- metadata:
- source: https://deepstack.readthedocs.io/en/latest/object-detection/index.html
- people: [[]]
---
# Object Detection
> ## Excerpt
> The object detection API locates and classifies 80 different kinds of objects in a single image.
---
The object detection API locates and classifies 80 different kinds of objects in a single image.
To use this API, you need to enable the detection API when starting DeepStack
## Starting DeepStack[¶](https://deepstack.readthedocs.io/en/latest/object-detection/index.html#starting-deepstack "Permalink to this headline")
Run the command below as it applies to the version you have installed
```
sudo docker run --gpus all -e VISION-DETECTION=True -v localstorage:/datastore -p 80:5000 deepquestai/deepstack:gpu
```
_Basic Parameters_
**\-e VISION-DETECTION=True** This enables the object detection API.
**\-v localstorage:/datastore** This specifies the local volume where DeepStack will store all data.
**\-p 80:5000** This makes DeepStack accessible via port 80 of the machine.
**Example**
```
import requests
image_data = open("test-image3.jpg","rb").read()
response = requests.post("http://localhost:80/v1/vision/detection",files={"image":image_data}).json()
for object in response["predictions"]:
print(object["label"])
print(response)
```
**Response**
```
dog
person
person
{'predictions': [{'x_max': 819, 'x_min': 633, 'y_min': 354, 'confidence': 99, 'label': 'dog', 'y_max': 546}, {'x_max': 601, 'x_min': 440, 'y_min': 116, 'confidence': 99, 'label': 'person', 'y_max': 516}, {'x_max': 445, 'x_min': 295, 'y_min': 84, 'confidence': 99, 'label': 'person', 'y_max': 514}], 'success': True}
```
We can use the coordinates returned to extract the objects
```
import requests
from PIL import Image
image_data = open("test-image3.jpg","rb").read()
image = Image.open("test-image3.jpg").convert("RGB")
response = requests.post("http://localhost:80/v1/vision/detection",files={"image":image_data}).json()
i = 0
for object in response["predictions"]:
label = object["label"]
y_max = int(object["y_max"])
y_min = int(object["y_min"])
x_max = int(object["x_max"])
x_min = int(object["x_min"])
cropped = image.crop((x_min,y_min,x_max,y_max))
cropped.save("image{}_{}.jpg".format(i,label))
i += 1
```
## Setting Minimum Confidence[¶](https://deepstack.readthedocs.io/en/latest/object-detection/index.html#setting-minimum-confidence "Permalink to this headline")
By default, the minimum confidence for detecting objects is 0.45. The confidence ranges between 0 and 1. If the confidence level for an object falls below the min\_confidence, no object is detected.
The min\_confidence parameter allows you to increase or reduce the minimum confidence.
We lower the confidence allowed below.
```
import requests
image_data = open("test-image3.jpg","rb").read()
response = requests.post("http://localhost:80/v1/vision/detection",
files={"image":image_data},data={"min_confidence":0.30}).json()
```
## CLASSES[¶](https://deepstack.readthedocs.io/en/latest/object-detection/index.html#classes "Permalink to this headline")
The following are the classes of objects DeepStack can detect in images
```
person, bicycle, car, motorcycle, airplane,
bus, train, truck, boat, traffic light, fire hydrant, stop_sign,
parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant,
bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase,
frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove,
skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork,
knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot,
hot dog, pizza, donot, cake, chair, couch, potted plant, bed, dining table,
toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave,
oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear,
hair dryer, toothbrush.
```
## Performance[¶](https://deepstack.readthedocs.io/en/latest/object-detection/index.html#performance "Permalink to this headline")
DeepStack offers three modes allowing you to tradeoff speed for performance. During startup, you can specify performance mode to be , **High** , **Medium** and **Low**.
The default mode is **Medium**.
You can specify a different mode during startup as seen below as seen below
```
sudo docker run --gpus all -e VISION-DETECTION=True -e MODE=High -v localstorage:/datastore -p 80:5000 deepquestai/deepstack:gpu
```
**Speed Modes are not available on the Raspberry PI Version**
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