JamesJayamuni
/

emotion_classification_v1.2

@@ -1,104 +1,104 @@
----
-license: apache-2.0
-base_model: google/vit-base-patch16-224-in21k
-tags:
-- generated_from_trainer
-datasets:
-- imagefolder
-metrics:
-- accuracy
-- precision
-- recall
-- f1
-model-index:
-- name: emotion_classification_v1.2
-  results:
-  - task:
-      name: Image Classification
-      type: image-classification
-    dataset:
-      name: imagefolder
-      type: imagefolder
-      config: default
-      split: train[:5000]
-      args: default
-    metrics:
-    - name: Accuracy
-      type: accuracy
-      value: 0.625
-    - name: Precision
-      type: precision
-      value: 0.620708259363687
-    - name: Recall
-      type: recall
-      value: 0.625
-    - name: F1
-      type: f1
-      value: 0.6034583857987293
----
-<!-- 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_v1.2
-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: 1.2401
-- Accuracy: 0.625
-- Precision: 0.6207
-- Recall: 0.625
-- F1: 0.6035
-## 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
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 15
-### Training results
-| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
-| No log        | 1.0   | 20   | 1.9487          | 0.3312   | 0.3554    | 0.3312 | 0.2830 |
-| No log        | 2.0   | 40   | 1.6735          | 0.4437   | 0.4238    | 0.4437 | 0.4232 |
-| No log        | 3.0   | 60   | 1.5359          | 0.4813   | 0.3990    | 0.4813 | 0.4272 |
-| No log        | 4.0   | 80   | 1.4249          | 0.5      | 0.4178    | 0.5    | 0.4443 |
-| No log        | 5.0   | 100  | 1.3733          | 0.5062   | 0.4753    | 0.5062 | 0.4653 |
-| No log        | 6.0   | 120  | 1.3513          | 0.5188   | 0.5076    | 0.5188 | 0.4908 |
-| No log        | 7.0   | 140  | 1.2377          | 0.6125   | 0.6163    | 0.6125 | 0.5976 |
-| No log        | 8.0   | 160  | 1.2354          | 0.6062   | 0.6131    | 0.6062 | 0.5961 |
-| No log        | 9.0   | 180  | 1.2574          | 0.575    | 0.5847    | 0.575  | 0.5728 |
-| No log        | 10.0  | 200  | 1.2493          | 0.5813   | 0.5912    | 0.5813 | 0.5776 |
-| No log        | 11.0  | 220  | 1.1954          | 0.5813   | 0.5795    | 0.5813 | 0.5730 |
-| No log        | 12.0  | 240  | 1.2283          | 0.5625   | 0.5651    | 0.5625 | 0.5598 |
-| No log        | 13.0  | 260  | 1.1984          | 0.5625   | 0.5800    | 0.5625 | 0.5643 |
-| No log        | 14.0  | 280  | 1.2308          | 0.5437   | 0.5523    | 0.5437 | 0.5414 |
-| No log        | 15.0  | 300  | 1.1665          | 0.5938   | 0.6005    | 0.5938 | 0.5935 |
-### Framework versions
-- Transformers 4.41.2
-- Pytorch 2.3.0
-- Datasets 2.19.1
-- Tokenizers 0.19.1

+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224-in21k
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: emotion_classification_v1.2
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: train[:5000]
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.625
+    - name: Precision
+      type: precision
+      value: 0.620708259363687
+    - name: Recall
+      type: recall
+      value: 0.625
+    - name: F1
+      type: f1
+      value: 0.6034583857987293
+---
+<!-- 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_v1.2
+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: 1.2401
+- Accuracy: 0.625
+- Precision: 0.6207
+- Recall: 0.625
+- F1: 0.6035
+## Model description
+A slightly more accurate model compared to previous 1.1 version. More information needed
+## Intended uses & limitations
+This model is fined tune solely for face emotion recognition.
+## 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
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 15
+### Training results
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| No log        | 1.0   | 20   | 1.9487          | 0.3312   | 0.3554    | 0.3312 | 0.2830 |
+| No log        | 2.0   | 40   | 1.6735          | 0.4437   | 0.4238    | 0.4437 | 0.4232 |
+| No log        | 3.0   | 60   | 1.5359          | 0.4813   | 0.3990    | 0.4813 | 0.4272 |
+| No log        | 4.0   | 80   | 1.4249          | 0.5      | 0.4178    | 0.5    | 0.4443 |
+| No log        | 5.0   | 100  | 1.3733          | 0.5062   | 0.4753    | 0.5062 | 0.4653 |
+| No log        | 6.0   | 120  | 1.3513          | 0.5188   | 0.5076    | 0.5188 | 0.4908 |
+| No log        | 7.0   | 140  | 1.2377          | 0.6125   | 0.6163    | 0.6125 | 0.5976 |
+| No log        | 8.0   | 160  | 1.2354          | 0.6062   | 0.6131    | 0.6062 | 0.5961 |
+| No log        | 9.0   | 180  | 1.2574          | 0.575    | 0.5847    | 0.575  | 0.5728 |
+| No log        | 10.0  | 200  | 1.2493          | 0.5813   | 0.5912    | 0.5813 | 0.5776 |
+| No log        | 11.0  | 220  | 1.1954          | 0.5813   | 0.5795    | 0.5813 | 0.5730 |
+| No log        | 12.0  | 240  | 1.2283          | 0.5625   | 0.5651    | 0.5625 | 0.5598 |
+| No log        | 13.0  | 260  | 1.1984          | 0.5625   | 0.5800    | 0.5625 | 0.5643 |
+| No log        | 14.0  | 280  | 1.2308          | 0.5437   | 0.5523    | 0.5437 | 0.5414 |
+| No log        | 15.0  | 300  | 1.1665          | 0.5938   | 0.6005    | 0.5938 | 0.5935 |
+### Framework versions
+- Transformers 4.41.2
+- Pytorch 2.3.0
+- Datasets 2.19.1
+- Tokenizers 0.19.1