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# TResNet |
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A **TResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that aim to boost accuracy while maintaining GPU training and inference efficiency. They contain several design tricks including a SpaceToDepth stem, [Anti-Alias downsampling](https://paperswithcode.com/method/anti-alias-downsampling), In-Place Activated BatchNorm, Blocks selection and [squeeze-and-excitation layers](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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```py |
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>>> import timm |
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>>> model = timm.create_model('tresnet_l', 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) |
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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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>>> |
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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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>>> |
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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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>>> |
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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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>>> |
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>>> |
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``` |
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Replace the model name with the variant you want to use, e.g. `tresnet_l`. 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](../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('tresnet_l', 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](../training_script) for training a new model afresh. |
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## Citation |
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```BibTeX |
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@misc{ridnik2020tresnet, |
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title={TResNet: High Performance GPU-Dedicated Architecture}, |
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author={Tal Ridnik and Hussam Lawen and Asaf Noy and Emanuel Ben Baruch and Gilad Sharir and Itamar Friedman}, |
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year={2020}, |
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eprint={2003.13630}, |
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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: TResNet |
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Paper: |
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Title: 'TResNet: High Performance GPU-Dedicated Architecture' |
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URL: https://paperswithcode.com/paper/tresnet-high-performance-gpu-dedicated |
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Models: |
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- Name: tresnet_l |
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In Collection: TResNet |
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Metadata: |
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FLOPs: 10873416792 |
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Parameters: 53456696 |
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File Size: 224440219 |
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Architecture: |
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- 1x1 Convolution |
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- Anti-Alias Downsampling |
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- Convolution |
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- Global Average Pooling |
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- InPlace-ABN |
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- Leaky ReLU |
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- ReLU |
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- Residual Connection |
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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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- AutoAugment |
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- Cutout |
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- Label Smoothing |
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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: 8x NVIDIA 100 GPUs |
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ID: tresnet_l |
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LR: 0.01 |
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Epochs: 300 |
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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: 0.0001 |
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Interpolation: bilinear |
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L267 |
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_81_5-235b486c.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: 81.49% |
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Top 5 Accuracy: 95.62% |
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- Name: tresnet_l_448 |
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In Collection: TResNet |
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Metadata: |
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FLOPs: 43488238584 |
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Parameters: 53456696 |
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File Size: 224440219 |
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Architecture: |
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- 1x1 Convolution |
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- Anti-Alias Downsampling |
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- Convolution |
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- Global Average Pooling |
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- InPlace-ABN |
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- Leaky ReLU |
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- ReLU |
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- Residual Connection |
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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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- AutoAugment |
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- Cutout |
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- Label Smoothing |
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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: 8x NVIDIA 100 GPUs |
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ID: tresnet_l_448 |
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LR: 0.01 |
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Epochs: 300 |
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Crop Pct: '0.875' |
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Momentum: 0.9 |
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Image Size: '448' |
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Weight Decay: 0.0001 |
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Interpolation: bilinear |
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L285 |
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_448-940d0cd1.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: 82.26% |
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Top 5 Accuracy: 95.98% |
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- Name: tresnet_m |
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In Collection: TResNet |
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Metadata: |
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FLOPs: 5733048064 |
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Parameters: 41282200 |
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File Size: 125861314 |
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Architecture: |
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- 1x1 Convolution |
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- Anti-Alias Downsampling |
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- Convolution |
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- Global Average Pooling |
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- InPlace-ABN |
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- Leaky ReLU |
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- ReLU |
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- Residual Connection |
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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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- AutoAugment |
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- Cutout |
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- Label Smoothing |
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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: 8x NVIDIA 100 GPUs |
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Training Time: < 24 hours |
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ID: tresnet_m |
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LR: 0.01 |
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Epochs: 300 |
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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: 0.0001 |
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Interpolation: bilinear |
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L261 |
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_m_80_8-dbc13962.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: 80.8% |
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Top 5 Accuracy: 94.86% |
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- Name: tresnet_m_448 |
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In Collection: TResNet |
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Metadata: |
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FLOPs: 22929743104 |
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Parameters: 29278464 |
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File Size: 125861314 |
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Architecture: |
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- 1x1 Convolution |
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- Anti-Alias Downsampling |
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- Convolution |
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- Global Average Pooling |
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- InPlace-ABN |
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- Leaky ReLU |
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- ReLU |
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- Residual Connection |
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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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- AutoAugment |
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- Cutout |
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- Label Smoothing |
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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: 8x NVIDIA 100 GPUs |
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ID: tresnet_m_448 |
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LR: 0.01 |
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Epochs: 300 |
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Crop Pct: '0.875' |
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Momentum: 0.9 |
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Image Size: '448' |
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Weight Decay: 0.0001 |
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Interpolation: bilinear |
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L279 |
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_m_448-bc359d10.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: 81.72% |
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Top 5 Accuracy: 95.57% |
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- Name: tresnet_xl |
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In Collection: TResNet |
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Metadata: |
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FLOPs: 15162534034 |
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Parameters: 75646610 |
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File Size: 314378965 |
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Architecture: |
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- 1x1 Convolution |
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- Anti-Alias Downsampling |
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- Convolution |
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- Global Average Pooling |
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- InPlace-ABN |
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- Leaky ReLU |
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- ReLU |
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- Residual Connection |
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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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- AutoAugment |
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- Cutout |
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- Label Smoothing |
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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: 8x NVIDIA 100 GPUs |
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ID: tresnet_xl |
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LR: 0.01 |
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Epochs: 300 |
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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: 0.0001 |
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Interpolation: bilinear |
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L273 |
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_xl_82_0-a2d51b00.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: 82.05% |
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Top 5 Accuracy: 95.93% |
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- Name: tresnet_xl_448 |
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In Collection: TResNet |
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Metadata: |
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FLOPs: 60641712730 |
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Parameters: 75646610 |
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File Size: 224440219 |
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Architecture: |
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- 1x1 Convolution |
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- Anti-Alias Downsampling |
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- Convolution |
|
- Global Average Pooling |
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- InPlace-ABN |
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- Leaky ReLU |
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- ReLU |
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- Residual Connection |
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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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- AutoAugment |
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- Cutout |
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- Label Smoothing |
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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: 8x NVIDIA 100 GPUs |
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ID: tresnet_xl_448 |
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LR: 0.01 |
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Epochs: 300 |
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Crop Pct: '0.875' |
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Momentum: 0.9 |
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Image Size: '448' |
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Weight Decay: 0.0001 |
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Interpolation: bilinear |
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L291 |
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_448-940d0cd1.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: 83.06% |
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Top 5 Accuracy: 96.19% |
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--> |