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303 lines
9.2 KiB
303 lines
9.2 KiB
# ECA-ResNet
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An **ECA ResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that utilises an [Efficient Channel Attention module](https://paperswithcode.com/method/efficient-channel-attention). Efficient Channel Attention is an architectural unit based on [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) that reduces model complexity without dimensionality reduction.
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## How do I use this model on an image?
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To load a pretrained model:
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```py
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>>> import timm
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>>> model = timm.create_model('ecaresnet101d', pretrained=True)
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>>> model.eval()
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```
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To load and preprocess the image:
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```py
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>>> import urllib
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>>> from PIL import Image
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>>> from timm.data import resolve_data_config
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>>> from timm.data.transforms_factory import create_transform
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>>> config = resolve_data_config({}, model=model)
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>>> transform = create_transform(**config)
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>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
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>>> urllib.request.urlretrieve(url, filename)
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>>> img = Image.open(filename).convert('RGB')
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>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
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```
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To get the model predictions:
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```py
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>>> import torch
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>>> with torch.no_grad():
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... out = model(tensor)
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>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
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>>> print(probabilities.shape)
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>>> # prints: torch.Size([1000])
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```
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To get the top-5 predictions class names:
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```py
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>>> # Get imagenet class mappings
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>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
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>>> urllib.request.urlretrieve(url, filename)
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>>> with open("imagenet_classes.txt", "r") as f:
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... categories = [s.strip() for s in f.readlines()]
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>>> # Print top categories per image
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>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
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>>> for i in range(top5_prob.size(0)):
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... print(categories[top5_catid[i]], top5_prob[i].item())
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>>> # prints class names and probabilities like:
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>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
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```
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Replace the model name with the variant you want to use, e.g. `ecaresnet101d`. You can find the IDs in the model summaries at the top of this page.
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To extract image features with this model, follow the [timm feature extraction examples](https://rwightman.github.io/pytorch-image-models/feature_extraction/), just change the name of the model you want to use.
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## How do I finetune this model?
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You can finetune any of the pre-trained models just by changing the classifier (the last layer).
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```py
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>>> model = timm.create_model('ecaresnet101d', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
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```
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To finetune on your own dataset, you have to write a training loop or adapt [timm's training
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script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
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## How do I train this model?
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You can follow the [timm recipe scripts](scripts) for training a new model afresh.
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## Citation
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```BibTeX
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@misc{wang2020ecanet,
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title={ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks},
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author={Qilong Wang and Banggu Wu and Pengfei Zhu and Peihua Li and Wangmeng Zuo and Qinghua Hu},
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year={2020},
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eprint={1910.03151},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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<!--
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Type: model-index
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Collections:
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- Name: ECAResNet
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Paper:
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Title: 'ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks'
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URL: https://paperswithcode.com/paper/eca-net-efficient-channel-attention-for-deep
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Models:
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- Name: ecaresnet101d
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In Collection: ECAResNet
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Metadata:
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FLOPs: 10377193728
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Parameters: 44570000
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File Size: 178815067
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Bottleneck Residual Block
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- Convolution
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- Efficient Channel Attention
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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- Squeeze-and-Excitation Block
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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Training Resources: 4x RTX 2080Ti GPUs
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ID: ecaresnet101d
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LR: 0.1
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Epochs: 100
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Layers: 101
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Crop Pct: '0.875'
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Batch Size: 256
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Image Size: '224'
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Weight Decay: 0.0001
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1087
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Weights: https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNet101D_281c5844.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 82.18%
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Top 5 Accuracy: 96.06%
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- Name: ecaresnet101d_pruned
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In Collection: ECAResNet
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Metadata:
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FLOPs: 4463972081
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Parameters: 24880000
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File Size: 99852736
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Bottleneck Residual Block
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- Convolution
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- Efficient Channel Attention
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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- Squeeze-and-Excitation Block
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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ID: ecaresnet101d_pruned
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Layers: 101
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Crop Pct: '0.875'
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Image Size: '224'
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1097
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Weights: https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45610/outputs/ECAResNet101D_P_75a3370e.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 80.82%
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Top 5 Accuracy: 95.64%
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- Name: ecaresnet50d
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In Collection: ECAResNet
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Metadata:
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FLOPs: 5591090432
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Parameters: 25580000
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File Size: 102579290
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Bottleneck Residual Block
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- Convolution
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- Efficient Channel Attention
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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- Squeeze-and-Excitation Block
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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Training Resources: 4x RTX 2080Ti GPUs
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ID: ecaresnet50d
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LR: 0.1
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Epochs: 100
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Layers: 50
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Crop Pct: '0.875'
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Batch Size: 256
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Image Size: '224'
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Weight Decay: 0.0001
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1045
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Weights: https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNet50D_833caf58.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 80.61%
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Top 5 Accuracy: 95.31%
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- Name: ecaresnet50d_pruned
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In Collection: ECAResNet
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Metadata:
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FLOPs: 3250730657
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Parameters: 19940000
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File Size: 79990436
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Bottleneck Residual Block
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- Convolution
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- Efficient Channel Attention
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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- Squeeze-and-Excitation Block
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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ID: ecaresnet50d_pruned
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Layers: 50
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Crop Pct: '0.875'
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Image Size: '224'
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1055
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Weights: https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45899/outputs/ECAResNet50D_P_9c67f710.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 79.71%
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Top 5 Accuracy: 94.88%
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- Name: ecaresnetlight
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In Collection: ECAResNet
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Metadata:
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FLOPs: 5276118784
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Parameters: 30160000
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File Size: 120956612
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Bottleneck Residual Block
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- Convolution
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- Efficient Channel Attention
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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- Squeeze-and-Excitation Block
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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ID: ecaresnetlight
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Crop Pct: '0.875'
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Image Size: '224'
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1077
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Weights: https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNetLight_4f34b35b.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 80.46%
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Top 5 Accuracy: 95.25%
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--> |