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-0.0.121.tar.gz
(21.9 kB
view hashes)
Built Distribution
Close
Hashes for cjm-yolox-pytorch-0.0.121.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | d6f8af0ded7a20d614a20ac409b3f1007ab438541f5b035d2862aef1b03104fa |
|
MD5 | 5ad20a39e91e89aa1de76b04989fe4cd |
|
BLAKE2b-256 | 89f31fdc95c9f8ff844a9d352c7adcd8bae31d67053304bed4dc2b3101317a9b |
Close
Hashes for cjm_yolox_pytorch-0.0.121-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 00a3a2491ce445cd26ed7d204b10ae55b8628d2fa71f2ca263bc06d04fe4cae3 |
|
MD5 | e9cfe16efb331c55fd08221d00c60552 |
|
BLAKE2b-256 | 769d20cab61be244a9462446e18bc3b295233f81552b4b261d8404c408e9b0de |