Downloads · 30 days
0
alexnet-imagenette
lazy-toad/alexnet-imagenette
alexnet-imagenette is a machine learning model from lazy-toad. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
A from-scratch AlexNet implementation trained on the Imagenette 320px dataset (10 classes, ~9.5k training images).
Access
Public
Updated Aug 30, 2026
Repo size
144 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.pt144 MB · 100%
At a glance
- License
- mit
- Access
- Public
- Created
- Aug 30, 2026
- Updated
- Aug 30, 2026
- SHA
- efd384b8
Model card
From the Hugging Face model README
AlexNet — Imagenette (320px)
A from-scratch AlexNet implementation trained on the Imagenette 320px dataset (10 classes, ~9.5k training images).
Trained as a learning project to understand classic CNN architectures end-to-end — data loading, training loop, mixed precision, evaluation, and deployment.
Results
| Metric | Value |
|---|---|
| Test accuracy | 79.09% |
| Best validation accuracy | 79.53% |
| Training epochs | 30 |
| Optimizer | SGD (lr=0.01, momentum=0.9, weight_decay=5e-4) |
| LR schedule | StepLR (step_size=15, gamma=0.1) |
| Hardware | Kaggle T4 GPU |
| Mixed precision | FP16 (AMP) |
| Training time | ~25-30 min |
Model architecture
Standard AlexNet, modified for 320×320 input:
| Layer | Type | In → Out | Kernel / Stride / Padding |
|---|---|---|---|
| 1 | Conv2d | 3 → 64 | 11×11 / 4 / 2 |
| ReLU + MaxPool2d | 3×3 / 2 | ||
| 2 | Conv2d | 64 → 192 | 5×5 / 1 / 2 |
| ReLU + MaxPool2d | 3×3 / 2 | ||
| 3 | Conv2d | 192 → 384 | 3×3 / 1 / 1 |
| ReLU | |||
| 4 | Conv2d | 384 → 256 | 3×3 / 1 / 1 |
| ReLU | |||
| 5 | Conv2d | 256 → 256 | 3×3 / 1 / 1 |
| ReLU + MaxPool2d | 3×3 / 2 | ||
| Flatten | 256×5×5 → 6400 | ||
| 6 | Linear + Dropout(0.5) + ReLU | 6400 → 4096 | |
| 7 | Linear + Dropout(0.5) + ReLU | 4096 → 4096 | |
| 8 | Linear | 4096 → 10 |
Classes
tench, english springer, cassette player, chain saw, church, french horn, garbage truck, gas pump, golf ball, parachute
Usage
Load weights
from huggingface_hub import hf_hub_download
import torch
weights_path = hf_hub_download(
repo_id="lazy-toad/alexnet-imagenette",
filename="alexnet_imagenette.pt",
)
state_dict = torch.load(weights_path, map_location="cpu")
- License
- mit
- Languages
- en
- Created
- Aug 30, 2026
- Updated
- Aug 30, 2026