https://github.com/AdeelH/pytorch-multi-class-focal-loss

An (unofficial) implementation of Focal Loss, as described in the RetinaNet paper, generalized to the multi-class case.
https://github.com/AdeelH/pytorch-multi-class-focal-loss

classification deep-learning imbalanced-classes implementation-of-research-paper loss-functions machine-learning multiclass-classification neural-network pytorch pytorch-implementation retinanet

Last synced: 3 months ago
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An (unofficial) implementation of Focal Loss, as described in the RetinaNet paper, generalized to the multi-class case.

• Host: GitHub
• URL: https://github.com/AdeelH/pytorch-multi-class-focal-loss
• Owner: AdeelH
• License: mit
• Created: 2020-09-03T09:08:36.000Z (about 4 years ago)
• Default Branch: master
• Last Pushed: 2024-01-22T19:03:41.000Z (10 months ago)
• Last Synced: 2024-04-20T17:00:32.817Z (7 months ago)
• Topics: classification, deep-learning, imbalanced-classes, implementation-of-research-paper, loss-functions, machine-learning, multiclass-classification, neural-network, pytorch, pytorch-implementation, retinanet
• Language: Python
• Homepage:
• Size: 27.3 KB
• Stars: 205
• Watchers: 4
• Forks: 24
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# Multi-class Focal Loss

An (unofficial) implementation of Focal Loss, as described in the RetinaNet paper, https://arxiv.org/abs/1708.02002, generalized to the multi-class case.

It is essentially an enhancement to cross-entropy loss and is useful for classification tasks when there is a large class imbalance. It has the effect of underweighting easy examples.

# Usage

- `FocalLoss` is an `nn.Module` and behaves very much like `nn.CrossEntropyLoss()` i.e.

    - supports the `reduction` and `ignore_index` params, and

    - is able to work with 2D inputs of shape `(N, C)` as well as K-dimensional inputs of shape `(N, C, d1, d2, ..., dK)`.

- Example usage

    ```python3

    focal_loss = FocalLoss(alpha, gamma)

	...

	inp, targets = batch

    out = model(inp)

	loss = focal_loss(out, targets)

    ```

# Loading through torch.hub

This repo supports importing modules through `torch.hub`. `FocalLoss` can be easily imported into your code via, for example:

```python3

focal_loss = torch.hub.load(

	'adeelh/pytorch-multi-class-focal-loss',

	model='FocalLoss',

	alpha=torch.tensor([.75, .25]),

	gamma=2,

	reduction='mean',

	force_reload=False

)

x, y = torch.randn(10, 2), (torch.rand(10) > .5).long()

loss = focal_loss(x, y)

```

Or:

```python3

focal_loss = torch.hub.load(

	'adeelh/pytorch-multi-class-focal-loss',

	model='focal_loss',

	alpha=[.75, .25],

	gamma=2,

	reduction='mean',

	device='cpu',

	dtype=torch.float32,

	force_reload=False

)

x, y = torch.randn(10, 2), (torch.rand(10) > .5).long()

loss = focal_loss(x, y)

```