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Cifar 10 Mobilenet

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

TipoOtherVersiónRevision 0cd3d92Bcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
4,85 MB
Versión
vRevision 0cd3d92
Actualizado
10 oct 2026
Descargas
11
Fuente
Hugging Face

Descripción de la fuente

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

Fuentes

1 fuente
Hugging FacePrincipalVersión 0cd3d92598881132f401b897f46bb75a96bb0652
huggingface.co/aijadugar/cifar-10-mobilenet/tree/0cd3d92598881132f401b897f46bb75a96bb0652

Verificado 10 oct

Artefactos del modelo

1 artefacto

mobilenet-cifar10.safetensors

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

Download file

Extractos de fuentes

3 extractos

--- 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