| Name | Version | Summary | date |
| smart-reid |
0.1.6 |
With no prior knowledge of machine learning or device-specific deployment, you can deploy a computer vision model to a range of devices and environments using Roboflow Inference CLI. |
2024-12-18 21:31:03 |
| inference-sdk |
0.31.1 |
With no prior knowledge of machine learning or device-specific deployment, you can deploy a computer vision model to a range of devices and environments using Roboflow Inference. |
2024-12-13 18:57:28 |
| inference-gpu |
0.31.1 |
With no prior knowledge of machine learning or device-specific deployment, you can deploy a computer vision model to a range of devices and environments using Roboflow Inference. |
2024-12-13 18:57:25 |
| inference-cpu |
0.31.1 |
With no prior knowledge of machine learning or device-specific deployment, you can deploy a computer vision model to a range of devices and environments using Roboflow Inference. |
2024-12-13 18:57:23 |
| inference-core |
0.31.1 |
With no prior knowledge of machine learning or device-specific deployment, you can deploy a computer vision model to a range of devices and environments using Roboflow Inference. |
2024-12-13 18:57:20 |
| inference-cli |
0.31.1 |
With no prior knowledge of machine learning or device-specific deployment, you can deploy a computer vision model to a range of devices and environments using Roboflow Inference CLI. |
2024-12-13 18:57:17 |
| inference |
0.31.1 |
With no prior knowledge of machine learning or device-specific deployment, you can deploy a computer vision model to a range of devices and environments using Roboflow Inference. |
2024-12-13 18:57:13 |
| roboflow |
1.1.50 |
Official Python package for working with the Roboflow API |
2024-12-13 15:09:21 |
| autodistill |
0.1.29 |
Distill large foundational models into smaller, domain-specific models for deployment |
2024-11-26 13:08:00 |
| autodistill-yolov11 |
0.1.4 |
Label data with and train YOLOv11 models. |
2024-10-03 08:03:50 |
| autodistill-grounded-sam-2 |
0.1.0 |
Use Segment Anything 2, grounded with Florence-2, to auto-label data for use in training vision models. |
2024-07-30 12:02:12 |
| autodistill-florence-2 |
0.1.0 |
Use Florence 2 to auto-label data for use in training fine-tuned object detection models. |
2024-06-19 14:36:46 |
| autodistill-paligemma |
0.1.1 |
Auto-label data with a PaliGemma model, or ine-tune a PaLiGemma model using custom data with Autodistill. |
2024-06-13 10:48:45 |
| autodistill-setfit |
0.1.1 |
Train SetFit models with Autodistill |
2024-06-11 16:25:23 |
| autodistill-fastvit |
0.1.2 |
FastViT model for use with Autodistill |
2024-05-20 14:48:42 |
| autodistill-gpt-4o |
0.1.5 |
GPT-4o model for use with Autodistill |
2024-05-15 08:54:59 |
| autodistill-grounding-dino |
0.1.4 |
GroundingDINO module for use with Autodistill |
2024-04-26 15:36:42 |
| rf-yoloworld |
0.0.1 |
|
2024-02-22 16:37:16 |
| yoloworld |
0.0.1 |
|
2024-02-22 16:37:00 |
| autodistill-efficient-yolo-world |
0.1.1 |
EfficientSAM + YOLO-World base model for use with Autodistill |
2024-02-21 09:31:15 |
Roboflow