NOTE: *You can probably ignore the paragraph below if you have deep technical knowledge of sklearn and ML in general.*

I am working on indexing image objects based on their position in an image. Their index is relative to the other image objects in each image,which varies significantly, so simple math will not work in indexing them. Moreover, I have tried to index them via their middle x coordinate in the image, but that only yields an accuracy of ~75% with sklearn DecisionTreeRegressor. Now I want to try to train a model to index them from their detection box's (obtained from tensorflow object recognition + pretrained neural network) x1,y1 or x1,x2,y1,y2 coordinates.

So here's my question:

Is an array such as

```
[
[[x0_0_0, x0_0_1], # <- object 1 x,y coords for image 1
[x0_1_0, x0_1_1]], # <- object 2 x,y coords for image 1
[ ... ],
[xn_0_0, xn_0_1], # <- object 1 x,y coords for image n
[xn_1_0, xn_1_1]] # <- object 1 x,y coords for image n
]
```

with a target array of

```
[[y0_0, y0_1], # <- indices of objects 1 and 2 in image 1
[ ... ],
[yn_0, yn_1]] # <- indices of objects 1 and 2 in image n
```

Viable for use in any supervised ML algorithms packaged in sklearn?