0.3618        | 30.0  | 300  | 1.4111          | 0.5062   |
+
+
+### Framework versions
+
+- Transformers 4.46.2
+- Pytorch 2.5.1+cu121
+- Datasets 3.1.0
+- Tokenizers 0.20.3

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