The Awesome Models repository is a collection of links to download PyTorch , Keras, and Tensorflow pre-trained models. If you find any link is broken, please raise an issue to have it fixed as soon as possible.
Framework | Model_link |
---|---|
AlexNet | model link |
ConvNeXt_Tiny | model link |
ConvNeXt_Small | model link |
ConvNeXt_Base | model link |
ConvNeXt_Large | model link |
DenseNet_121 | model link |
DenseNet_161 | model link |
DenseNet_169 | model link |
DenseNet_201 | model link |
EfficientNet_B0 | model link |
EfficientNet_B1 | model link |
EfficientNet_B2 | model link |
EfficientNet_B3 | model link |
EfficientNet_B4 | model link |
EfficientNet_B5 | model link |
EfficientNet_B6 | model link |
EfficientNet_B7 | model link |
EfficientNet_V2_S | model link |
EfficientNet_V2_M | model link |
EfficientNet_V2_L | model link |
GoogLeNet | model link |
Inception V3 | model link |
MaxVit | model link |
MNASNet0_5 | model link |
MNASNet0_75 | model link |
MNASNet1_0 | model link |
MNASNet1_3 | model link |
MobileNet V2 | model link |
MobileNet V3_Small | model link |
MobileNet V3_Large | model link |
RegNet_Y_400mf | model link |
RegNet_Y_800mf | model link |
RegNet_Y_1_6gf | model link |
RegNet_Y_3_2gf | model link |
RegNet_Y_8gf | model link |
RegNet_Y_16gf | model link |
RegNet_Y_32gf | model link |
RegNet_Y_128gf | model link |
RegNet_X_400mf | model link |
RegNet_X_800mf | model link |
RegNet_X_1_6gf | model link |
RegNet_X_3_2gf | model link |
RegNet_X_8gf | model link |
RegNet_X_16gf | model link |
RegNet_X_32gf | model link |
ResNet_18 | model link |
ResNet_34 | model link |
ResNet_50 | model link |
ResNet_101 | model link |
ResNet_152 | model link |
ResNeXt50_32x4d | model link |
ResNeXt101_32x8d | model link |
ResNeXt101_64x4d | model link |
ShuffleNetV2_x0_5 | model link |
ShuffleNetV2_x1_0 | model link |
ShuffleNetV2_x1_5 | model link |
ShuffleNetV2_x2_0 | model link |
SqueezeNet1_0 | model link |
SqueezeNet1_1 | model link |
SwinTransformer_t | model link |
SwinTransformer_s | model link |
SwinTransformer_b | model link |
SwinTransformer_v2_t | model link |
SwinTransformer_v2_s | model link |
SwinTransformer_v2_b | model link |
VGG11 | model link |
VGG11_bn | model link |
VGG13 | model link |
VGG13_bn | model link |
VGG16 | model link |
VGG16_bn | model link |
VGG19 | model link |
VGG19_bn | model link |
VisionTransformer_b_16 | model link |
VisionTransformer_b_32 | model link |
VisionTransformer_l_16 | model link |
VisionTransformer_l_32 | model link |
VisionTransformer_h_14 | model link |
Wide ResNet50_2 | model link |
Wide ResNet101_2 | model link |
Framework | Model_link |
---|---|
Faster R-CNN_resnet50_fpn | model link |
Faster R-CNN_resnet50_fpn_V2 | model link |
Faster R-CNN_mobilenet_v3_large_fpn | model link |
Faster R-CNN_mobilenet_v3_large_320_fpn | model link |
FCOS_resnet50_fpn | model link |
RetinaNet_resnet50_fpn | model link |
RetinaNet_resnet50_fpn_v2 | model link |
SSD300_vgg16 | model link |
SSDlite320_mobilenet_v3_large | model link |
Framework | Model_link |
---|---|
Xception | model link |
VGG16 | model link |
VGG19 | model link |
ResNet50 | model link |
ResNet50V2 | model link |
ResNet101 | model link |
ResNet101V2 | model link |
ResNet152 | model link |
ResNet152V2 | model link |
InceptionV3 | model link |
InceptionResNetV2 | model link |
MobileNet | model link |
MobileNetV2 | model link |
DenseNet121 | model link |
DenseNet169 | model link |
DenseNet201 | model link |
NASNetMobile | model link |
NASNetLarge | model link |
EfficientNetB0 | model link |
EfficientNetB1 | model link |
EfficientNetB2 | model link |
EfficientNetB3 | model link |
EfficientNetB4 | model link |
EfficientNetB5 | model link |
EfficientNetB6 | model link |
EfficientNetB7 | model link |
EfficientNetV2B0 | model link |
EfficientNetV2B1 | model link |
EfficientNetV2B2 | model link |
EfficientNetV2B3 | model link |
EfficientNetV2S | model link |
EfficientNetV2M | model link |
EfficientNetV2L | model link |
ConvNeXtTiny | model link |
ConvNeXtSmall | model link |
ConvNeXtBase | model link |
ConvNeXtLarge | model link |
ConvNeXtXLarge | model link |
Mask R-CNN | model link |
AlexNet | model link |
Framework | Model_link |
---|---|
Efficientnetv2-S | model link |
Efficientnetv2-M | model link |
Efficientnetv2-L | model link |
Efficientnetv2-S-21K | model link |
Efficientnetv2-M-21K | model link |
Efficientnetv2-L-21K | model link |
Efficientnetv2-Xl-21K | model link |
Efficientnetv2-B0-21K | model link |
Efficientnetv2-B1-21K | model link |
Efficientnetv2-B2-21K | model link |
Efficientnetv2-B3-21K | model link |
Efficientnetv2-S-21K-ft1K | model link |
Efficientnetv2-M-21K-ft1K | model link |
Efficientnetv2-L-21K-ft1K | model link |
Efficientnetv2-Xl-21K-ft1K | model link |
Efficientnetv2-B0-21K-ft1K | model link |
Efficientnetv2-B1-21K-ft1K | model link |
Efficientnetv2-B2-21K-ft1K | model link |
Efficientnetv2-B3-21K-ft1K | model link |
Efficientnetv2-B0 | model link |
Efficientnetv2-B1 | model link |
Efficientnetv2-B2 | model link |
Efficientnetv2-B3 | model link |
Efficientnet_B0 | model link |
Efficientnet_B1 | model link |
Efficientnet_B2 | model link |
Efficientnet_B3 | model link |
Efficientnet_B4 | model link |
Efficientnet_B5 | model link |
Efficientnet_B6 | model link |
Efficientnet_B7 | model link |
Bit_S-R50X1 | model link |
Inception_V3 | model link |
Inception_Resnet_V2 | model link |
Resnet_V1_50 | model link |
Resnet_V1_101 | model link |
Resnet_V1_152 | model link |
Resnet_V2_50 | model link |
Resnet_V2_101 | model link |
Resnet_V2_152 | model link |
Nasnet_Large | model link |
Nasnet_Mobile | model link |
Pnasnet_Large | model link |
Mobilenet_V2_100_224 | model link |
Mobilenet_V2_130_224 | model link |
Mobilenet_V2_140_224 | model link |
Mobilenet_V3_Small_100_224 | model link |
Mobilenet_V3_Small_075_224 | model link |
Mobilenet_V3_Large_100_224 | model link |
Mobilenet_V3_Large_075_224 | model link |
Computer Vision moves fast! Sometimes our model links are invalid. If you notice that any of the model link is not working properly, create a bug report and let us know.
If you have an idea for a new model we should do, create a feature request. We are constantly looking for new models.
We are here for you, so don't hesitate to reach out