End of training

README.md

@@ -0,0 +1,79 @@
+---
+library_name: transformers
+base_model: motheecreator/vit-Facial-Expression-Recognition
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: vit-Facial-Expression-Recognition
+  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: 0.9320388349514563
+---
+
+<!-- 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-Facial-Expression-Recognition
+
+This model is a fine-tuned version of [motheecreator/vit-Facial-Expression-Recognition](https://huggingface.co/motheecreator/vit-Facial-Expression-Recognition) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1672
+- Accuracy: 0.9320
+
+## 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: 8
+- total_train_batch_size: 256
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 1000
+- num_epochs: 50
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|
+| 0.7209        | 21.6216 | 100  | 0.5301          | 0.8155   |
+| 0.0966        | 43.2432 | 200  | 0.1672          | 0.9320   |
+
+
+### Framework versions
+
+- Transformers 4.45.1
+- Pytorch 2.4.0+cu118
+- Datasets 2.21.0
+- Tokenizers 0.20.0

all_results.json

@@ -1,8 +1,8 @@
 {
-    "epoch": 8.64864864864865,
-    "eval_accuracy": 0.44660194174757284,
-    "eval_loss": 1.8702455759048462,
-    "eval_runtime": 6.0614,
-    "eval_samples_per_second": 16.993,
-    "eval_steps_per_second": 0.66
+    "epoch": 43.24324324324324,
+    "eval_accuracy": 0.9320388349514563,
+    "eval_loss": 0.16715261340141296,
+    "eval_runtime": 5.9264,
+    "eval_samples_per_second": 17.38,
+    "eval_steps_per_second": 0.675
 }

eval_results.json

@@ -1,8 +1,8 @@
 {
-    "epoch": 8.64864864864865,
-    "eval_accuracy": 0.44660194174757284,
-    "eval_loss": 1.8702455759048462,
-    "eval_runtime": 6.0614,
-    "eval_samples_per_second": 16.993,
-    "eval_steps_per_second": 0.66
+    "epoch": 43.24324324324324,
+    "eval_accuracy": 0.9320388349514563,
+    "eval_loss": 0.16715261340141296,
+    "eval_runtime": 5.9264,
+    "eval_samples_per_second": 17.38,
+    "eval_steps_per_second": 0.675
 }