Hailo Model Zoo YOLOv8 retraining environment

General
,
7

@nina-vilela
This is my scripts for compile custom retrained yolov8n model
(Input size=1280x1280, Classes=6, DFCversion=3.27.0)

# custom_yolov8n.yaml
base:
- base/yolov8.yaml
postprocessing:
  device_pre_post_layers:
    nms: true
  hpp: true
network:
  network_name: yolov8n
paths: 
  alls_script: custom_yolov8n.alls
parser:
  nodes:
  - null
  - - /model.22/cv2.0/cv2.0.2/Conv
    - /model.22/cv3.0/cv3.0.2/Conv
    - /model.22/cv2.1/cv2.1.2/Conv
    - /model.22/cv3.1/cv3.1.2/Conv
    - /model.22/cv2.2/cv2.2.2/Conv
    - /model.22/cv3.2/cv3.2.2/Conv
quantization:
  calib_set:
  - models_files/my_dataset/adm_bbox_v2_calib_8000.tfrecord
preprocessing:
  network_type: detection
  input_shape:
  - 1280
  - 1280
  - 3
  meta_arch: yolo_v5
  padding_color: 114

.

# custom_yolov8n.alls
normalization1 = normalization([0.0, 0.0, 0.0], [255.0, 255.0, 255.0])

model_optimization_flavor(optimization_level=4)

change_output_activation(conv42, sigmoid)
change_output_activation(conv53, sigmoid)
change_output_activation(conv63, sigmoid)

performance_param(compiler_optimization_level=max)

nms_postprocess("../../postprocess_config/custom_yolov8n_nms_config_1280.json", meta_arch=yolov8, engine=cpu)

model_optimization_config(calibration, batch_size=16, calibset_size=8000)
post_quantization_optimization(finetune, policy=enabled, dataset_size=8000, epochs=8, learning_rate=0.0001)

.

# custom_yolov8n_nms_config_1280.json
{
	"nms_scores_th": 0.2,
	"nms_iou_th": 0.7,
	"image_dims": [
		1280,
		1280
	],
	"max_proposals_per_class": 100,
	"classes": 6,
	"regression_length": 16,
	"background_removal": false,
	"bbox_decoders": [
		{
			"name": "yolov8n/bbox_decoder41",
			"stride": 8,
			"reg_layer": "yolov8n/conv41",
			"cls_layer": "yolov8n/conv42"
		},
		{
			"name": "yolov8n/bbox_decoder52",
			"stride": 16,
			"reg_layer": "yolov8n/conv52",
			"cls_layer": "yolov8n/conv53"
		},
		{
			"name": "yolov8n/bbox_decoder62",
			"stride": 32,
			"reg_layer": "yolov8n/conv62",
			"cls_layer": "yolov8n/conv63"
		}
	]
}

So by compiling the above script, I was able to get a maximum mAP50 score of 0.65.
Now, I’m trying to optimize with full-16-bit and add this solutions as you recommanded.