A PyTorch implementation of the YOLOX object detection model based on the mmdetection implementation.
Project description
cjm-yolox-pytorch
Install
pip install cjm_yolox_pytorch
How to use
from cjm_yolox_pytorch.model import MODEL_TYPES, build_model
Select model type
model_type = MODEL_TYPES[0]
model_type
'yolox_tiny'
Build YOLOX model
import torch
yolox = build_model(model_type, 19)
test_inp = torch.randn(1, 3, 256, 256)
with torch.no_grad():
cls_scores, bbox_preds, objectness = yolox(test_inp)
print(f"cls_scores: {[cls_score.shape for cls_score in cls_scores]}")
print(f"bbox_preds: {[bbox_pred.shape for bbox_pred in bbox_preds]}")
print(f"objectness: {[objectness.shape for objectness in objectness]}")
cls_scores: [torch.Size([1, 19, 32, 32]), torch.Size([1, 19, 16, 16]), torch.Size([1, 19, 8, 8])]
bbox_preds: [torch.Size([1, 4, 32, 32]), torch.Size([1, 4, 16, 16]), torch.Size([1, 4, 8, 8])]
objectness: [torch.Size([1, 1, 32, 32]), torch.Size([1, 1, 16, 16]), torch.Size([1, 1, 8, 8])]
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
cjm-yolox-pytorch-0.0.125.tar.gz
(21.9 kB
view hashes)
Built Distribution
Close
Hashes for cjm-yolox-pytorch-0.0.125.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 39dde60463f12920c0011e97b329af2e9cb729d76cada9a7341a19818fa6f480 |
|
MD5 | f6f20b68079a805b505069f81689f20c |
|
BLAKE2b-256 | 231c61f5acfd8bfc4b0afc1b757bda16d045d6adb3070f23034fb600b944c3b9 |
Close
Hashes for cjm_yolox_pytorch-0.0.125-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | bf6a6a03118559ef7c4f968012f4e6cae6ad8685a905d0f1275747edc1280d32 |
|
MD5 | 9720934acc15819a000a877ad89ec6b8 |
|
BLAKE2b-256 | 63823d3415841a52932d4a4be68d142eafe50e61e7772b714e604a6c73d6b474 |