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--- |
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license: apache-2.0 |
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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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- gym |
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- workout |
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- classifier |
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--- |
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# **Gym-Workout-Classifier-SigLIP2** |
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> **Gym-Workout-Classifier-SigLIP2** 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 gym workout exercises 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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barbell biceps curl 0.9613 0.9574 0.9593 493 |
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bench press 0.9402 0.9359 0.9381 437 |
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chest fly machine 0.9694 0.9484 0.9588 368 |
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deadlift 0.9833 0.9542 0.9685 371 |
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decline bench press 0.9884 0.9499 0.9688 359 |
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hammer curl 0.9917 0.9398 0.9651 382 |
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hip thrust 0.9692 0.9717 0.9705 389 |
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incline bench press 0.9297 0.9588 0.9440 510 |
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lat pulldown 0.9607 0.9735 0.9670 452 |
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lateral raises 0.9539 0.9814 0.9674 590 |
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leg extension 0.9573 0.9854 0.9712 410 |
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leg raises 0.9939 0.9109 0.9506 359 |
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plank 0.9828 0.9856 0.9842 695 |
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pull up 0.9882 0.9744 0.9813 430 |
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push up 0.9382 0.9762 0.9568 420 |
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romanian deadlift 0.9617 0.9716 0.9667 388 |
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russian twist 0.8702 0.9918 0.9270 365 |
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shoulder press 0.9499 0.9525 0.9512 358 |
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squat 0.9761 0.9441 0.9598 519 |
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t bar row 0.9806 0.9743 0.9774 467 |
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tricep dips 0.9834 0.9713 0.9773 488 |
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tricep pushdown 0.9837 0.9657 0.9746 437 |
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accuracy 0.9638 9687 |
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macro avg 0.9643 0.9625 0.9630 9687 |
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weighted avg 0.9647 0.9638 0.9639 9687 |
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``` |
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The model categorizes images into 22 workout classes: |
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- **Class 0:** "barbell biceps curl" |
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- **Class 1:** "bench press" |
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- **Class 2:** "chest fly machine" |
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- **Class 3:** "deadlift" |
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- **Class 4:** "decline bench press" |
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- **Class 5:** "hammer curl" |
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- **Class 6:** "hip thrust" |
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- **Class 7:** "incline bench press" |
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- **Class 8:** "lat pulldown" |
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- **Class 9:** "lateral raises" |
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- **Class 10:** "leg extension" |
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- **Class 11:** "leg raises" |
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- **Class 12:** "plank" |
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- **Class 13:** "pull up" |
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- **Class 14:** "push up" |
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- **Class 15:** "romanian deadlift" |
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- **Class 16:** "russian twist" |
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- **Class 17:** "shoulder press" |
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- **Class 18:** "squat" |
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- **Class 19:** "t bar row" |
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- **Class 20:** "tricep dips" |
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- **Class 21:** "tricep pushdown" |
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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/Gym-Workout-Classifier-SigLIP2" |
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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 workout_classification(image): |
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"""Predicts workout exercise classification for an 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": "barbell biceps curl", "1": "bench press", "2": "chest fly machine", "3": "deadlift", |
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"4": "decline bench press", "5": "hammer curl", "6": "hip thrust", "7": "incline bench press", |
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"8": "lat pulldown", "9": "lateral raises", "10": "leg extension", "11": "leg raises", |
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"12": "plank", "13": "pull up", "14": "push up", "15": "romanian deadlift", |
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"16": "russian twist", "17": "shoulder press", "18": "squat", "19": "t bar row", |
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"20": "tricep dips", "21": "tricep pushdown" |
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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=workout_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="Gym Workout Classification", |
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description="Upload an image to classify the workout exercise." |
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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 **Gym-Workout-Classifier-SigLIP2** model is designed to classify different gym exercises based on images. Potential use cases include: |
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- **Workout Tracking:** Identifying exercises performed during a workout session. |
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- **Personal Training Assistance:** Helping trainers analyze and correct exercise form. |
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- **Gym Activity Monitoring:** Automating exercise logging and analysis in fitness apps. |
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- **AI-Powered Fitness Coaching:** Supporting AI-based fitness programs with real-time workout recognition. |
