Sync widgets demo

packages/tasks/src/tasks/zero-shot-object-detection/about.md

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+## Use Cases
+
+### Object Search
+
+Zero-shot object detection models can be used in image search. Smartphones, for example, use zero-shot object detection models to detect entities (such as specific places or objects) and allow the user to search for the entity on the internet.
+
+### Object Counting
+
+Zero-shot object detection models are used to count instances of objects in a given image. This can include counting the objects in warehouses or stores or the number of visitors in a store. They are also used to manage crowds at events to prevent disasters.
+
+## Inference
+
+You can infer with zero-shot object detection models through the `zero-shot-object-detection` pipeline. When calling the pipeline, you just need to specify a path or HTTP link to an image and the candidate labels.
+
+```python
+from transformers import pipeline
+from PIL import Image
+
+image = Image.open("my-image.png").convert("RGB")
+
+detector = pipeline(model="google/owlvit-base-patch32", task="zero-shot-object-detection")
+
+predictions = detector(
+    image,
+    candidate_labels=["a photo of a cat", "a photo of a dog"],
+)
+
+# [{'score': 0.95,
+#   'label': 'a photo of a cat',
+#   'box': {'xmin': 180, 'ymin': 71, 'xmax': 271, 'ymax': 178}},
+#   ...
+# ]
+```
+
+# Useful Resources
+
+- [Zero-shot object detection task guide](https://huggingface.co/docs/transformers/tasks/zero_shot_object_detection)
+
+This page was made possible thanks to the efforts of [Victor Guichard](https://huggingface.co/VictorGuichard)

packages/tasks/src/tasks/zero-shot-object-detection/data.ts

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+import type { TaskDataCustom } from "..";
+
+const taskData: TaskDataCustom = {
+	datasets: [],
+	demo: {
+		inputs: [
+			{
+				filename: "zero-shot-object-detection-input.jpg",
+				type: "img",
+			},
+			{
+				label: "Classes",
+				content: "cat, dog, bird",
+				type: "text",
+			},
+		],
+		outputs: [
+			{
+				filename: "zero-shot-object-detection-output.jpg",
+				type: "img",
+			},
+		],
+	},
+	metrics: [
+		{
+			description:
+				"The Average Precision (AP) metric is the Area Under the PR Curve (AUC-PR). It is calculated for each class separately",
+			id: "Average Precision",
+		},
+		{
+			description: "The Mean Average Precision (mAP) metric is the overall average of the AP values",
+			id: "Mean Average Precision",
+		},
+		{
+			description:
+				"The APα metric is the Average Precision at the IoU threshold of a α value, for example, AP50 and AP75",
+			id: "APα",
+		},
+	],
+	models: [
+		{
+			description: "Solid zero-shot object detection model that uses CLIP as backbone.",
+			id: "google/owlvit-base-patch32",
+		},
+		{
+			description: "The improved version of the owlvit model.",
+			id: "google/owlv2-base-patch16-ensemble",
+		},
+	],
+	spaces: [],
+	summary:
+		"Zero-shot object detection is a computer vision task to detect objects and their classes in images, without any prior training or knowledge of the classes. Zero-shot object detection models receive an image as input, as well as a list of candidate classes, and output the bounding boxes and labels where the objects have been detected.",
+	widgetModels: [],
+	youtubeId: "",
+};
+
+export default taskData;