there's no "predict_batch" option in the web interface...

models/{model_name}/predict_batch (POST)
Performs inference on specified model and a list of images, and returns bounding boxes

Couln't find a way to load models, In the tutorial, it is shown that model path is name like dummy_5000, which directory it is going to search for dummy_5000. I also trained yolov3 using sample dataset to get all 5 files needed, but have no idea how to load them into inference.

I get the following error when trying to load the models from the docs web interface.
the obj.name file is in the "models" dir and I tried to put relate and absolute path in the Yolo.data file.

names=/home/sim/Desktop/BMW-YOLOv4-Inference-API-GPU/models/tiny_fhd/obj.names

INFO:     Started server process [1]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:1234 (Press CTRL+C to quit)
Couldn't open file: /home/sim/Desktop/BMW-YOLOv4-Inference-API-GPU/models/tiny_fhd/obj.names

Hi, can improve the docs about models folder structure.
this is my config:
/root/BMW-YOLOv4-Inference-API-GPU/models/
/root/BMW-YOLOv4-Inference-API-GPU/models/yolov4_darknet_detection
/root/BMW-YOLOv4-Inference-API-GPU/models/yolov4_darknet_detection/yolov4.weights
/root/BMW-YOLOv4-Inference-API-GPU/models/yolov4_darknet_detection/obj.data
/root/BMW-YOLOv4-Inference-API-GPU/models/yolov4_darknet_detection/config.json
/root/BMW-YOLOv4-Inference-API-GPU/models/yolov4_darknet_detection/yolov4.cfg
/root/BMW-YOLOv4-Inference-API-GPU/models/yolov4_darknet_detection/obj.names
/root/BMW-YOLOv4-Inference-API-GPU/models/.gitignore

And this is de content of the files:
/root/BMW-YOLOv4-Inference-API-GPU/models/yolov4_darknet_detection/obj.data:
classes=80
names=/models/yolov4/obj.names
/root/BMW-YOLOv4-Inference-API-GPU/models/yolov4_darknet_detection/config.json:
{
"inference_engine_name": "yolov4_darknet_detection",
"detection_threshold": 0.6,
"nms_threshold": 0.45,
"hier_threshold": 0.5,
"framework": "yolo",
"type": "detection",
"network": "network_name"
}
/root/BMW-YOLOv4-Inference-API-GPU/models/yolov4_darknet_detection/yolov4.cfg:
[net]
batch=64
subdivisions=8

Training

#width=512
#height=512
width=608
height=608
channels=3
momentum=0.949
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1

learning_rate=0.0013
burn_in=1000
max_batches = 500500
policy=steps
steps=400000,450000
scales=.1,.1

#cutmix=1
mosaic=1

#:104x104 54:52x52 85:26x26 104:13x13 for 416

[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=mish

Downsample

[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish

[route]
layers = -2

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish

[route]
layers = -1,-7

[convolutional]
.....
/root/BMW-YOLOv4-Inference-API-GPU/models/yolov4_darknet_detection/obj.names:
person
bicycle
car
motorbike
aeroplane
bus
train
truck
boat
traffic light
fire hydrant
stop sign
parking meter
bench
bird
cat
dog
horse
sheep
cow
elephant
bear
zebra
giraffe
backpack
umbrella
handbag
tie
suitcase
frisbee
skis
snowboard
sports ball
kite
baseball bat
baseball glove
skateboard
surfboard
tennis racket
bottle
wine glass
cup
fork
knife
spoon
bowl
banana
apple
sandwich
orange
broccoli
carrot
hot dog
pizza
donut
cake
chair
sofa
pottedplant
bed
diningtable
toilet
tvmonitor
laptop
mouse
remote
keyboard
cell phone
microwave
oven
toaster
sink
refrigerator
book
clock
vase
scissors
teddy bear
hair drier
toothbrush
And this is the response when try to load from http://..docs#/default/load_custom_load_get
{
"data": null,
"error": "Inference engine not found: yolov4_darknet_detection",
"success": false
}
Thanks.