# Choose Your Tool There are various criteria to consider when selecting a Deep Learning tool for your application, such as the type of information you want to extract from your images, the inference speed, the time you have to annotate ground truth, etc. #### Comparison of the Deep Learning tools | | EasyClassify | EasySegment Supervised | EasySegment Unsupervised | EasyLocate BBox | EasyLocate Interest Point | |-------------------------|----------------------------------------------------------------------------------------------|--------------------------|--------------------------------------------------------------------|-------------------------------------------------------------------|-----------------------------------| | Inference speed | ++++ | + | ++ | +++ | +++ | | Prediction | Unique image label | Labeled segmentation map | OK/NOK segmentation map | Labeled bounding box | Labeled points | | Additional capability | [Heatmap](../easyclassify/index.html.md#easyclassify-compute-heatmap-property) | | Capacity to detect unknown defects^{[1](#unknown-defects)} | Distingish overlapping objects, counting^{[2](#counting)} | Counting^{[2](#counting)} | | Ground truth annotation | Image label | Per-pixel label | OK/NOK image label | Labeled bounding boxes | Labeled points | --- * **[1]** EasySegment Unsupervised trains only on images of a good product and identifies images of defective product by how much they deviate from images of good products. The advantage is that the tool can detect and segment defects that are not in your dataset and that you did not expect. The drawback is that the type of defects that the tool can detect is more limited compared to the other tools. * **[2]** EasyLocate works well with partially occluded objects or with objects overlapping each other. You can use the EasyLocate tools to do counting applications.