End of training

README.md

@@ -1,88 +1,93 @@
----
-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: emotion-classification
-  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: 0.53125
----
-
-<!-- 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. -->
-
-# emotion-classification
-
-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: 1.3928
-- Accuracy: 0.5312
-
-## 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: Use OptimizerNames.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: 10
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| No log        | 1.0   | 5    | 1.7580          | 0.4625   |
-| 1.6979        | 2.0   | 10   | 1.6392          | 0.5      |
-| 1.6979        | 3.0   | 15   | 1.5611          | 0.4813   |
-| 1.5214        | 4.0   | 20   | 1.4991          | 0.5312   |
-| 1.5214        | 5.0   | 25   | 1.4644          | 0.5188   |
-| 1.3978        | 6.0   | 30   | 1.4421          | 0.5375   |
-| 1.3978        | 7.0   | 35   | 1.4245          | 0.5188   |
-| 1.3172        | 8.0   | 40   | 1.3936          | 0.5312   |
-| 1.3172        | 9.0   | 45   | 1.3925          | 0.5563   |
-| 1.2726        | 10.0  | 50   | 1.3728          | 0.6125   |
-
-
-### Framework versions
-
-- Transformers 4.52.4
-- Pytorch 2.7.1+cu128
-- Datasets 3.6.0
-- Tokenizers 0.21.1
+---
+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: emotion-classification
+  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: 0.51875
+---
+
+<!-- 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. -->
+
+# emotion-classification
+
+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: 1.3560
+- Accuracy: 0.5188
+
+## 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: Use OptimizerNames.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: 15
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 1.0   | 5    | 1.6699          | 0.4313   |
+| 1.5821        | 2.0   | 10   | 1.6118          | 0.4562   |
+| 1.5821        | 3.0   | 15   | 1.5550          | 0.475    |
+| 1.445         | 4.0   | 20   | 1.5128          | 0.5062   |
+| 1.445         | 5.0   | 25   | 1.4508          | 0.5375   |
+| 1.3202        | 6.0   | 30   | 1.4364          | 0.5      |
+| 1.3202        | 7.0   | 35   | 1.3776          | 0.575    |
+| 1.2242        | 8.0   | 40   | 1.3966          | 0.5      |
+| 1.2242        | 9.0   | 45   | 1.3724          | 0.525    |
+| 1.1589        | 10.0  | 50   | 1.3483          | 0.525    |
+| 1.1589        | 11.0  | 55   | 1.3186          | 0.5687   |
+| 1.0962        | 12.0  | 60   | 1.3295          | 0.5375   |
+| 1.0962        | 13.0  | 65   | 1.3058          | 0.5875   |
+| 1.0542        | 14.0  | 70   | 1.3296          | 0.5375   |
+| 1.0542        | 15.0  | 75   | 1.3185          | 0.5813   |
+
+
+### Framework versions
+
+- Transformers 4.52.4
+- Pytorch 2.7.1+cu128
+- Datasets 3.6.0
+- Tokenizers 0.21.1

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