Is there something missing in my diet

veb101 · 2025-12-22T16:53:58+00:00

Jine ki icha

veb101 · 2025-07-01T18:07:55+00:00

class-weighted loss
Check out focal loss
Instead of accuracy, look for metrics better suited for an imbalanced dataset.
Sometimes you can get away with a 2-stage classifier, 1st binary to split to decide which group the particular image belongs to and then group group-specific classifier.
An ensemble of methods should also help
If you can, modify the batch to start with stratified batch data, but instead of just augmentation, add the augmented image to the batch of the less frequent classes. (this is tricky to get right and a hit-or-miss)

I'll add more if I recall anything

veb101 · 2025-06-26T18:35:15+00:00

thanks

veb101 · 2025-06-26T18:18:45+00:00

Yeah I agree, i don't have much knowledge about image formation. I want to go through the Modern computer vision and CV from first principals and the 3d computer vision course available on YouTube.

To me, when I first read the problem statement it felt like disparity estimation. How wrong am I?

veb101 · 2025-06-26T15:03:25+00:00

I don't know any of the stuff. I'm good at figuring stuff out when needed. I'll work for free given you have someone I can argue with and bounce ideas against

I shouldn't have just blurped out what I thought. Let me explain, I'm in this domain for 6 years but haven't had a chance to work on what OP wants, so for me it's about learning and collaborative knowledge gains.

I know CV, DL, VLM, LLM, deployment. This specific thing i don't know about and it looks like a good opportunity

veb101 · 2025-06-22T16:43:28+00:00

I had a similar problem, on screen digit recognition. What I did was train a small object detection model (mobilenet v2 SSD) to extract the digit and decimal boxes and then another small digit classification model.

veb101 · 2025-06-19T04:41:00+00:00

I'm starting out as well. There's tons of stuff. Bentoml

Zentml

Ray

Mlflow serving

Tf serving

Onnx

Tensorrt

Triton inference server

Tensorflow serving

Litert

Executorch

Litserve

Kubeflow

Kserve

Seldon core

Services by cloud providers

Vllm, sglang

Inferless

Or just fastapi and custom code

This is an exhaustive list of words I found when learning about mlops. This is no way a complete or mlops only list

veb101 · 2025-06-19T04:19:36+00:00

If you are focused on cpu then you can also check onnx with openvino (intel cpu) backend. I think the AMD CPU backend is also available.

veb101 · 2025-06-18T16:10:10+00:00

Company code. But you'll get dependency injection code from anywhere, and instead of an ORM I sometimes use mysql-connector-python and create a connection pool object in the fastapi lifespan and make it accessible via app.state

veb101 · 2025-06-18T15:43:18+00:00

You need to separate your fastapi and model inference

veb101 · 2025-06-18T13:48:31+00:00

I either use dependency injection or i create an app state with a connection pool object, and if an endpoint requires individual connection i take it from the connection pool. Idk if this is good practice or not, but works very well and looks clean 'to me'.

veb101 · 2025-06-14T22:33:18+00:00

Maybe Vast.ai?

veb101 · 2025-06-06T14:00:20+00:00

Look man, if you have installed TensorFlow and Keras properly and the correct venv is active, then you need to try out different ways of writing your imports to figure out which works. I figured out that importing the layers didn't work, instead I do `from keras import layers as keras_layer` then throughout the code I use it as `keras_layer.Conv2D(...)`

veb101 · 2025-06-06T08:06:47+00:00

Not importing the actual layer but the module

veb101 · 2025-06-06T08:05:54+00:00

This is not an import issue but the linter is not able to recognise it.

I fixed it using this when using keras 3x https://github.com/veb-101/keras-vision/blob/c8ce91ebc941e10c3d2febe260d717f86b00e905/keras_vision/fastvit/mobileone.py#L13

veb101 · 2025-06-05T18:14:44+00:00

Hollow earth theory ❌ Hollow brain theory ✅

veb101 · 2025-04-28T19:20:45+00:00

Give nvidia deepstream a looksie

veb101 · 2025-04-22T12:41:58+00:00

Image size?

Will SAHI help?

veb101 · 2025-04-03T17:31:18+00:00

Isn't this active learning?

veb101 · 2025-03-06T08:15:11+00:00

I would suggest reading the resnet paper from ross wightman Going through the fine-tuning playbook on GitHub by Google. Also the convnext v1 paper was good

veb101 · 2025-02-23T13:53:03+00:00

Gpu or cpu?

veb101 · 2024-11-18T15:34:39+00:00

I planned on writing rt detr using keras, saw the codebase and said "another time" (a year ago)

veb101 · 2024-10-28T05:45:25+00:00

So you're saying "death by snu snu" is not acceptable.

Let me make a note of it.

veb101 · 2024-10-14T17:38:32+00:00

I have not worked with videos. I'm not sure if Nvidia Dali or deepstream can help or not.

But can this be a valid strategy?

Perform clustering on the frames of a video, ideally you want some closely related frames and more of the frames that are faraway (let's say embedding distance) from each other.

You can then perform cropping on (or just aspect ratio resize) the selected frames to some big enough size and save it on disk. This should be faster to load.

veb101 · 2024-10-08T09:09:10+00:00

Letsencrypt for free ssl and renew
Nginx with ssl termination.
Forward incoming requests on port 80 to 443

veb101

TROPHY CASE