- Click here for the full tutorial - classification.ipynb
- Click here to download Jupyter Notebook
- The above Jupyter Notebook is a deeper dive into retail loss prevention solution classification project.
- To get hands on, Please go to Intel® DevCloud to start your hands on lab on Jupyter Notebook
- Note: You must have Intel Devcloud Account.
Intel® DevCloud is a sandbox for prototyping and experimenting with AI inference workloads on Intel® hardware specialized for deep learning.
Optimize your deep-learning models with the tools built into the Intel® Distribution of OpenVINO™ toolkit, and then test their performance on combinations of CPUs, GPUs, and VPUs (Vision Processing Units) such as the Intel® Neural Compute Stick 2 (NCS2).
Even if you're new to machine learning, Intel DevCloud has everything you need to quickly get started, including:
- Jupyter* Notebook-based tutorials and sample applications
- Trained models
- Sample data
- Executable code from the Intel Distribution of OpenVINO toolkit
- Tools for tuning and optimizing your models
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Learn the difference between classification, object-detection, and segmentation models, then test your newfound knowledge by modifying and classifying a user loaded image.
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You'll run a classification tutorial using a Jupyter* Notebook on Intel® DevCloud, then interpret the results to explain the resulting confidence levels.
- Between 1 to 2 hours.
- Intermediate knowledge of programming in Python*
- Experience with training and deploying deep learning models
- Familiarity with different DL layers and architectures (CNN based)
- Familiarity with the command line (bash terminal)
- Experience using OpenCV*