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132 lines
4.4 KiB
132 lines
4.4 KiB
# Inception v4
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**Inception-v4** is a convolutional neural network architecture that builds on previous iterations of the Inception family by simplifying the architecture and using more inception modules than [Inception-v3](https://paperswithcode.com/method/inception-v3).
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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('inception_v4', 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. `inception_v4`. 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('inception_v4', 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](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{szegedy2016inceptionv4,
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title={Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning},
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author={Christian Szegedy and Sergey Ioffe and Vincent Vanhoucke and Alex Alemi},
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year={2016},
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eprint={1602.07261},
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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: Inception v4
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Paper:
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Title: Inception-v4, Inception-ResNet and the Impact of Residual Connections on
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Learning
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URL: https://paperswithcode.com/paper/inception-v4-inception-resnet-and-the-impact
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Models:
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- Name: inception_v4
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In Collection: Inception v4
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Metadata:
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FLOPs: 15806527936
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Parameters: 42680000
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File Size: 171082495
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Architecture:
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- Average Pooling
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- Dropout
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- Inception-A
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- Inception-B
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- Inception-C
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- Reduction-A
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- Reduction-B
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- Label Smoothing
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- RMSProp
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- Weight Decay
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Training Data:
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- ImageNet
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Training Resources: 20x NVIDIA Kepler GPUs
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ID: inception_v4
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LR: 0.045
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Dropout: 0.2
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '299'
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/inception_v4.py#L313
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/inceptionv4-8e4777a0.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: 1.01%
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Top 5 Accuracy: 16.85%
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--> |