Andrew Ng’s VisionAgent: Streamlining Imaginative and prescient AI Options

Mannequin	Recall	Precision	F1 Rating
Touchdown AI	77.0%	82.6%	79.7% (highest)
Microsoft Florence-2	43.4%	36.6%	39.7%
Google OWLv2	81.0%	29.5%	43.2%
Alibaba Qwen2.5-VL-7B-Instruct	26.0%	54.0%	35.1%

4. Key Takeaways

• Touchdown AI’s Agentic Object Detection achieved the best F1 Rating (79.7%), that means it balances precision and recall higher than the others.
• Google OWLv2 had the best recall (81.0%), that means it detects probably the most objects however has decrease precision.
• Alibaba’s Qwen2.5-VL-7B-Instruct had comparatively low recall however reasonable precision.
• Microsoft Florence-2 confirmed decrease efficiency throughout all metrics in comparison with the opposite fashions.

Touchdown AI’s method appears to supply probably the most balanced and efficient efficiency in object detection duties, as mirrored in its highest F1 rating. The opposite fashions have trade-offs, with some excelling in recall however missing in precision, or vice versa.

Sensible Working of VisionAgent

VisionAgent follows a structured course of to supply outputs successfully:

• Add a picture you wish to analyze
• Write a immediate (e.g., “detect folks with glasses”)
• Our AI agent analyzes the picture totally
• Obtain detection outcomes in your picture

1. Immediate: “Detect greens in and across the basket”

Step 1: Dialog

A person initiates a request, describing the imaginative and prescient process in pure language. VisionAgent interprets this immediate and confirms the duty.

Enter Picture

Dialog

“I’ll allow you to write code to detect greens in and across the basket within the picture. I’ll use object detection to establish numerous greens within the scene.”

Step 2: Planner

I have to first perceive what’s within the picture and get options on easy methods to detect greens in and across the basket. I’ll begin by loading the picture and utilizing claude35_vqa and suggestion capabilities.

Planning Steps

• Perceive the picture contents utilizing VQA (Visible Query Answering).
• Generate related options for one of the best detection method.
• Choose instruments that go well with the duty, resembling object detection and color-based classification.

Instance Plan

• Determine all objects within the picture.
• Decide which objects are greens.
• Differentiate greens inside and out of doors the basket.
• Use a nested detection construction to investigate the spatial relationships.

Execution:

picture = load_image('vegetablebasket.jpg')
claude35_vqa('Are you able to describe this picture? What greens do you see in and across the basket?', [image])
suggestion('How can I detect greens in and across the basket on this picture?', [image])

Statement

[claude35_vqa output]
This picture exhibits a lovely array of contemporary greens organized round and in a wicker basket. The greens embody:

1. Contemporary carrots with inexperienced tops
2. Cauliflower
3. Onions
4. Potatoes
5. Broccoli
6. Eggplant
7. Purple tomatoes
8. Yellow squash/zucchini
9. Purple cabbage
10. Inexperienced bell pepper
11. Purple bell pepper
12. Cucumbers
13. Contemporary garlic
14. Contemporary herbs (seems to be parsley)
15. Inexperienced banana peppers or chili peppers

The greens seem like very contemporary and vibrant in coloration, organized on what seems to be like a picket floor or chopping board. The composition exhibits a pleasant number of colours, from deep purples to brilliant reds, oranges, and greens, making it visually interesting and suggesting a wholesome, nutritious number of produce.
[end of claude35_vqa output]

[suggestions]

1. The greens are each in and round a basket, so relative_position would assist in figuring out their places with respect to the basket as an anchor level.

2. nested_structure applies as a result of some greens are nested/contained throughout the basket whereas others are organized outdoors it.

3. coloration could be helpful because the greens are distinctly coloured (crimson tomatoes, orange carrots, inexperienced broccoli, purple cabbage, and so on.), making coloration a key identifier.

4. The issue doesn't contain small textual content, grid patterns, monitoring in video, evaluating sizes particularly, or coping with extraordinarily small objects in a big picture. The depth place is seen however not essential to the detection process.


