Eraly-ml/cifar10-cnn-ai-made

CIFAR-10 CNN — 🤖 Fully AI-Made

This model — the architecture, training code, evaluation, plots, and this very card — was created end-to-end by an AI agent (Claude, via the Hermes ML research loop). No human wrote the code. Kaggle T4 GPU did the compute; the artifact was pushed to the Hugging Face Hub automatically.

A compact 3-block VGG-style CNN trained from scratch on CIFAR-10.

Results

metric	value
Test accuracy	91.52%
Test loss	0.542
Best val accuracy	91.52%
Parameters	1.15M
Epochs	18
Train time (T4)	5.9 min

Training curves

Confusion matrix

Per-class accuracy

class	accuracy
automobile	96.0%
truck	95.2%
frog	94.9%
ship	94.6%
horse	93.7%
airplane	92.7%
deer	91.7%
dog	86.5%
bird	86.5%
cat	83.4%

Sample predictions

Architecture

3 conv blocks (64→128→256 ch, each = 2×[Conv3×3–BN–ReLU]+MaxPool) → GAP → Dropout(0.3) → Linear(10). AdamW + OneCycleLR, AMP, label smoothing 0.05, random crop + h-flip augmentation.

Usage

import torch, torch.nn as nn, torch.nn.functional as F
# (define the CNN class as in config.json), then:
sd = torch.load("cifar10_cnn.pt", map_location="cpu")
model.load_state_dict(sd); model.eval()

Provenance

• Dataset: uoft-cs/cifar10
• Compute: Kaggle Notebook, 1× NVIDIA Tesla T4
• Pipeline: Hermes ml-research-loop (Kaggle GPU → Hugging Face)
• Author: AI agent (Claude). Human involvement: pressed go.