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Hi, I have a custom trained yolov5 model, When i convert to fp32 and run accuracy checker on it i am facing a reshape error. to verify my configuration yaml file i have tried the same configuration file in pretrained yolov5 fp32 model and the accuracy checker works fine there . Is there anyways to merge two channels in openvino ?? Have attached the error screen grab
The yaml configuration file i used
models:
- name: yolo_v5
launchers:
- framework: dlsdk
model: /home/nga_hitech_ib/yolov5_MBU/yolov5s.xml
weights: /home/nga_hitech_ib/yolov5_MBU/yolov5s.bin
device: CPU
adapter:
type: yolo_v5
anchors: "10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326"
num: 3
coords: 4
classes: 80
threshold: 0.001
anchor_masks: [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
raw_output: True
outputs:
- '345'
- '404'
- '463'
datasets:
- name: COCO2017_detection_80cl
data_source: /home/nga_hitech_ib/model_test/coco_2017/val2017
annotation_conversion:
converter : mscoco_detection
annotation_file: /home/nga_hitech_ib/model_test/coco_2017/instances_val2017.json
images_dir: /home/nga_hitech_ib/model_test/coco_2017/val2017
has_background: false
use_full_label_map: false
preprocessing:
- type: resize
size: 640
postprocessing:
- type: resize_prediction_boxes
- type: filter
apply_to: prediction
min_confidence: 0.001
remove_filtered: true
- type: nms
overlap: 0.5
- type: clip_boxes
apply_to: prediction
metrics:
- type: map
integral: 11point
ignore_difficult: true
presenter: print_scalar
- name: AP@0.5
type: coco_precision
max_detections: 100
threshold: 0.5
- name: AP@0.5:0.05:95
type: coco_precision
max_detections: 100
threshold: '0.5:0.05:0.95'
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Hi, @Wan_Intel
So I remembered I used only 4 classes , and changed 80 to 4 in the configuration file its working now, Thank you so much for help.
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Hi Ashwin_J_S,
Thanks for your information.
I’ll share the information with next level for further investigation. Meanwhile, is it possible to share your custom model with us?
Regards,
Wan
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Thank you for the update @Wan_Intel , But as I mentioned before unfortunately I am prohibited to share the model with you.
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Hi Ashwin_J_S,
Noted with thanks.
Could you please share the config file you used with the Accuracy Checker?
Regards,
Wan
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Sure @Wan_Intel ,
models:
- name: yolo_v5
launchers:
- framework: dlsdk
device: CPU
adapter:
type: yolo_v5
anchors: "10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326"
num: 3
coords: 4
classes: 80
threshold: 0.001
anchor_masks: [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
raw_output: True
outputs:
- '443'
- '487'
- '531'
datasets:
- name: small
data_source: "/home/ubuntu/coco_2017/val2017/"
annotation_conversion:
converter: mscoco_detection
annotation_file: "/home/ubuntu/coco_2017/annotations/instances_val2017.json"
images_dir: "/home/ubuntu/coco_2017/val2017/"
preprocessing:
- type: resize
size: 640
postprocessing:
- type: resize_prediction_boxes
- type: filter
apply_to: prediction
min_confidence: 0.001
remove_filtered: True
- type: nms
overlap: 0.5
- type: clip_boxes
apply_to: prediction
metrics:
- type: map
integral: 11point
ignore_difficult: true
presenter: print_scalar
- type: coco_precision
max_detections: 100
threshold: 0.5
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Hi Ashwin_J_S,
Could you please use anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]] in the config file and run Accuracy Checker again?
Regards,
Wan
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Hi @Wan_Intel , Yes I am able to use the benchmark_app with my custom model.
benchmark_app -m last.xml -niter 100
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Hi Ashwin_J_S,
Could you share the command you use to run Accuracy Checker?
Regards,
Wan
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Hi Ashwin_J_S,
I encountered the same error when I change the classes number.
May I know how many classes did you used to train the model?
Regards,
Wan
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Hi, @Wan_Intel
So I remembered I used only 4 classes , and changed 80 to 4 in the configuration file its working now, Thank you so much for help.
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Hi Ashwin_J_S,
Great! We’re happy that you are now able to run your model with Accuracy Checker.
This thread will no longer be monitored since this issue has been resolved.
If you need any additional information from Intel, please submit a new question.
Thanks, and best regards,
Wan
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