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# MixNet |
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**MixNet** is a type of convolutional neural network discovered via AutoML that utilises [MixConvs](https://paperswithcode.com/method/mixconv) instead of regular [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution). |
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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('mixnet_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. `mixnet_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('mixnet_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](../scripts) for training a new model afresh. |
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## Citation |
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```BibTeX |
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@misc{tan2019mixconv, |
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title={MixConv: Mixed Depthwise Convolutional Kernels}, |
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author={Mingxing Tan and Quoc V. Le}, |
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year={2019}, |
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eprint={1907.09595}, |
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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: MixNet |
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Paper: |
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Title: 'MixConv: Mixed Depthwise Convolutional Kernels' |
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URL: https://paperswithcode.com/paper/mixnet-mixed-depthwise-convolutional-kernels |
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Models: |
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- Name: mixnet_l |
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In Collection: MixNet |
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Metadata: |
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FLOPs: 738671316 |
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Parameters: 7330000 |
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File Size: 29608232 |
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Architecture: |
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- Batch Normalization |
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- Dense Connections |
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- Dropout |
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- Global Average Pooling |
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- Grouped Convolution |
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- MixConv |
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- Squeeze-and-Excitation Block |
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- Swish |
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Tasks: |
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- Image Classification |
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Training Techniques: |
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- MNAS |
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Training Data: |
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- ImageNet |
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ID: mixnet_l |
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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/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1669 |
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_l-5a9a2ed8.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: 78.98% |
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Top 5 Accuracy: 94.18% |
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- Name: mixnet_m |
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In Collection: MixNet |
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Metadata: |
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FLOPs: 454543374 |
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Parameters: 5010000 |
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File Size: 20298347 |
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Architecture: |
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- Batch Normalization |
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- Dense Connections |
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- Dropout |
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- Global Average Pooling |
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- Grouped Convolution |
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- MixConv |
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- Squeeze-and-Excitation Block |
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- Swish |
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Tasks: |
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- Image Classification |
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Training Techniques: |
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- MNAS |
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Training Data: |
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- ImageNet |
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ID: mixnet_m |
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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/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1660 |
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_m-4647fc68.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: 77.27% |
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Top 5 Accuracy: 93.42% |
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- Name: mixnet_s |
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In Collection: MixNet |
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Metadata: |
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FLOPs: 321264910 |
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Parameters: 4130000 |
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File Size: 16727982 |
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Architecture: |
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- Batch Normalization |
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- Dense Connections |
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- Dropout |
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- Global Average Pooling |
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- Grouped Convolution |
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- MixConv |
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- Squeeze-and-Excitation Block |
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- Swish |
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Tasks: |
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- Image Classification |
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Training Techniques: |
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- MNAS |
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Training Data: |
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- ImageNet |
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ID: mixnet_s |
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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/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1651 |
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_s-a907afbc.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: 75.99% |
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Top 5 Accuracy: 92.79% |
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- Name: mixnet_xl |
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In Collection: MixNet |
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Metadata: |
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FLOPs: 1195880424 |
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Parameters: 11900000 |
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File Size: 48001170 |
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Architecture: |
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- Batch Normalization |
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- Dense Connections |
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- Dropout |
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- Global Average Pooling |
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- Grouped Convolution |
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- MixConv |
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- Squeeze-and-Excitation Block |
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- Swish |
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Tasks: |
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- Image Classification |
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Training Techniques: |
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- MNAS |
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Training Data: |
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- ImageNet |
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ID: mixnet_xl |
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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/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1678 |
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_xl_ra-aac3c00c.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: 80.47% |
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Top 5 Accuracy: 94.93% |
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--> |