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README.md

@@ -1,13 +1,19 @@
----
-title: ObjectDetector YOLOSCOPE
-emoji: 🐢
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-sdk_version: 5.38.0
-app_file: app.py
-pinned: false
-short_description: It used to detect objects in image  using hustvl/yolos-base
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 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/)

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()

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

requirements.txt

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+gradio
+torch
+torchvision
+transformers
+pillow