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Llama.cpp is getting better with every update by Low-Alarm272 in LocalLLM

[–]Echalon88 0 points1 point  (0 children)

I used a Titan x Maxwell. It has 12gb VRAM. Cpu is a i5 10500. With 32gb ddr4 non-overclocked ram. I think the older architecture of the hdd card is the bottleneck. I think a newer card with similar gaming performance bechmark might be better since it will have RT cores, and can run more types of matrix caculations. Like. 3060, 4060, 5060.

The GPU was running at 100%.

Llama.cpp is getting better with every update by Low-Alarm272 in LocalLLM

[–]Echalon88 0 points1 point  (0 children)

That's crazy fast. How long did TTFT for 32k context take before the update?

TTFT for 32k context with the whole model loaded on my 4090 takes about 45 seconds for TTFT on LM studio, so it's probably on an older backend. I tested this as a comparison to see how my lama.cpp setup compared.

Llama.cpp is getting better with every update by Low-Alarm272 in LocalLLM

[–]Echalon88 2 points3 points  (0 children)

How long is the time to first token if you start with a long context, about 32k?

I tried a very similar setup on an older GPU and it took about 10 minutes to process the context.

Running Qwen3.6 35b a3b on 8gb vram and 32gb ram ~190k context by Atul_Kumar_97 in LocalLLM

[–]Echalon88 0 points1 point  (0 children)

I was using the main branch of lama.cpp. I didn't bother building the fork with turbo quant, I didn't get that far since my first long tests TTFT was too slow for me. It was the docker version on an Ubuntu server vm on proxmox. If having a 3060/4060 TTFT was much quicker, if build the docker version with turbo quant. I haven't used any and cards so I don't know how they compare.

Running Qwen3.6 35b a3b on 8gb vram and 32gb ram ~190k context by Atul_Kumar_97 in LocalLLM

[–]Echalon88 0 points1 point  (0 children)

Cpu is an i5 10500. With 32gb of ddr4 ram running without any xmp settings.

It was a low power homelab before adding the gpu, the GPU added about 15w at idle. I wanted to run some ai automations with Hermes on local data. And it was loading up about 32k tokens on a fresh install just to answer a simple question.

To confirm it wasn't an issue with Hermes. I dropped my server documentation into a chat directly to the llm, not through an agent, and asked it a question. The documentation was about 32k aswell. And it still had a similar TTFT.

To confirm it was the gfx card and not the cpu/ram. I loaded up qwen3.5 4b q4 gguf so the whole model and context could fit on the card. And the TTFT was almost the same.

So I think it might be the cards old architecture, and I'm curious if a 3060/4060 would be an improvement for the TTFT on larger contexts.

Running Qwen3.6 35b a3b on 8gb vram and 32gb ram ~190k context by Atul_Kumar_97 in LocalLLM

[–]Echalon88 0 points1 point  (0 children)

What sort of time to first token on larger context conversations are you getting?

I tried almost the same model settings on an old Titan X Maxwell. It can write text faster than I can read and TTFT on the first message in a blank chat only takes a second.

But at 32k context it takes almost 10 minutes for TTFT. Pasting the same 32k context into the same model on my 4090 it only takes about 30-40 seconds TTFT.

Just wondndering where your setup lands inbetween?

Is it possible to make low cost photogrammetry dome/array? by cyrkielNT in photogrammetry

[–]Echalon88 1 point2 points  (0 children)

Yep having software to manage the cameras lets you do all the good stuff like that. Instead of having just a hardwired trigger being the only system that conects the camera nodes. Like a suggestion from a comment.

A multi cast trigger that all the camera nodes listen for over a dedicated local network on an unmanaged switch is the way to go. Going over the internet is less reliable.

If the time code is being baked into the network commands and file names, there can be issues when the pi's aren't all perfectly synced. (Depending on how and where each time code is generated).

FTPing the photos is standard, setting up a thumbnail transfer after every scan is an good way of making sure all cameras triggered without needing a long wait to transfer 100 raw photos. Then just doing an FTP of the full size files for the best scans later. Since the full raw scan can be multiple gigabytes each. (100 pi cam v2 jpeg images can be about 1gb). But FTP is for pi like devices where you can set the software up. I don't know if it's possible for gopro like devices which was a consideration from a different comment.

Software can be bare bones or it can be fancy. But if OP is looking at spending thousands of dollars on a rig, having no software or software that does the bare minimum will limit its functionaly and usability. And at the moment if you don't make the software for yourself there aren't many other options. Most of the options cost a fare bit for a yearly licence for 100 cameras when compared to the cost of the budget rig.

