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- Collections:
- - Name: MixMIM
- Metadata:
- Architecture:
- - Attention Dropout
- - Convolution
- - Dense Connections
- - Dropout
- - GELU
- - Layer Normalization
- - Multi-Head Attention
- - Scaled Dot-Product Attention
- - Tanh Activation
- Paper:
- Title: 'MixMIM: Mixed and Masked Image Modeling for Efficient Visual Representation
- Learning'
- URL: https://arxiv.org/abs/2205.13137
- README: configs/mixmim/README.md
- Code:
- URL: https://github.com/open-mmlab/mmpretrain/blob/main/mmpretrain/models/backbones/mixmim.py
- Version: v1.0.0rc4
- Models:
- - Name: mixmim_mixmim-base_16xb128-coslr-300e_in1k
- Metadata:
- Epochs: 300
- Batch Size: 2048
- FLOPs: 16351906816
- Parameters: 114665784
- Training Data: ImageNet-1k
- In Collection: MixMIM
- Results: null
- Weights: https://download.openmmlab.com/mmselfsup/1.x/mixmim/mixmim-base-p16_16xb128-coslr-300e_in1k/mixmim-base-p16_16xb128-coslr-300e_in1k_20221208-44fe8d2c.pth
- Config: configs/mixmim/mixmim_mixmim-base_16xb128-coslr-300e_in1k.py
- Downstream:
- - mixmim-base_mixmim-pre_8xb128-coslr-100e_in1k
- - Name: mixmim-base_mixmim-pre_8xb128-coslr-100e_in1k
- Metadata:
- Epochs: 100
- Batch Size: 1024
- FLOPs: 16351906816
- Parameters: 88344352
- Training Data: ImageNet-1k
- In Collection: MixMIM
- Results:
- - Task: Image Classification
- Dataset: ImageNet-1k
- Metrics:
- Top 1 Accuracy: 84.63
- Weights: https://download.openmmlab.com/mmselfsup/1.x/mixmim/mixmim-base-p16_16xb128-coslr-300e_in1k/mixmim-base-p16_ft-8xb128-coslr-100e_in1k/mixmim-base-p16_ft-8xb128-coslr-100e_in1k_20221208-41ecada9.pth
- Config: configs/mixmim/benchmarks/mixmim-base_8xb128-coslr-100e_in1k.py
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