# cjm-yolox-pytorch
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
## Install
``` sh
pip install cjm_yolox_pytorch
```
## How to use
``` python
import torch
from cjm_yolox_pytorch.model import MODEL_TYPES, build_model
```
**Select model type**
``` python
model_type = MODEL_TYPES[0]
model_type
```
'yolox_tiny'
**Build YOLOX model**
``` python
yolox = build_model(model_type, 19, pretrained=True)
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]}")
```
The file ./pretrained_checkpoints/yolox_tiny.pth already exists and overwrite is set to False.
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])]
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"description": "# cjm-yolox-pytorch\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\n## Install\n\n``` sh\npip install cjm_yolox_pytorch\n```\n\n## How to use\n\n``` python\nimport torch\nfrom cjm_yolox_pytorch.model import MODEL_TYPES, build_model\n```\n\n**Select model type**\n\n``` python\nmodel_type = MODEL_TYPES[0]\nmodel_type\n```\n\n 'yolox_tiny'\n\n**Build YOLOX model**\n\n``` python\nyolox = build_model(model_type, 19, pretrained=True)\n\ntest_inp = torch.randn(1, 3, 256, 256)\n\nwith torch.no_grad():\n cls_scores, bbox_preds, objectness = yolox(test_inp)\n \nprint(f\"cls_scores: {[cls_score.shape for cls_score in cls_scores]}\")\nprint(f\"bbox_preds: {[bbox_pred.shape for bbox_pred in bbox_preds]}\")\nprint(f\"objectness: {[objectness.shape for objectness in objectness]}\")\n```\n\n The file ./pretrained_checkpoints/yolox_tiny.pth already exists and overwrite is set to False.\n cls_scores: [torch.Size([1, 19, 32, 32]), torch.Size([1, 19, 16, 16]), torch.Size([1, 19, 8, 8])]\n bbox_preds: [torch.Size([1, 4, 32, 32]), torch.Size([1, 4, 16, 16]), torch.Size([1, 4, 8, 8])]\n objectness: [torch.Size([1, 1, 32, 32]), torch.Size([1, 1, 16, 16]), torch.Size([1, 1, 8, 8])]\n",
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