A simple PyTorch implementation of focal loss.
Project description
focal-loss-pytorch
Simple vectorized PyTorch implementation of binary unweighted focal loss as specified by [1].
References
[1] Lin, T. Y., et al. "Focal loss for dense object detection." arXiv 2017." arXiv preprint arXiv:1708.02002 (2002).
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
focal_loss_pytorch-0.0.1.tar.gz
(13.9 kB
view details)
Built Distribution
File details
Details for the file focal_loss_pytorch-0.0.1.tar.gz
.
File metadata
- Download URL: focal_loss_pytorch-0.0.1.tar.gz
- Upload date:
- Size: 13.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.9.12
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | b83c56208bd4e68d6cd513f25ab86e2df3e544520be10c5814786f768686816a |
|
MD5 | 620fac2c29c79a431fa9a821b8906be1 |
|
BLAKE2b-256 | 3db063390c9bdbe2e7f8f9bcaa1a7b6d28462f21a7d56654362b2be3605138c3 |
File details
Details for the file focal_loss_pytorch-0.0.1-py3-none-any.whl
.
File metadata
- Download URL: focal_loss_pytorch-0.0.1-py3-none-any.whl
- Upload date:
- Size: 13.8 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.9.12
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0e4a1969394d6913fa27ca367fb815a29f2069ec17f11c7e22c628e6d9c20363 |
|
MD5 | 9334bcb7c319a80a9a5a9c2464c7450c |
|
BLAKE2b-256 | b1be8cd5f58eb74b933e10861dc518c151ee1b65264a2c7f5ac2f09839b8ac7d |