Delete yolo

yolo/README.md

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-# YOLOS Object Detection with Gradio
-
-This Gradio demo uses the pretrained YOLOS transformer (`hustvl/yolos-base`) from Hugging Face Transformers to detect objects in uploaded images.
-
-## Features
-- Upload any image
-- Detect objects with YOLOS
-- See bounding boxes and object labels
-- Adjustable confidence threshold
-
-## Run Locally
-```bash
-pip install -r requirements.txt
-python app.py
-```
-
-## Powered By
-- [Hugging Face Transformers](https://huggingface.co/transformers/)
-- [Gradio](https://gradio.app/)

yolo/app.py

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-
-# STEP 1: Install dependencies
-# Note: Use requirements.txt when deploying
-import torch
-from transformers import AutoImageProcessor, AutoModelForObjectDetection
-from PIL import Image, ImageDraw, ImageFont
-import gradio as gr
-
-# STEP 2: Load YOLOS model & processor
-model_name = "hustvl/yolos-base"
-processor = AutoImageProcessor.from_pretrained(model_name)
-model = AutoModelForObjectDetection.from_pretrained(model_name)
-model.eval()
-
-if torch.cuda.is_available():
-    model.to(torch.float16).to("cuda")
-
-# STEP 3: Detection function with object name return
-def detect_yolos(image, threshold=0.5):
-    image = image.convert("RGB")
-    inputs = processor(images=image, return_tensors="pt").to(model.device)
-    with torch.no_grad():
-        outputs = model(**inputs)
-
-    target_sizes = torch.tensor([image.size[::-1]], device=model.device)
-    results = processor.post_process_object_detection(outputs, threshold=threshold, target_sizes=target_sizes)[0]
-
-    draw = ImageDraw.Draw(image)
-    font = ImageFont.load_default()
-    detected_labels = []
-
-    for score, label_idx, box in zip(results["scores"], results["labels"], results["boxes"]):
-        label = model.config.id2label[label_idx.item()]
-        detected_labels.append(label)
-        box = [round(i, 2) for i in box.tolist()]
-        draw.rectangle(box, outline="green", width=2)
-        draw.text((box[0], box[1] - 10), f"{label}: {score:.2f}", fill="green", font=font)
-
-    label_summary = ", ".join(set(detected_labels)) if detected_labels else "No objects detected."
-    return image, label_summary
-
-# STEP 4: Gradio UI
-demo = gr.Interface(
-    fn=detect_yolos,
-    inputs=[
-        gr.Image(type="pil", label="Upload Image"),
-        gr.Slider(0, 1, value=0.5, label="Confidence Threshold")
-    ],
-    outputs=[
-        gr.Image(type="pil", label="Image with Detections"),
-        gr.Textbox(label="Detected Object Names")
-    ],
-    title="📦 YOLOS Object Detection + Label List",
-    description="Detects objects using YOLOS and lists all object names in a textbox."
-)
-
-demo.launch()

yolo/huggingface.yml

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-sdk: gradio
-sdk_version: 4.27.0
-python_version: 3.10
-app_file: app.py
-title: YOLOS Object Detection
-emoji: 📦
-color_from: green
-color_to: blue
-license: mit

yolo/requirements.txt

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-gradio
-torch
-torchvision
-transformers
-pillow