Edge Impulse Experts / AI-driven Ancillary Lab Assistant w/ UNO Q Public

This lists all test data. You can manage this data through Data acquisition.

Test data

Set the 'expected outcome' for each sample to the desired outcome to automatically score the impulse.

Apply filters

Sample name	Expected outcome	F1 score
skeleton_model_20260401_110637	skeleton_model	100%	Show classification Show in data acquisition Show in feature explorer
dynamometer_20260401_111030	dynamometer	0%	Show classification Show in data acquisition Show in feature explorer
alcohol_burner_20260401_110352	alcohol_burner	100%	Show classification Show in data acquisition Show in feature explorer
dynamometer_20260401_111029	dynamometer	0%	Show classification Show in data acquisition Show in feature explorer
microscope_20260401_110756	microscope	100%	Show classification Show in data acquisition Show in feature explorer
microscope_20260401_110757	microscope	100%	Show classification Show in data acquisition Show in feature explorer
bunsen_burner_20260401_110538	bunsen_burner	100%	Show classification Show in data acquisition Show in feature explorer
alcohol_burner_20260401_110350	alcohol_burner	100%	Show classification Show in data acquisition Show in feature explorer
bunsen_burner_20260401_110539	bunsen_burner	0%	Show classification Show in data acquisition Show in feature explorer
skeleton_model_20260401_110636	skeleton_model	100%	Show classification Show in data acquisition Show in feature explorer

Model testing output

Results

Accuracy

70.00%

Metrics for Object detection

Metric	Value
Precision (non-background)	1.00
Recall (non-background)	0.73
F1 Score (non-background)	0.84

Feature explorer

Every learning block has a threshold. This can be the minimum confidence that a neural network needs to have, or the maximum anomaly score before a sample is tagged as an anomaly. You can configure these thresholds to tweak the sensitivity of these learning blocks. This affects both live classification and model testing.

Object detection

Score threshold

Should be numeric

These users will be notified over email when this job finishes.

	Name
	Kutluhan Aktar

We're always looking for ways to improve Edge Impulse. If you have any feedback, please let us know!

Subject

This field is required

How was your experience?

This field is required

You'll need a free Edge Impulse account to clone this project. Create an account to add your own data, modify models, and join the largest community of embedded machine learning developers!

Target device

Define your target device requirements to inform model optimizations and performance calculations. No device yet? Use the default settings which you can change at any time.

Target device

Processor family

Clock rate

| MHz

Max

Custom device name

Application budget

Specify the available RAM and ROM for the model's operation, along with the maximum allowed latency for your specific application. Not sure yet? Start with the defaults and modify them later on.

RAM

| KB

Max

ROM

| KB

Max

Latency

| ms

Max

YEARLY

$400 /month

billed annually

SAVE 15%

MONTHLY

$475 /month

billed monthly

First name*

Please enter your first name

Last name*

Please enter your last name

Company email*

Please enter a valid email address

Company name*

Please enter your company name

Country*

Could you please provide more details about your use case, including any specific hardware requirements and the challenges you're currently facing?*

Please enter your use case details

Edge Impulse needs the contact information you provide to us to contact you about our products and services. You may unsubscribe from these communications at any time. For information on how to unsubscribe, as well as our privacy practices and commitment to protecting your privacy, please review our Privacy Policy.

Test data

Apply filters

Add data

Model testing output

Results

Accuracy

Metrics for Object detection

Confusion matrix

Feature explorer

Test data

Model variants

Apply filters

Add data

Model testing output

Results

Accuracy

Metrics for Object detection

Confusion matrix

Feature explorer

Settings