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--- |
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license: apache-2.0 |
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datasets: |
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- strangerzonehf/Super-Emojies-DLC |
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language: |
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- en |
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base_model: |
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- google/siglip2-base-patch16-224 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- Emoji-Scope |
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- Google |
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- CrossEmoji Classifier |
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--- |
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# **Emoji-Scope** |
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> **Emoji-Scope** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a single-label classification task. It is designed to classify emoji images into different style categories using the **SiglipForImageClassification** architecture. |
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```py |
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Classification Report: |
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precision recall f1-score support |
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Apple Style 0.9336 0.8538 0.8919 725 |
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DoCoMo Style 0.9130 0.8400 0.8750 100 |
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Facebook Style 0.8713 0.8915 0.8813 691 |
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Gmail Style 0.8289 0.8750 0.8514 288 |
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Google Style 0.8725 0.9505 0.9098 727 |
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JoyPixels Style 0.8960 0.9614 0.9276 726 |
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KDDI Style 0.9444 0.9333 0.9389 255 |
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Samsung Style 0.9584 0.9681 0.9632 690 |
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SoftBank Style 0.8407 0.8053 0.8226 190 |
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Twitter Style 0.9939 0.8900 0.9390 727 |
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Windows Style 0.9949 0.9949 0.9949 583 |
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accuracy 0.9200 5702 |
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macro avg 0.9134 0.9058 0.9087 5702 |
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weighted avg 0.9222 0.9200 0.9201 5702 |
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``` |
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The model categorizes images into eleven emoji styles: |
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- **Class 0:** "Apple Style" |
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- **Class 1:** "DoCoMo Style" |
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- **Class 2:** "Facebook Style" |
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- **Class 3:** "Gmail Style" |
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- **Class 4:** "Google Style" |
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- **Class 5:** "JoyPixels Style" |
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- **Class 6:** "KDDI Style" |
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- **Class 7:** "Samsung Style" |
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- **Class 8:** "SoftBank Style" |
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- **Class 9:** "Twitter Style" |
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- **Class 10:** "Windows Style" |
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# **Run with Transformers🤗** |
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```python |
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!pip install -q transformers torch pillow gradio |
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``` |
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```python |
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import gradio as gr |
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from transformers import AutoImageProcessor |
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from transformers import SiglipForImageClassification |
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from transformers.image_utils import load_image |
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from PIL import Image |
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import torch |
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# Load model and processor |
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model_name = "prithivMLmods/Emoji-Scope" |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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def emoji_classification(image): |
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"""Predicts the style category of an emoji image.""" |
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image = Image.fromarray(image).convert("RGB") |
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inputs = processor(images=image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() |
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labels = { |
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"0": "Apple Style", |
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"1": "DoCoMo Style", |
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"2": "Facebook Style", |
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"3": "Gmail Style", |
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"4": "Google Style", |
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"5": "JoyPixels Style", |
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"6": "KDDI Style", |
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"7": "Samsung Style", |
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"8": "SoftBank Style", |
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"9": "Twitter Style", |
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"10": "Windows Style" |
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} |
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predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))} |
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return predictions |
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# Create Gradio interface |
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iface = gr.Interface( |
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fn=emoji_classification, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(label="Prediction Scores"), |
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title="Emoji Style Classification", |
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description="Upload an emoji image to classify its style." |
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) |
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# Launch the app |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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# **Intended Use:** |
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The **Emoji-Scope** model is designed to classify emoji images based on different style categories. Potential use cases include: |
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- **Emoji Standardization:** Identifying different emoji styles across platforms. |
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- **User Experience Design:** Helping developers ensure consistency in emoji usage. |
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- **Digital Art & Design:** Assisting artists in selecting preferred emoji styles. |
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- **Educational Purposes:** Teaching differences in emoji representation. |
