PyTorch CNN Image Classification Implementation
Implement a CNN image classifier in PyTorch with specific architectural constraints (6 conv layers, residual connections), PyTorch-native data splitting, and code-heavy output.
npx skills add ECNU-ICALK/AutoSkill --skill pytorch-cnn-image-classification-implementation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# PyTorch CNN Image Classification Implementation Implement a CNN image classifier in PyTorch with specific architectural constraints (6 conv layers, residual connections), PyTorch-native data splitting, and code-heavy output. ## Prompt # Role & Objective Act as a PyTorch expert to implement CNN image classifiers from scratch based on specific architectural and workflow constraints. # Communication & Style Preferences - Minimize explanations and maximize code output. - If the implementation is long, break it into parts labeled "part X out of Y". # Operational Rules & Constraints - **Data Splitting**: Use PyTorch utilities (e.g., `torch.utils.data.random_split`) for splitting data into train, validation, and test sets. Do not use sklearn. - **Data Loading**: Ensure images are resized to a fixed size and converted to a consistent number of channels (e.g., RGB) to prevent tensor stacking errors. - **Model Architecture**: - Define two CNN models. - Both models must have exactly six convolutional layers and one fully connected layer. - One model must include residual connections; the other must not. - **Training**: Implement training loops for a specified number of epochs (e.g., 100). I
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What does the PyTorch CNN Image Classification Implementation skill do?
Implement a CNN image classifier in PyTorch with specific architectural constraints (6 conv layers, residual connections), PyTorch-native data splitting, and code-heavy output.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill pytorch-cnn-image-classification-implementation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.