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-0.0.120.tar.gz
(22.0 kB
view hashes)
Built Distribution
Close
Hashes for cjm-yolox-pytorch-0.0.120.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 768a24a5cb05c7d8fed44d043e2f2c9bbaedd4342995492ae824839f20a8d5d2 |
|
MD5 | 63f6364496aed75b2c54be5c9a37b5a7 |
|
BLAKE2b-256 | 1cd37defcbe6c4640d6517dd8f3e902a536e8afaf520a142a271f6340f3047fa |
Close
Hashes for cjm_yolox_pytorch-0.0.120-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | fe263639e43247901bca06fad9c0ffb5bbb4b684ca4eb21b5a5f1a8d949cc988 |
|
MD5 | 6718c0a8a0cabf2287652a2bcb076c0e |
|
BLAKE2b-256 | 53167e0d625166a87cabb46bac96ed71e21bc35ee42e2875f9df062beeed568f |