Comments (9)
hi there,
Thanks for reporting the problem - may I ask when the model was trained? (I assume u trained model @ Roboflow platform)
I am asking as we've this problem reported and reverted changes making models being opset 17 into 16 again.
from inference.
When I update the inference package manually the opset issue is solved indeed, but the CUDAExecutionProvider cannot be found and therefore defaults to the CPUExecutionProvider. Are you sure the latest inference code is compatible with CUDA 10.2.3?
from inference.
yolov8s (OD) Generated on Feb 5, 2024 = no issue
yolov8s (OD) Generated on Feb 22, 2024 = no issue
yolov8s (OD) Generated on Mar 5, 2024 = Issue
yolov8s (OD) Generated on Mar 20, 2024 = issue
yolov8s (OD) Generated on Mar 25, 2024 = issue
Roboflow 3.0 (OD) Generated on Mar 13, 2024 = NO issue
Roboflow 3.0 (OD) Generated on Mar 18, 2024 = No issue
Roboflow 3.0 (OD) Generated on Mar 18, 2024 = NO issue
Roboflow 3.0 (OD) Generated on Apr 19, 2024 = Issue
yolov8s (IS) Generated on Feb 26, 2024 = no issue
yolov8l (IS) Generated on Mar 5, 2024 = issue
yolov8s (IS) Generated on Mar 6, 2024 = issue
yolov8s (IS) Generated on Mar 14, 2024 = issue
yolov8s (IS) Generated on Mar 28, 2024 = issue
Roboflow 3.0 (IS) Generated on Mar 14, 2024 = NO issue
Roboflow 3.0 (IS) Generated on Apr 17, 2024 = issue
from inference.
OD = Object detection
IS = Instance Segmentation
from inference.
So only new models trained will be compatible again?
from inference.
Let's connect through e-mail ([email protected])
What u report is indeed worrying - I would like to be able to take a look at models artefacts to verify what's going on, but I would need to know internal details about ur project at the platform to figure out the issue
from inference.
I already reported and shown the issue to Jack Gallo, he knows the details
from inference.
It would basically be one line of code if you do Yolo.export(format="onnx", opset=16) if onnxruntime version < 1.12.0
from inference.
Ok, I will ask Jack.
and yes, we also though so in terms of solution
from inference.
Related Issues (20)
- Remove PARENT_ID from top-level outputs and keep only in sv.Detections.data
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- Better structure for LMM `workflows` blocks
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- Discussion: How to structure LLM, LMM blocks regarding structured usage of outputs in other blocks
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- `DocTR` model output missing important information that model produces
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- Make the descriptions of all models parameters easier to understand (especially for people without in-depth CV context)
- Improve `workflows` docs to explain on how init parameters are passed to blocks
- Should we have multiple types of models [in `workflows`]?
- Get rid of `asyncio` in `workflows` and in some parts of `inference`
- How to handle cropping in `workflows`?
- Tests assets - static files or pulled from Roboflow hosting?
- Shall blocks types be shadowed by plugins?
- Can I stream the video with bounding boxes as an RTSP stream to a server? HOT 2
- ❗ Get rid of `sv.BoxAnnotator` from `InferencePipeline` sink before it gets removed in `supervision==0.22.0` HOT 1
- `inference` crash during installation HOT 3
- Add Python 3.12 support
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from inference.