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Gym-Workout-Classifier-SigLIP2

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.

 Classification Report:
 precision recall f1-score support
 
 barbell biceps curl 0.9613 0.9574 0.9593 493
 bench press 0.9402 0.9359 0.9381 437
 chest fly machine 0.9694 0.9484 0.9588 368
 deadlift 0.9833 0.9542 0.9685 371
 decline bench press 0.9884 0.9499 0.9688 359
 hammer curl 0.9917 0.9398 0.9651 382
 hip thrust 0.9692 0.9717 0.9705 389
 incline bench press 0.9297 0.9588 0.9440 510
 lat pulldown 0.9607 0.9735 0.9670 452
 lateral raises 0.9539 0.9814 0.9674 590
 leg extension 0.9573 0.9854 0.9712 410
 leg raises 0.9939 0.9109 0.9506 359
 plank 0.9828 0.9856 0.9842 695
 pull up 0.9882 0.9744 0.9813 430
 push up 0.9382 0.9762 0.9568 420
 romanian deadlift 0.9617 0.9716 0.9667 388
 russian twist 0.8702 0.9918 0.9270 365
 shoulder press 0.9499 0.9525 0.9512 358
 squat 0.9761 0.9441 0.9598 519
 t bar row 0.9806 0.9743 0.9774 467
 tricep dips 0.9834 0.9713 0.9773 488
 tricep pushdown 0.9837 0.9657 0.9746 437
 
 accuracy 0.9638 9687
 macro avg 0.9643 0.9625 0.9630 9687
 weighted avg 0.9647 0.9638 0.9639 9687

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The model categorizes images into 22 workout classes:

• Class 0: "barbell biceps curl"
• Class 1: "bench press"
• Class 2: "chest fly machine"
• Class 3: "deadlift"
• Class 4: "decline bench press"
• Class 5: "hammer curl"
• Class 6: "hip thrust"
• Class 7: "incline bench press"
• Class 8: "lat pulldown"
• Class 9: "lateral raises"
• Class 10: "leg extension"
• Class 11: "leg raises"
• Class 12: "plank"
• Class 13: "pull up"
• Class 14: "push up"
• Class 15: "romanian deadlift"
• Class 16: "russian twist"
• Class 17: "shoulder press"
• Class 18: "squat"
• Class 19: "t bar row"
• Class 20: "tricep dips"
• Class 21: "tricep pushdown"

Dataset ID2LABEL

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("YOUR-DATASET-HERE")

# Extract unique labels
labels = dataset["train"].features["label"].names

# Create id2label mapping
id2label = {str(i): label for i, label in enumerate(labels)}

# Print the mapping
print(id2label)

Run with Transformers🤗

!pip install -q transformers torch pillow gradio

import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/Gym-Workout-Classifier-SigLIP2"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

def workout_classification(image):
 """Predicts workout exercise classification for an image."""
 image = Image.fromarray(image).convert("RGB")
 inputs = processor(images=image, return_tensors="pt")
 
 with torch.no_grad():
 outputs = model(**inputs)
 logits = outputs.logits
 probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
 
 labels = {
 "0": "barbell biceps curl", "1": "bench press", "2": "chest fly machine", "3": "deadlift",
 "4": "decline bench press", "5": "hammer curl", "6": "hip thrust", "7": "incline bench press",
 "8": "lat pulldown", "9": "lateral raises", "10": "leg extension", "11": "leg raises",
 "12": "plank", "13": "pull up", "14": "push up", "15": "romanian deadlift",
 "16": "russian twist", "17": "shoulder press", "18": "squat", "19": "t bar row",
 "20": "tricep dips", "21": "tricep pushdown"
 }
 predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
 
 return predictions

# Create Gradio interface
iface = gr.Interface(
 fn=workout_classification,
 inputs=gr.Image(type="numpy"),
 outputs=gr.Label(label="Prediction Scores"),
 title="Gym Workout Classification",
 description="Upload an image to classify the workout exercise."
)

# Launch the app
if __name__ == "__main__":
 iface.launch()

Intended Use:

The Gym-Workout-Classifier-SigLIP2 model is designed to classify different gym exercises based on images. Potential use cases include:

• Workout Tracking: Identifying exercises performed during a workout session.
• Personal Training Assistance: Helping trainers analyze and correct exercise form.
• Gym Activity Monitoring: Automating exercise logging and analysis in fitness apps.
• AI-Powered Fitness Coaching: Supporting AI-based fitness programs with real-time workout recognition.

Base model

google/siglip2-base-patch16-224

(119)

Test finetune • 7 items • Updated 1 day ago • 2