The best motivation to quit Image Classification... x_x

Other people in 2019:
Realtime image classification!

Me in 2019:
22075ms to find all occurrences of a string of the screen

The next step for improving large language models (if not diffusion) is hot-encoding.

The idea is pretty straightforward:
Generate many prompts, or take many prompts as a training and validation set. Do partial inference, and find the intersection of best overall performance with least computation.

Then save the state of the network during partial inference, and use that for all subsequent inferences. Sort of like LoRa, but for inference, instead of fine-tuning.

Inference, after-all, is what matters. And there has to be some subset of prompt-based initializations of a network, that perform, regardless of the prompt, (generally) as well as a full inference step.

Likewise with diffusion, there likely exists some priors (based on the training data) that speed up reconstruction or lower the network loss, allowing us to substitute a 'snapshot' that has the correct distribution, without necessarily performing a full generation.

Another idea I had was 'semantic centering' instead of regional image labelling. The idea is to find some patch of an object within an image, and ask, for all such patches that belong to an object, what best describes the object? if it were a dog, what patch of the image is "most dog-like" etc. I could see it as being much closer to how the human brain quickly identifies objects by short-cuts. The size of such patches could be adjusted to minimize the cross-entropy of classification relative to the tested size of each patch (pixel-sized patches for example might lead to too high a training loss). Of course it might allow us to do a scattershot 'at a glance' type lookup of potential image contents, even if you get multiple categories for a single pixel, it greatly narrows the total span of categories you need to do subsequent searches for.

In other news I'm starting a new ML blackbook for various ideas. Old one is mostly outdated now, and I think I scanned it (and since buried it somewhere amongst my ten thousand other files like a digital hoarder) and lost it.

I have some other 'low-hanging fruit' type ideas for improving existing and emerging models but I'll save those for another time.

Finally, after days of research and failures, I managed to understand and tweak TensorFlow's program for image classification.

The feel of power, arcane knowledge and fascination is just incredible.

Might not seem much these days, nobody was interested in it. But I, deep inside, knew: I was proud of myself.

FUCK. YOU. AYLIEN.

- For your shitty hashtag generator, that generated #FCBarcelona for a game review
- For your shitty classification endpoint
- For your shitty sentiment analysis that only works in the demo
- For your shitty image tagging that clarifai is way better at
- And for your "semantic labeling" that doesn't work

FUCK YOUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUU

Used pip install to integrate tensorflow with python 3.5 on windows 10 machine but there were no models available in it. Had to download that separately and add it to tensorflow. Then tried using both inception and clasify_image.py but gives name error cannot find core. But when tested on python idle there were no error there. I don't want to custom create my own classifier but retrain the model. Any solution people?

Deep learning. Working on an image classification problem for a big company. The "boss" ask me to teach an AI to classify images into a few classes.

"Mmm, ok...I just need to create the dataset and then build the AI...so.."

Where is the problem??

The problem is that the classes are so perfectly similar that no one knows how to help me create the dataset and I have to do it alone.

That's how you spend your weeks in a loop where you look at thousands of images over and over just to have something decent start your work.

After that I felt like...
"I'm the hero they deserves, but not the one they need right now" - Cit

Make your code available for your team members, please.

So we're working on this robotics project using ROS, a framework that enables multiple nodes in a network exchange their functionality among each other through tcp connections. Each node can be implemented and executed on your own machine, and tested with dummy inputs, but in collaboration they make a robot do fancy stuff.

The knowledgebase needs data from the image processing unit, providing this data to others with semantic context to high level planning, which uses this semantic data for decision making and calling the robot manipulation node with meaningful input, to navigate the robot's components in the environment. We use a dedicated machine, which pulls the corresponding repositories and is always kept configured correctly, to run each node, such that everybody has access to each other's work when needed.

So far so good. We tried to convince the manipulation guy (let's call him John) to run his code on our central machine, not a week, but since the first day, 5 months ago. Our cluster classification has been unavailable for 2 months, but my collegue fixed that. We still can't run the whole project without John's computer. If his machine blows up we're fucked.

Each milestone feels like a big-bang-test, fixing issues in interfaces last-minute. We see the whole demo just moments before our supervisors arrive at the door.

I just hope he doesn't get hit by a truck.

I'm looking for a image segmentation and classification web based tool to create ground truth for my dataset in next Deep Learning project, what tool do You use?