[suggestion 0]
It's essential to discover the colour of objects within the picture, you should use the next code to assist with this process:

import numpy as np
import cv2

color_ranges = {
    "red_lower": ((0, 100, 100), (int(179 * 20 / 360), 255, 255)),
    "orange": ((int(179 * 21 / 360), 100, 100), (int(179 * 50 / 360), 255, 255)),
    "yellow": ((int(179 * 51 / 360), 100, 100), (int(179 * 70 / 360), 255, 255)),
    "inexperienced": ((int(179 * 71 / 360), 100, 100), (int(179 * 150 / 360), 255, 255)),
    "cyan": ((int(179 * 151 / 360), 100, 100), (int(179 * 180 / 360), 255, 255)),
    "blue": ((int(179 * 181 / 360), 100, 100), (int(179 * 265 / 360), 255, 255)),
    "purple": ((int(179 * 266 / 360), 100, 100), (int(179 * 290 / 360), 255, 255)),
    "pink": ((int(179 * 291 / 360), 100, 100), (int(179 * 330 / 360), 255, 255)),
    "red_upper": ((int(179 * 331 / 360), 100, 100), (179, 255, 255)),
    "white": ((0, 0, 200), (179, 25, 255)),
    "grey": ((0, 0, 50), (179, 50, 200)),
    "black": ((0, 0, 0), (179, 255, 30)),
}

def get_color(picture, color_ranges):
    hsv_image = cv2.cvtColor(picture, cv2.COLOR_RGB2HSV)
    detected_colors = {}
    for coloration, (decrease, higher) in color_ranges.gadgets():
        upper_range = np.array(higher, dtype=np.uint8)
        lower_range = np.array(decrease, dtype=np.uint8)
        masks = cv2.inRange(hsv_image, lower_range, upper_range)
        detected_pixels = cv2.countNonZero(masks)
        detected_colors[color] = detected_pixels

    if "red_lower" in detected_colors and "red_upper" in detected_colors:
        detected_colors["red"] = detected_colors["red_lower"] + detected_colors["red_upper"]
        del detected_colors["red_lower"]
        del detected_colors["red_upper"]
    return sorted(detected_colors, key=detected_colors.get, reverse=True)[0]

[end of suggestion 0]
[suggestion 1]
You are attempting to depend objects inside objects, or a nested construction. You may resolve this by first detecting the outer objects, then cropping the picture to the bounding field of every outer object and detecting the interior objects. You should use the next code to assist with this process:

all_dets = <an object detection software>("object", picture)

peak, width = picture.form[:2]

def space(field):
    return (field[2] - field[0]) * (field[3] - field[1])

# solely verify interior detections on high 25 largest outer detections
largest_dets = sorted(dets, key=lambda x: space(x["bbox"]), reverse=True)[:25]
for det in largest_dets:
    x1 = int(det["bbox"][0] * width)
    y1 = int(det["bbox"][1] * peak)
    x2 = int(det["bbox"][2] * width)
    y2 = int(det["bbox"][3] * peak)

    crop = picture[y1:y2, x1:x2]
    crop_height, crop_width = crop.form[:2]

    inner_dets = <an object detection software>("object", crop)
    for inner_det in inner_dets:
        x1_inner = int(inner_det["bbox"][0] * crop_width)
        y1_inner = int(inner_det["bbox"][1] * crop_height)
        x2_inner = int(inner_det["bbox"][2] * crop_width)
        y2_inner = int(inner_det["bbox"][3] * crop_height)

        bbox = [
            x1 + x1_inner,
            y1 + y1_inner,
            x1 + x2_inner,
            y1 + y2_inner,
        ]
        norm_bbox = [
            bbox[0] / width,
            bbox[1] / peak,
            bbox[2] / width,
            bbox[3] / peak,
        ]
        all_dets.append(
            {
                "label": inner_det["label"],
                "rating": inner_det["score"],
                "bbox": norm_bbox,
            }
        )

