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| Collections: | |
| - Name: ResNet | |
| Metadata: | |
| Training Data: ImageNet-1k | |
| Training Techniques: | |
| - SGD with Momentum | |
| - Weight Decay | |
| Training Resources: 8x V100 GPUs | |
| Epochs: 100 | |
| Batch Size: 256 | |
| Architecture: | |
| - ResNet | |
| Paper: | |
| URL: https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html | |
| Title: "Deep Residual Learning for Image Recognition" | |
| README: configs/resnet/README.md | |
| Code: | |
| URL: https://github.com/open-mmlab/mmclassification/blob/v0.15.0/mmcls/models/backbones/resnet.py#L383 | |
| Version: v0.15.0 | |
| Models: | |
| - Name: resnet18_8xb16_cifar10 | |
| Metadata: | |
| Training Data: CIFAR-10 | |
| Epochs: 200 | |
| Batch Size: 128 | |
| FLOPs: 560000000 | |
| Parameters: 11170000 | |
| In Collection: ResNet | |
| Results: | |
| - Dataset: CIFAR-10 | |
| Metrics: | |
| Top 1 Accuracy: 94.82 | |
| Task: Image Classification | |
| Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20210528-bd6371c8.pth | |
| Config: configs/resnet/resnet18_8xb16_cifar10.py | |
| - Name: resnet34_8xb16_cifar10 | |
| Metadata: | |
| Training Data: CIFAR-10 | |
| Epochs: 200 | |
| Batch Size: 128 | |
| FLOPs: 1160000000 | |
| Parameters: 21280000 | |
| In Collection: ResNet | |
| Results: | |
| - Dataset: CIFAR-10 | |
| Metrics: | |
| Top 1 Accuracy: 95.34 | |
| Task: Image Classification | |
| Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20210528-a8aa36a6.pth | |
| Config: configs/resnet/resnet34_8xb16_cifar10.py | |
| - Name: resnet50_8xb16_cifar10 | |
| Metadata: | |
| Training Data: CIFAR-10 | |
| Epochs: 200 | |
| Batch Size: 128 | |
| FLOPs: 1310000000 | |
| Parameters: 23520000 | |
| In Collection: ResNet | |
| Results: | |
| - Dataset: CIFAR-10 | |
| Metrics: | |
| Top 1 Accuracy: 95.55 | |
| Task: Image Classification | |
| Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.pth | |
| Config: configs/resnet/resnet50_8xb16_cifar10.py | |
| - Name: resnet101_8xb16_cifar10 | |
| Metadata: | |
| Training Data: CIFAR-10 | |
| Epochs: 200 | |
| Batch Size: 128 | |
| FLOPs: 2520000000 | |
| Parameters: 42510000 | |
| In Collection: ResNet | |
| Results: | |
| - Dataset: CIFAR-10 | |
| Metrics: | |
| Top 1 Accuracy: 95.58 | |
| Task: Image Classification | |
| Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20210528-2d29e936.pth | |
| Config: configs/resnet/resnet101_8xb16_cifar10.py | |
| - Name: resnet152_8xb16_cifar10 | |
| Metadata: | |
| Training Data: CIFAR-10 | |
| Epochs: 200 | |
| Batch Size: 128 | |
| FLOPs: 3740000000 | |
| Parameters: 58160000 | |
| In Collection: ResNet | |
| Results: | |
| - Dataset: CIFAR-10 | |
| Metrics: | |
| Top 1 Accuracy: 95.76 | |
| Task: Image Classification | |
| Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20210528-3e8e9178.pth | |
| Config: configs/resnet/resnet152_8xb16_cifar10.py | |
| - Name: resnet50_8xb16_cifar100 | |
| Metadata: | |
| Training Data: CIFAR-100 | |
| Epochs: 200 | |
| Batch Size: 128 | |
| FLOPs: 1310000000 | |
| Parameters: 23710000 | |
| In Collection: ResNet | |
| Results: | |
| - Dataset: CIFAR-100 | |
| Metrics: | |
| Top 1 Accuracy: 79.90 | |
| Top 5 Accuracy: 95.19 | |
| Task: Image Classification | |
| Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar100_20210528-67b58a1b.pth | |
| Config: configs/resnet/resnet50_8xb16_cifar100.py | |
| - Name: resnet18_8xb32_in1k | |
| Metadata: | |
| FLOPs: 1820000000 | |
| Parameters: 11690000 | |
| In Collection: ResNet | |
| Results: | |
| - Dataset: ImageNet-1k | |
| Metrics: | |
