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472 lines
13 KiB
472 lines
13 KiB
# Summary
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**RegNetY** is a convolutional network design space with simple, regular models with parameters: depth $d$, initial width $w\_{0} > 0$, and slope $w\_{a} > 0$, and generates a different block width $u\_{j}$ for each block $j < d$. The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure):
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$$ u\_{j} = w\_{0} + w\_{a}\cdot{j} $$
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For **RegNetX** authors have additional restrictions: we set $b = 1$ (the bottleneck ratio), $12 \leq d \leq 28$, and $w\_{m} \geq 2$ (the width multiplier).
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For **RegNetY** authors make one change, which is to include [Squeeze-and-Excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block).
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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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```python
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import timm
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model = timm.create_model('regnety_002', 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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```python
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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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```python
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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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```python
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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. `regnety_002`. 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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```python
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model = timm.create_model('regnety_002', pretrained=True).reset_classifier(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](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
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## Citation
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```BibTeX
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@misc{radosavovic2020designing,
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title={Designing Network Design Spaces},
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author={Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Dollár},
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year={2020},
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eprint={2003.13678},
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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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Models:
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- Name: regnety_002
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Metadata:
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FLOPs: 255754236
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Epochs: 100
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Batch Size: 1024
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Training Data:
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- ImageNet
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x NVIDIA V100 GPUs
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Dense Connections
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- Global Average Pooling
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- Grouped Convolution
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- ReLU
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- Squeeze-and-Excitation Block
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File Size: 12782926
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Tasks:
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- Image Classification
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ID: regnety_002
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 5.0e-05
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L409
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In Collection: RegNetY
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- Name: regnety_016
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Metadata:
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FLOPs: 2070895094
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Epochs: 100
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Batch Size: 1024
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Training Data:
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- ImageNet
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x NVIDIA V100 GPUs
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Dense Connections
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- Global Average Pooling
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- Grouped Convolution
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- ReLU
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- Squeeze-and-Excitation Block
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File Size: 45115589
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Tasks:
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- Image Classification
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ID: regnety_016
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 5.0e-05
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L433
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In Collection: RegNetY
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- Name: regnety_004
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Metadata:
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FLOPs: 515664568
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Epochs: 100
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Batch Size: 1024
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Training Data:
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- ImageNet
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x NVIDIA V100 GPUs
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Dense Connections
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- Global Average Pooling
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- Grouped Convolution
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- ReLU
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- Squeeze-and-Excitation Block
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File Size: 17542753
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Tasks:
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- Image Classification
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ID: regnety_004
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 5.0e-05
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L415
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In Collection: RegNetY
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- Name: regnety_006
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Metadata:
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FLOPs: 771746928
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Epochs: 100
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Batch Size: 1024
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Training Data:
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- ImageNet
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x NVIDIA V100 GPUs
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Dense Connections
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- Global Average Pooling
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- Grouped Convolution
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- ReLU
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- Squeeze-and-Excitation Block
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File Size: 24394127
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Tasks:
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- Image Classification
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ID: regnety_006
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 5.0e-05
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L421
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In Collection: RegNetY
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- Name: regnety_008
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Metadata:
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FLOPs: 1023448952
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Epochs: 100
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Batch Size: 1024
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Training Data:
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- ImageNet
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x NVIDIA V100 GPUs
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Dense Connections
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- Global Average Pooling
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- Grouped Convolution
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- ReLU
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- Squeeze-and-Excitation Block
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File Size: 25223268
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Tasks:
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- Image Classification
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ID: regnety_008
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 5.0e-05
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L427
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In Collection: RegNetY
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- Name: regnety_032
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Metadata:
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FLOPs: 4081118714
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Epochs: 100
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Batch Size: 512
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Training Data:
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- ImageNet
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x NVIDIA V100 GPUs
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Dense Connections
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- Global Average Pooling
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- Grouped Convolution
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- ReLU
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- Squeeze-and-Excitation Block
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File Size: 78084523
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Tasks:
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- Image Classification
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ID: regnety_032
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 5.0e-05
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L439
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In Collection: RegNetY
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- Name: regnety_080
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Metadata:
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FLOPs: 10233621420
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Epochs: 100
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Batch Size: 512
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Training Data:
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- ImageNet
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x NVIDIA V100 GPUs
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Dense Connections
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- Global Average Pooling
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- Grouped Convolution
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- ReLU
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- Squeeze-and-Excitation Block
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File Size: 157124671
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Tasks:
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- Image Classification
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ID: regnety_080
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 5.0e-05
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L457
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In Collection: RegNetY
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- Name: regnety_040
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Metadata:
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FLOPs: 5105933432
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Epochs: 100
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Batch Size: 512
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Training Data:
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- ImageNet
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x NVIDIA V100 GPUs
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Dense Connections
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- Global Average Pooling
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- Grouped Convolution
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- ReLU
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- Squeeze-and-Excitation Block
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File Size: 82913909
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Tasks:
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- Image Classification
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ID: regnety_040
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 5.0e-05
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L445
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In Collection: RegNetY
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- Name: regnety_064
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Metadata:
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FLOPs: 8167730444
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Epochs: 100
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Batch Size: 512
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Training Data:
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- ImageNet
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x NVIDIA V100 GPUs
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Dense Connections
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- Global Average Pooling
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- Grouped Convolution
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- ReLU
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- Squeeze-and-Excitation Block
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File Size: 122751416
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Tasks:
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- Image Classification
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ID: regnety_064
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 5.0e-05
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L451
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In Collection: RegNetY
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- Name: regnety_120
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Metadata:
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FLOPs: 15542094856
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Epochs: 100
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Batch Size: 512
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Training Data:
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- ImageNet
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x NVIDIA V100 GPUs
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Dense Connections
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- Global Average Pooling
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- Grouped Convolution
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- ReLU
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- Squeeze-and-Excitation Block
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File Size: 207743949
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Tasks:
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- Image Classification
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ID: regnety_120
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 5.0e-05
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L463
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In Collection: RegNetY
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- Name: regnety_160
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Metadata:
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FLOPs: 20450196852
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Epochs: 100
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Batch Size: 512
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Training Data:
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- ImageNet
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x NVIDIA V100 GPUs
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Dense Connections
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- Global Average Pooling
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- Grouped Convolution
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- ReLU
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- Squeeze-and-Excitation Block
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File Size: 334916722
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Tasks:
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- Image Classification
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ID: regnety_160
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 5.0e-05
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L469
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In Collection: RegNetY
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- Name: regnety_320
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Metadata:
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FLOPs: 41492618394
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Epochs: 100
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Batch Size: 256
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Training Data:
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- ImageNet
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x NVIDIA V100 GPUs
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Architecture:
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- 1x1 Convolution
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|
- Batch Normalization
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- Convolution
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|
- Dense Connections
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- Global Average Pooling
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- Grouped Convolution
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- ReLU
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- Squeeze-and-Excitation Block
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File Size: 580891965
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Tasks:
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- Image Classification
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ID: regnety_320
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 5.0e-05
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L475
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In Collection: RegNetY
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Collections:
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- Name: RegNetY
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Paper:
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title: Designing Network Design Spaces
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url: https://paperswithcode.com//paper/designing-network-design-spaces
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type: model-index
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Type: model-index
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--> |