Instructions to use machinelearningzuu/queue_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use machinelearningzuu/queue_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="machinelearningzuu/queue_detection")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("machinelearningzuu/queue_detection") model = AutoModelForObjectDetection.from_pretrained("machinelearningzuu/queue_detection") - Notebooks
- Google Colab
- Kaggle
Upload ConditionalDetrForObjectDetection
Browse files- README.md +1 -1
- config.json +1 -1
README.md
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
---
|
| 2 |
-
license: apache-2.0
|
| 3 |
base_model: microsoft/conditional-detr-resnet-50
|
|
|
|
| 4 |
tags:
|
| 5 |
- generated_from_trainer
|
| 6 |
model-index:
|
|
|
|
| 1 |
---
|
|
|
|
| 2 |
base_model: microsoft/conditional-detr-resnet-50
|
| 3 |
+
license: apache-2.0
|
| 4 |
tags:
|
| 5 |
- generated_from_trainer
|
| 6 |
model-index:
|
config.json
CHANGED
|
@@ -1,5 +1,5 @@
|
|
| 1 |
{
|
| 2 |
-
"_name_or_path": "
|
| 3 |
"activation_dropout": 0.0,
|
| 4 |
"activation_function": "relu",
|
| 5 |
"architectures": [
|
|
|
|
| 1 |
{
|
| 2 |
+
"_name_or_path": "machinelearningzuu/queue_detection",
|
| 3 |
"activation_dropout": 0.0,
|
| 4 |
"activation_function": "relu",
|
| 5 |
"architectures": [
|