| Top 1 Accuracy: 69.90 | |
| Top 5 Accuracy: 89.43 | |
| Task: Image Classification | |
| Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth | |
| Config: configs/resnet/resnet18_8xb32_in1k.py | |
| - Name: resnet34_8xb32_in1k | |
| Metadata: | |
| FLOPs: 3680000000 | |
| Parameters: 2180000 | |
| In Collection: ResNet | |
| Results: | |
| - Dataset: ImageNet-1k | |
| Metrics: | |
| Top 1 Accuracy: 73.62 | |
| Top 5 Accuracy: 91.59 | |
| Task: Image Classification | |
| Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_8xb32_in1k_20210831-f257d4e6.pth | |
| Config: configs/resnet/resnet34_8xb32_in1k.py | |
| - Name: resnet50_8xb32_in1k | |
| Metadata: | |
| FLOPs: 4120000000 | |
| Parameters: 25560000 | |
| In Collection: ResNet | |
| Results: | |
| - Dataset: ImageNet-1k | |
| Metrics: | |
| Top 1 Accuracy: 76.55 | |
| Top 5 Accuracy: 93.06 | |
| Task: Image Classification | |
| Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth | |
| Config: configs/resnet/resnet50_8xb32_in1k.py | |
| - Name: resnet101_8xb32_in1k | |
| Metadata: | |
| FLOPs: 7850000000 | |
| Parameters: 44550000 | |
| In Collection: ResNet | |
| Results: | |
| - Dataset: ImageNet-1k | |
| Metrics: | |
| Top 1 Accuracy: 77.97 | |
| Top 5 Accuracy: 94.06 | |
| Task: Image Classification | |
| Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.pth | |
| Config: configs/resnet/resnet101_8xb32_in1k.py | |
| - Name: resnet152_8xb32_in1k | |
| Metadata: | |
| FLOPs: 11580000000 | |
| Parameters: 60190000 | |
| In Collection: ResNet | |
| Results: | |
| - Dataset: ImageNet-1k | |
| Metrics: | |
| Top 1 Accuracy: 78.48 | |
| Top 5 Accuracy: 94.13 | |
| Task: Image Classification | |
| Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_8xb32_in1k_20210901-4d7582fa.pth | |
| Config: configs/resnet/resnet152_8xb32_in1k.py | |
| - Name: resnetv1d50_8xb32_in1k | |
| Metadata: | |
| FLOPs: 4360000000 | |
| Parameters: 25580000 | |
| In Collection: ResNet | |
| Results: | |
| - Dataset: ImageNet-1k | |
| Metrics: | |
| Top 1 Accuracy: 77.54 | |
| Top 5 Accuracy: 93.57 | |
| Task: Image Classification | |
| Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.pth | |
| Config: configs/resnet/resnetv1d50_8xb32_in1k.py | |
| - Name: resnetv1d101_8xb32_in1k | |
| Metadata: | |
| FLOPs: 8090000000 | |
| Parameters: 44570000 | |
| In Collection: ResNet | |
| Results: | |
| - Dataset: ImageNet-1k | |
| Metrics: | |
| Top 1 Accuracy: 78.93 | |
| Top 5 Accuracy: 94.48 | |
| Task: Image Classification | |
| Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.pth | |
| Config: configs/resnet/resnetv1d101_8xb32_in1k.py | |
| - Name: resnetv1d152_8xb32_in1k | |
| Metadata: | |
| FLOPs: 11820000000 | |
| Parameters: 60210000 | |
| In Collection: ResNet | |
| Results: | |
| - Dataset: ImageNet-1k | |
| Metrics: | |
| Top 1 Accuracy: 79.41 | |
| Top 5 Accuracy: 94.70 | |
| Task: Image Classification | |
| Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.pth | |
| Config: configs/resnet/resnetv1d152_8xb32_in1k.py | |
| - Name: resnet50_8xb32-fp16_in1k | |
| Metadata: | |
| FLOPs: 4120000000 | |
| Parameters: 25560000 | |
| Training Techniques: | |
| - SGD with Momentum | |
| - Weight Decay | |
| - Mixed Precision Training | |
| In Collection: ResNet | |
| Results: | |
| - Task: Image Classification | |
| Dataset: ImageNet-1k | |
| Metrics: | |
| Top 1 Accuracy: 76.30 | |
| Top 5 Accuracy: 93.07 | |
| Weights: https://download.openmmlab.com/mmclassification/v0/fp16/resnet50_batch256_fp16_imagenet_20210320-b3964210.pth | |
| Config: configs/resnet/resnet50_8xb32-fp16_in1k.py | |
