Update README.md

README.md

@@ -7,6 +7,7 @@ tags:
 - generated_from_trainer
 datasets:
 - imagefolder
+- FastJobs/Visual_Emotional_Analysis
 metrics:
 - accuracy
 model-index:
@@ -25,6 +26,7 @@ model-index:
     - name: Accuracy
       type: accuracy
       value: 0.6125
+pipeline_tag: image-classification
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,22 +34,36 @@ should probably proofread and complete it, then remove this comment. -->
 
 # vit-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 FastJobs/Visual_Emotional_Analysis dataset.
+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 [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/FastJobs/Visual_Emotional_Analysis) dataset.
 It achieves the following results on the evaluation set:
 - Loss: 1.3802
 - Accuracy: 0.6125
 
-## Model description
-
-More information needed
-
 ## Intended uses & limitations
 
-More information needed
+### Intended Uses
+- Emotion classification from visual inputs (images).
+
+### Limitations
+- May reflect biases from the training dataset.
+- Performance may degrade in domains outside the training data.
+- Not suitable for critical or sensitive decision-making tasks.
 
 ## Training and evaluation data
 
-More information needed
+This model was trained on the [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/FastJobs/Visual_Emotional_Analysis) dataset. 
+
+The dataset contains:
+- **800 images** annotated with **8 emotion labels**:
+  - Anger
+  - Contempt
+  - Disgust
+  - Fear
+  - Happy
+  - Neutral
+  - Sad
+  - Surprise
+
 
 ## Training procedure
 
@@ -72,10 +88,32 @@ The following hyperparameters were used during training:
 | 0.0474        | 7.5   | 300  | 1.3802          | 0.6125   |
 | 0.0368        | 10.0  | 400  | 1.4388          | 0.5938   |
 
+##  How to use this model
+```python
+from transformers import AutoImageProcessor, ViTForImageClassification
+import torch
+from PIL import Image
+import requests
+
+from huggingface_hub import login
+login(api_key)
+
+image = Image.open("image.jpg").convert("RGB")
+
+image_processor = AutoImageProcessor.from_pretrained("digo-prayudha/vit-emotion-classification")
+model = ViTForImageClassification.from_pretrained("digo-prayudha/vit-emotion-classification")
+
+inputs = image_processor(image, return_tensors="pt")
+
+with torch.no_grad():
+    logits = model(**inputs).logits
 
+predicted_label = logits.argmax(-1).item()
+print(model.config.id2label[predicted_label])
+```
 ### Framework versions
 
 - Transformers 4.47.1
 - Pytorch 2.5.1+cu121
 - Datasets 3.2.0
-- Tokenizers 0.21.0
+- Tokenizers 0.21.0