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Fixes the usage of SimCLR loss in README.md #107

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Fixes the usage of SimCLR loss in README.md #107

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9 changes: 5 additions & 4 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -12,14 +12,15 @@ This repo covers an reference implementation for the following papers in PyTorch
ImageNet model (small batch size with the trick of the momentum encoder) is released [here](https://www.dropbox.com/s/l4a69ececk4spdt/supcon.pth?dl=0).

## Loss Function
The loss function [`SupConLoss`](https://github.com/HobbitLong/SupContrast/blob/master/losses.py#L11) in `losses.py` takes `features` (L2 normalized) and `labels` as input, and return the loss. If `labels` is `None` or not passed to the it, it degenerates to SimCLR.
The loss function [`SupConLoss`](https://github.com/HobbitLong/SupContrast/blob/master/losses.py#L11) in `losses.py` takes `features` (L2 normalized) and `labels` as input, and return the loss. If `labels` is `None` or not passed to the it, it degenerates to SimCLR. To use SimCLR loss, temperature and base_temperature must be set to the same value.

Usage:
```python
from losses import SupConLoss

# define loss with a temperature `temp`
criterion = SupConLoss(temperature=temp)
criterionSupContrast = SupConLoss(temperature=temp)
criterionSimCLR = SupConLoss(temperature=temp, base_temperature=temp)

# features: [bsz, n_views, f_dim]
# `n_views` is the number of crops from each image
Expand All @@ -29,9 +30,9 @@ features = ...
labels = ...

# SupContrast
loss = criterion(features, labels)
loss = criterionSupContrast(features, labels)
# or SimCLR
loss = criterion(features)
loss = criterionSimCLR(features)
...
```

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