| - Name: resnet50_8xb256-rsb-a1-600e_in1k | |
| Metadata: | |
| FLOPs: 4120000000 | |
| Parameters: 25560000 | |
| Training Techniques: | |
| - LAMB | |
| - Weight Decay | |
| - Cosine Annealing | |
| - Mixup | |
| - CutMix | |
| - RepeatAugSampler | |
| - RandAugment | |
| Epochs: 600 | |
| Batch Size: 2048 | |
| In Collection: ResNet | |
| Results: | |
| - Task: Image Classification | |
| Dataset: ImageNet-1k | |
| Metrics: | |
| Top 1 Accuracy: 80.12 | |
| Top 5 Accuracy: 94.78 | |
| Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth | |
| Config: configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py | |
| - Name: resnet50_8xb256-rsb-a2-300e_in1k | |
| Metadata: | |
| FLOPs: 4120000000 | |
| Parameters: 25560000 | |
| Training Techniques: | |
| - LAMB | |
| - Weight Decay | |
| - Cosine Annealing | |
| - Mixup | |
| - CutMix | |
| - RepeatAugSampler | |
| - RandAugment | |
| Epochs: 300 | |
| Batch Size: 2048 | |
| In Collection: ResNet | |
| Results: | |
| - Task: Image Classification | |
| Dataset: ImageNet-1k | |
| Metrics: | |
| Top 1 Accuracy: 79.55 | |
| Top 5 Accuracy: 94.37 | |
| Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a2-300e_in1k_20211228-0fd8be6e.pth | |
| Config: configs/resnet/resnet50_8xb256-rsb-a2-300e_in1k.py | |
| - Name: resnet50_8xb256-rsb-a3-100e_in1k | |
| Metadata: | |
| FLOPs: 4120000000 | |
| Parameters: 25560000 | |
| Training Techniques: | |
| - LAMB | |
| - Weight Decay | |
| - Cosine Annealing | |
| - Mixup | |
| - CutMix | |
| - RandAugment | |
| Batch Size: 2048 | |
| In Collection: ResNet | |
| Results: | |
| - Task: Image Classification | |
| Dataset: ImageNet-1k | |
| Metrics: | |
| Top 1 Accuracy: 78.30 | |
| Top 5 Accuracy: 93.80 | |
| Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a3-100e_in1k_20211228-3493673c.pth | |
| Config: configs/resnet/resnet50_8xb256-rsb-a3-100e_in1k.py | |
| - Name: resnetv1c50_8xb32_in1k | |
| Metadata: | |
| FLOPs: 4360000000 | |
| Parameters: 25580000 | |
| In Collection: ResNet | |
| Results: | |
| - Dataset: ImageNet-1k | |
| Metrics: | |
| Top 1 Accuracy: 77.01 | |
| Top 5 Accuracy: 93.58 | |
| Task: Image Classification | |
| Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c50_8xb32_in1k_20220214-3343eccd.pth | |
| Config: configs/resnet/resnetv1c50_8xb32_in1k.py | |
| - Name: resnetv1c101_8xb32_in1k | |
| Metadata: | |
| FLOPs: 8090000000 | |
| Parameters: 44570000 | |
| In Collection: ResNet | |
| Results: | |
| - Dataset: ImageNet-1k | |
| Metrics: | |
| Top 1 Accuracy: 78.30 | |
| Top 5 Accuracy: 94.27 | |
| Task: Image Classification | |
| Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c101_8xb32_in1k_20220214-434fe45f.pth | |
| Config: configs/resnet/resnetv1c101_8xb32_in1k.py | |
| - Name: resnetv1c152_8xb32_in1k | |
| Metadata: | |
| FLOPs: 11820000000 | |
| Parameters: 60210000 | |
| In Collection: ResNet | |
| Results: | |
| - Dataset: ImageNet-1k | |
| Metrics: | |
| Top 1 Accuracy: 78.76 | |
| Top 5 Accuracy: 94.41 | |
| Task: Image Classification | |
| Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c152_8xb32_in1k_20220214-c013291f.pth | |
| Config: configs/resnet/resnetv1c152_8xb32_in1k.py | |
| - Name: resnet50_8xb8_cub | |
| Metadata: | |
| FLOPs: 16480000000 | |
| Parameters: 23920000 | |
| In Collection: ResNet | |
| Results: | |
| - Dataset: CUB-200-2011 | |
| Metrics: | |
| Top 1 Accuracy: 88.45 | |
| Task: Image Classification | |
| Pretrain: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth | |
| Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cub_20220307-57840e60.pth | |
| Config: configs/resnet/resnet50_8xb8_cub.py | |