[end of suggestion 1]
[suggestion 2]
You are attempting to find an objects relative to 'anchor' objects. The 'anchor' objects may be detected positive, however there are various of the opposite objects and also you solely wish to return those which can be positioned relative to the 'anchor' objects as specified by the person. You should use the next code to assist with this process:

# First discover a mannequin that may detect the situation of the anchor objects
anchor_dets = <a mannequin that may detect the situation of the anchor objects>("anchor object", picture)
# Then discover a mannequin that may detect the situation of the relative objects
relative_dets = <a mannequin that may detect the situation of the relative objects>("relative object", picture)

# This will provide you with relative objects 'above' the anchor objects since it is the
# distance between the decrease left nook of the relative object and the higher left
# nook of the anchor object. The remaining capabilities can be utilized to get the opposite
# relative positions.
def above_distance(box1, box2):
    return (box1["bbox"][0] - box2["bbox"][0]) ** 2 + (
        box1["bbox"][3] - box2["bbox"][1]
    ) ** 2

def below_distance(box1, box2):
    return (box1["bbox"][0] - box2["bbox"][0]) ** 2 + (
        box1["bbox"][1] - box2["bbox"][3]
    ) ** 2

def right_distance(box1, box2):
    return (box1["bbox"][0] - box2["bbox"][2]) ** 2 + (
        box1["bbox"][1] - box2["bbox"][1]
    ) ** 2

def left_distance(box1, box2):
    return (box1["bbox"][2] - box2["bbox"][0]) ** 2 + (
        box1["bbox"][1] - box2["bbox"][1]
    ) ** 2

closest_boxes = []
for anchor_det in anchor_dets:
    # You should use any of the above capabilities to get the relative place
    distances = [
        (relative_det, above_distance(relative_det, anchor_det))
        for relative_det in relative_dets
    ]
    # You need to seize the closest object for every of the anchors. This line will give
    # you the field instantly above the anchor field (or beneath, left, proper relying on
    # the operate used)
    closest_box = min(distances, key=lambda x: x[1])[0]
    closest_boxes.append(closest_box)

[end of suggestion 2]
[end of suggestions]

<depend>9</depend>

Step 3: Execution

The deliberate steps are executed utilizing the VisionAgent Library and Instruments Library. This includes loading the picture, operating fashions, and producing detection outputs.

Planner

Based mostly on Claude’s description and the options, I have to detect greens each in and across the basket. It is a nested construction downside the place I first have to detect the basket after which detect greens in relation to it. Let me first get a software that may detect each the basket and greens.

Instance Execution Code

picture = load_image('vegetablebasket.jpg')
claude35_vqa('Are you able to describe this picture? What greens do you see in and across the basket?', [image])
suggestion('How can I detect greens in and across the basket on this picture?', [image])
get_tool_for_task('Detect basket and greens within the picture', [image])

Statement and Last Output

After execution, VisionAgent produces structured outcomes:

• Detected greens categorized inside and out of doors the basket.
• Bounding field coordinates for every detected vegetable.
• A deployable AI mannequin that can be utilized for real-world functions.

Output

2. Immediate: “Determine crimson automotive within the video”

Enter Video: Video Hyperlink

Step 1: Dialog

I’ll allow you to write code to establish and monitor crimson vehicles within the video. I’ll use object detection and color-based filtering to perform this process.

Step 2: Planner

I have to first perceive what’s within the video and get options on easy methods to establish a crimson automotive. I’ll extract some frames and use claude35_vqa to explain the video content material, then get options.

