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-0.0.130.tar.gz
(21.8 kB
view hashes)
Built Distribution
Close
Hashes for cjm-yolox-pytorch-0.0.130.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 8e3cdb57e693d1c336d45d8491f70732c2f129a3d28b0c24a6fc4b878153567a |
|
MD5 | d3623eec30b51067911a7400f0d087c3 |
|
BLAKE2b-256 | c816f4b82ce1f05048678cd69899e98e92173fb39efb8d0189dfc1317ef3bed6 |
Close
Hashes for cjm_yolox_pytorch-0.0.130-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 50a8d11d6c05dbfd8a9289044936f6c0aff498696586b19c304406feec33aceb |
|
MD5 | c67cd58104263c3760f1b34676af91e1 |
|
BLAKE2b-256 | 779706590ef7e1d342efaf1d9db5a96d9d01c66736672b85212c8e454e02c198 |