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Cifar 10 Mobilenet

A PyTorch implementation of MobileNetV2 trained from scratch on the CIFAR-10 dataset.

ArtOtherVersionRevision 0cd3d92BBewertung

Modellquelle

Art
Other
Dateigröße
4,85 MB
Version
vRevision 0cd3d92
Aktualisiert
10. Okt. 2026
Downloads
11
Quelle
Hugging Face

Quellenbeschreibung

MobileNetV2 on CIFAR-10

A PyTorch implementation of MobileNetV2 trained from scratch on the CIFAR-10 dataset.

Model Details

  • Architecture: MobileNetV2
  • Framework: PyTorch
  • Dataset: CIFAR-10
  • Input Size: 3 × 32 × 32
  • Classes: 10

CIFAR-10 Classes

LabelClass
0airplane
1automobile
2bird
3cat
4deer
5dog
6frog
7horse
8ship
9truck

Training

  • Optimizer: SGD
  • Learning Rate: 0.1
  • Momentum: 0.9
  • Weight Decay: 5e-4
  • Scheduler: StepLR
  • Loss: CrossEntropyLoss
  • Epochs: 30
  • Batch Size: 128

Performance

MetricValue
Test Accuracy84.62%

Model Files

  • mobilenetv2_cifar10.pth

Load Model

model = MobileNetV2()

model.load_state_dict(
    torch.load("mobilenetv2_cifar10.pth")
)

model.eval()

Inference

with torch.no_grad():
    outputs = model(images)
    _, predicted = torch.max(outputs, 1)

Author

Ankit Bari

Quellen

1 Quelle
Hugging FacePrimärVersion 0cd3d92598881132f401b897f46bb75a96bb0652
huggingface.co/aijadugar/cifar-10-mobilenet/tree/0cd3d92598881132f401b897f46bb75a96bb0652

Verifiziert 10. Okt.

Modellartefakte

1 Artefakt

mobilenet-cifar10.safetensors

safetensors · 4,85 MB · SHA-256 25eee7cab53d…1464 · Hugging Face

Download file

Quellenauszüge

3 Auszüge

--- license: mit language: - en library_name: pytorch pipeline_tag: image-classification tags: - pytorch - mobilenetv2 - cnn - cifar10 - image-classification - computer-vision datasets: - cifar10 --- # MobileNetV2 on CIFAR-10 A PyTorch implementation of MobileNetV2 trained from scratch on the CIFAR-10 dataset. ## Model Details - Architecture: MobileNetV2 - Framework: PyTorch - Dataset: CIFAR-10 - Input Size: 3 × 32 × 32 - Classes: 10 ## CIFAR-10 Classes | Label | Class | |------:|--------| | 0 | airplane | | 1 | automobile | | 2 | bird | | 3 | cat | | 4 | deer | | 5 | dog | | 6 | frog | | 7 | horse | | 8 | ship | | 9 | truck | ## Training - Optimizer: SGD - Learning Rate: 0.1 - Momentum: 0.9 - Weight Decay: 5e-4 - Scheduler: StepLR - Loss: CrossEntropyLoss - Epochs: 30 - Batch Size: 128 ## Performance | Metric | Value | |--------|------:| | Test Accuracy | 84.62% | ## Model Files - `mobilenetv2_cifar10.pth` ## Load Model ```python model = MobileNetV2() model.load_state_dict( torch.load("mobilenetv2_cifar10.pth") ) model.eval() ``` ## Inference ```python with torch.no_grad(): outputs = model(images) _, predicted = torch.max(outputs, 1) ``` ## Author Ankit Bari - GitHub: https://github.com/aijadugar - Hugging Face: https://huggingface.co/aijadugar

Source context: 11 downloads · 1 likes · Pipeline image-classification · Library pytorch · Repo aijadugar/cifar-10-mobilenet

Source context: 14013 downloads · 1 likes · Pipeline image-classification · Library pytorch · Repo aijadugar/cifar-10-mobilenet