Step 3: Execution

frames = extract_frames_and_timestamps('videoplayback(4).mp4', 1)
frames = [f["frame"] for f in frames]
claude35_vqa('Are you able to describe this video? Is there a crimson automotive in it?', frames[:5])
suggestion('How can I establish the crimson automotive within the video?', frames[:5])

Statement

[claude35_vqa output]
Sure, there's a crimson automotive (#21) on this video sequence, which seems to be a NASCAR race clip exhibiting an incident between two vehicles - a crimson #21 automotive and a black/gold #3 automotive with Winchester branding. The video exhibits them making contact round flip 4 of the monitor, with the vehicles getting shut to one another and doubtlessly buying and selling paint. The sequence is labeled as "Josh Berry vs Austin Dillon" and exhibits what seems to be a tense racing second between the 2 drivers. The textual content overlays point out "WOAH TURN 4", "CONTACT", "BERRY", "A LOT", and "THERE" because the sequence progresses.
[end of claude35_vqa output]

[suggestions]

Based mostly on the person's request to establish the crimson automotive within the video and the offered video frames, that is primarily a case of:

1. finding_features_with_video_tracking - The sequence exhibits a NASCAR race interplay between two vehicles (Josh Berry and Austin Dillon) the place monitoring the crimson #21 automotive by means of the flip and phone is essential
2. coloration - The precise request mentions figuring out the "crimson automotive", making coloration identification related
3. relative_position - The interplay between the 2 vehicles requires understanding their positions relative to one another by means of the flip and phone

The opposite classes do not apply as a result of:
- This is not about small textual content or giant pictures
- There aren't any grid patterns or lacking components
- This is not about evaluating sizes or nested buildings
- Whereas there's some depth notion in racing, the request is not particularly about figuring out depth place


[suggestion 0]
It's essential to discover the colour of objects within the picture, you should use the next code to assist with this process:

import numpy as np
import cv2

color_ranges = {
    "red_lower": ((0, 100, 100), (int(179 * 20 / 360), 255, 255)),
    "orange": ((int(179 * 21 / 360), 100, 100), (int(179 * 50 / 360), 255, 255)),
    "yellow": ((int(179 * 51 / 360), 100, 100), (int(179 * 70 / 360), 255, 255)),
    "inexperienced": ((int(179 * 71 / 360), 100, 100), (int(179 * 150 / 360), 255, 255)),
    "cyan": ((int(179 * 151 / 360), 100, 100), (int(179 * 180 / 360), 255, 255)),
    "blue": ((int(179 * 181 / 360), 100, 100), (int(179 * 265 / 360), 255, 255)),
    "purple": ((int(179 * 266 / 360), 100, 100), (int(179 * 290 / 360), 255, 255)),
    "pink": ((int(179 * 291 / 360), 100, 100), (int(179 * 330 / 360), 255, 255)),
    "red_upper": ((int(179 * 331 / 360), 100, 100), (179, 255, 255)),
    "white": ((0, 0, 200), (179, 25, 255)),
    "grey": ((0, 0, 50), (179, 50, 200)),
    "black": ((0, 0, 0), (179, 255, 30)),
}

def get_color(picture, color_ranges):
    hsv_image = cv2.cvtColor(picture, cv2.COLOR_RGB2HSV)
    detected_colors = {}
    for coloration, (decrease, higher) in color_ranges.gadgets():
        upper_range = np.array(higher, dtype=np.uint8)
        lower_range = np.array(decrease, dtype=np.uint8)
        masks = cv2.inRange(hsv_image, lower_range, upper_range)
        detected_pixels = cv2.countNonZero(masks)
        detected_colors[color] = detected_pixels

    if "red_lower" in detected_colors and "red_upper" in detected_colors:
        detected_colors["red"] = detected_colors["red_lower"] + detected_colors["red_upper"]
        del detected_colors["red_lower"]
        del detected_colors["red_upper"]
    return sorted(detected_colors, key=detected_colors.get, reverse=True)[0]

[end of suggestion 0]
[suggestion 1]
First attempt to resolve the issue utilizing a VQA software earlier than utilizing the monitoring method for a quicker and simpler answer:

reply = <VQA software to reply your query>("<your immediate right here>", picture)

If that doesn't work, you may monitor the objects within the video after which establish options on these objects. It's essential to first get a software that may monitor objects in a video, after which for every object discover one other software to establish the options on the thing. You should use the next code to assist with this process:

track_predictions = <object monitoring software>("object", video_frames)


