Available pretrained models
May 15, 2018 ยท View on GitHub
Classification accuracy of available pretrained models. (Datasets are ImageNet1K, ImageNet11K and Place365 Challenge)
Please note that the following results are calculated with different datasets.
- ResNet accuracy from Reproduce ResNet-v2 using MXNet
- DenseNet-169 accuracy from A MXNet implementation of DenseNet with BC structure
- SE-ResNeXt-50 accuracy from SENet.mxnet
- Other accuracy from MXNet model gallery and MXNet - Image Classification - Pre-trained Models
| model | Top-1 Accuracy | Top-5 Accuracy | download size | model size (MXNet) | dataset | image shape |
|---|---|---|---|---|---|---|
| CaffeNet | 54.5% | 78.3% | 233MB | 9.3MB | ImageNet1K | 227x227 |
| SqueezeNet | 55.4% | 78.8% | 4.8MB | 4.8MB | ImageNet1K | 227x227 |
| NIN | 58.8% | 81.3% | 30MB | 30MB | ImageNet1K | 224x224 |
| ResNet-18 | 69.5% | 89.1% | 45MB | 43MB | ImageNet1K | 224x224 |
| VGG16 | 71.0% | 89.8% | 528MB | 58MB | ImageNet1K | 224x224 |
| VGG19 | 71.0% | 89.8% | 549MB | 78MB | ImageNet1K | 224x224 |
| Inception-BN | 72.5% | 90.8% | 44MB | 40MB | ImageNet1K | 224x224 |
| ResNet-34 | 72.8% | 91.1% | 84MB | 82MB | ImageNet1K | 224x224 |
| ResNet-50 | 75.6% | 92.8% | 98MB | 90MB | ImageNet1K | 224x224 |
| ResNet-101 | 77.3% | 93.4% | 171MB | 163MB | ImageNet1K | 224x224 |
| ResNet-152 | 77.8% | 93.6% | 231MB | 223MB | ImageNet1K | 224x224 |
| ResNet-200 | 77.9% | 93.8% | 248MB | 240MB | ImageNet1K | 224x224 |
| Inception-v3 | 76.9% | 93.3% | 92MB | 84MB | ImageNet1K | 299x299 |
| ResNeXt-50 | 76.9% | 93.3% | 96MB | 89MB | ImageNet1K | 224x224 |
| ResNeXt-101 | 78.3% | 94.1% | 170MB | 162MB | ImageNet1K | 224x224 |
| ResNeXt-101-64x4d | 79.1% | 94.3% | 320MB | 312MB | ImageNet1K | 224x224 |
| ResNet-152 (imagenet11k) | 41.6% | - | 311MB | 223MB | ImageNet11K | 224x224 |
| ResNet-50 (Place365 Challenge) | 31.1% | - | 181MB | 90MB | Place365ch | 224x224 |
| ResNet-152 (Place365 Challenge) | 33.6% | - | 313MB | 223MB | Place365ch | 224x224 |
| DenseNet-169 | 75.3% | 92.8% | 55MB | 48MB | ImageNet1K | 224x224 |
| SE-ResNeXt-50 | 76.7% | 93.4% | 103MB | 95MB | ImageNet1K | 224x224 |
- The
download sizeis the file size when first downloading pretrained model. - The
model sizeis the file size to be saved after fine-tuning.
To use these pretrained models, specify the following pretrained model name in config.yml.
| model | pretrained model name |
|---|---|
| CaffeNet | imagenet1k-caffenet |
| SqueezeNet | imagenet1k-squeezenet |
| NIN | imagenet1k-nin |
| VGG16 | imagenet1k-vgg16 |
| VGG19 | imagenet1k-vgg19 |
| Inception-BN | imagenet1k-inception-bn |
| ResNet-18 | imagenet1k-resnet-18 |
| ResNet-34 | imagenet1k-resnet-34 |
| ResNet-50 | imagenet1k-resnet-50 |
| ResNet-101 | imagenet1k-resnet-101 |
| ResNet-152 | imagenet1k-resnet-152 |
| ResNet-152 (imagenet11k) | imagenet11k-resnet-152 |
| ResNet-200 | imagenet1k-resnet-200 |
| Inception-v3 | imagenet1k-inception-v3 |
| ResNeXt-50 | imagenet1k-resnext-50 |
| ResNeXt-101 | imagenet1k-resnext-101 |
| ResNeXt-101-64x4d | imagenet1k-resnext-101-64x4d |
| ResNet-50 (Place365 Challenge) | imagenet11k-place365ch-resnet-50 |
| ResNet-152 (Place365 Challenge) | imagenet11k-place365ch-resnet-152 |
| DenseNet-169 | imagenet1k-densenet-169 |
| SE-ResNeXt-50 | imagenet1k-se-resnext-50 |