# Step 1: undergo every body and every prediction and extract the anticipated bounding containers as crops
obj_to_info = {}
for body, frame_predictions in zip(video_frames, track_predictions):
    for obj in frame_predictions:
        if obj["label"] not in obj_to_info:
            obj_to_info[obj["label"]] = []
        peak, width = body.form[:2]
        crop = body[
            int(obj["bbox"][1] * peak) : int(obj["bbox"][3] * peak),
            int(obj["bbox"][0] * width) : int(obj["bbox"][2] * width),
            :,
        ]
        # For every crop use an object detection software, VQA software or classification software to establish if the thing accommodates the options you need
        output = <software to establish your characteristic or a number of options>("<your characteristic(s) right here>", crop)
        obj_to_info[obj["label"]].prolong(output)

print(f"{len(obj_to_info)} objects tracked")

objects_with_info = set()
for infos in obj_to_info:
    for information in data:
        if data["label"] == "<your characteristic right here>":
            objects_with_info.add(data)
            break

print(f"{len(objects_with_info)} objects with options discovered")

[end of suggestion 1]
[suggestion 2]
You are attempting to find an objects relative to 'anchor' objects. The 'anchor' objects may be detected positive, however there are various of the opposite objects and also you solely wish to return those which can be positioned relative to the 'anchor' objects as specified by the person. You should use the next code to assist with this process:

# First discover a mannequin that may detect the situation of the anchor objects
anchor_dets = <a mannequin that may detect the situation of the anchor objects>("anchor object", picture)
# Then discover a mannequin that may detect the situation of the relative objects
relative_dets = <a mannequin that may detect the situation of the relative objects>("relative object", picture)

# This will provide you with relative objects 'above' the anchor objects since it is the
# distance between the decrease left nook of the relative object and the higher left
# nook of the anchor object. The remaining capabilities can be utilized to get the opposite
# relative positions.
def above_distance(box1, box2):
    return (box1["bbox"][0] - box2["bbox"][0]) ** 2 + (
        box1["bbox"][3] - box2["bbox"][1]
    ) ** 2

def below_distance(box1, box2):
    return (box1["bbox"][0] - box2["bbox"][0]) ** 2 + (
        box1["bbox"][1] - box2["bbox"][3]
    ) ** 2

def right_distance(box1, box2):
    return (box1["bbox"][0] - box2["bbox"][2]) ** 2 + (
        box1["bbox"][1] - box2["bbox"][1]
    ) ** 2

def left_distance(box1, box2):
    return (box1["bbox"][2] - box2["bbox"][0]) ** 2 + (
        box1["bbox"][1] - box2["bbox"][1]
    ) ** 2

closest_boxes = []
for anchor_det in anchor_dets:
    # You should use any of the above capabilities to get the relative place
    distances = [
        (relative_det, above_distance(relative_det, anchor_det))
        for relative_det in relative_dets
    ]
    # You need to seize the closest object for every of the anchors. This line will give
    # you the field instantly above the anchor field (or beneath, left, proper relying on
    # the operate used)
    closest_box = min(distances, key=lambda x: x[1])[0]
    closest_boxes.append(closest_box)

[end of suggestion 2]
[end of suggestions]

<depend>9</depend>

Planner

Based mostly on Claude’s description and the options, I want to trace vehicles within the video and establish the crimson one. Let me first get a software for monitoring vehicles within the video.

Execution

get_tool_for_task('Monitor vehicles in a NASCAR race video', frames[:5])

Output

Conclusion

VisionAgent is remodeling the way in which builders construct, check, and deploy AI-driven imaginative and prescient functions. By automating tedious processes and offering ready-to-use instruments, it considerably reduces growth time whereas guaranteeing high-quality outcomes. Whether or not you’re an AI researcher, a developer, or a enterprise seeking to implement laptop imaginative and prescient options, VisionAgent offers a quick, versatile, and scalable method to obtain your objectives.

With ongoing developments in AI, VisionAgent is predicted to evolve additional, incorporating even extra highly effective fashions and increasing its ecosystem to help a wider vary of functions. Now’s the proper time to discover how VisionAgent can improve your AI-driven imaginative and prescient initiatives.

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