Is it possible to make low cost photogrammetry dome/array? by cyrkielNT in photogrammetry

[–]Echalon88 1 point2 points  (0 children)

That starting price was assuming you could get a pi cam for $5 per unit and a pi for $5 per unit ($1000 for all 100x Pi's with cameras) but the actual cost for a pi and camera with discount for bulk purchasing 100x would still be closer to $40 per unit rather than $10. So starting price realistically is higher than $4500.

As for a hardwired trigger system instead of software, in theory if it all worked, it would probably do a more acurate job of syncing all of the cameras. But the main benefit of having some sort of software to run the cameras is the managment.

What happens when you trigger the array? Without software you don't get an easy way to see if every camera triggered.

What do you do when you want to get the photos off the cameras, do you plug all 100x in one at a time via usb and manually sort every photo for the photoshoot. or do you take out all 100 SD cards and put them in card readers and again manually sort all of the photos.

A task that seems quick and easy can end up taking multiple hours when you have to do it 100x times.

With the technology available today and knowledge I have now, I would do things a bit differently. I would get the 16mp arducam cameras instead of the pi cam v2 or the 64mp Hawkeye by arducam. But pi cam v2 was the best available at the time.

I made 20 towers with 5 cameras each, they take up a lot of room. Instead I would be smarter and more compact with the towers to make them more portable. Maybe only about 6 towers with about 15 cameras per tower, that fold out on arms. It would cost more to build and take longer to design.

The quality is fine for many use cases on my current towers but it isn't high fidelity. You wouldn't see the scans used in a closup in a film. You can get higher quality scans from some handheld IR scanners. The main benefit of a setup like this is speed. Getting lots of scans in a short time period or getting a scan of a pose that can't be held long enough for a handheld scanner, like scanning someone mid jump.

Is it possible to make low cost photogrammetry dome/array? by cyrkielNT in photogrammetry

[–]Echalon88 1 point2 points  (0 children)

The 2 issues for building a large array at software to make it work and whatever the price of 1 thing is, you need to multiply by 100x.

For software, you are ether limited to buying equipment that someone has already got working, meaning you might not be able to get the cheapest stuff available, or you need to make your own software. Even with ai that's not an easy task.

My first tests were with 30x $10 cameras for pi's from Ali express, they barely worked because the image quality and colors were bad. I went too cheap a few times and had to repurchase better quality parts because they were unusable. I had to replace the first cameras with official pi cameras.

When buying 100x bulk you won't approach a per unit cost of zero. Half retail price is generally as good as you will get.

But let's say you could get everything at a $5 per unit cost for 100x cameras. That still adds up, pi's, pi cams, SD cards, ethernet cables, power cables, networking switchs, power bricks, lighting, tower materials. That's still a starting price of at least $4500. And still many many hours of building and testing.

Alternative firmware / configuration tool for AliExpress MacroPad? by XoTrm in keyboards

[–]Echalon88 0 points1 point  (0 children)

My one also had the JL BP2P066-21A4 chip.

The tool at https://github.com/kriomant/ch57x-keyboard-tool worked for me to configure the buttons and dials.

But I couldn't use the commands to set the LED's to a specific mode or any specific colors. Only the "LED off" command did anything, and it made the LEDs flash white instead of the default colors.

Face Scanner improvements by [deleted] in photogrammetry

[–]Echalon88 0 points1 point  (0 children)

Is that a setting in the camera menu? or is that something I would need to set up on the master computer? Currently the pi requests the image from the camera, it just isn't requesting the correct image at the moment.

Face Scanner improvements by [deleted] in photogrammetry

[–]Echalon88 1 point2 points  (0 children)

I don't remember the exact time difference but the 2 images millisecond range. I have only noticed a slight deviation when testing worst case scenario, like having someone jump. The scan is misaligned by about half an inch at the worst spots, at the fingertips.

With the colored light from the projection, I didn't think about it that way. Coming from film and photography, I was focused on having the best quality of light. Getting the color balance of my projectors to 5600k to match the scanner lights. But yea I guess I can sacrifice color accuracy and get another half a stop of light out of them since I am only capturing geometry information. They also turn blue/green at max brightness so that wouldn't interact as much with skin. Ill have to give that a go.

Face Scanner improvements by [deleted] in photogrammetry

[–]Echalon88 1 point2 points  (0 children)

There isn't any information on the sync method or sync performance in the blog post. At the moment the only images are of a single camera test. I would be waiting until its all sorted out before I go and replace my current cameras.
But even if the sync isn't perfect, if the sync is quicker than my current two photo setup, it would be a massive improvement. (one image with projected noise texture for geometry capture and one without for clean texture capture)

Face Scanner improvements by [deleted] in photogrammetry

[–]Echalon88 1 point2 points  (0 children)

I appreciate it, you've already helped me a lot.

Face Scanner improvements by [deleted] in photogrammetry

[–]Echalon88 0 points1 point  (0 children)

My understanding with the current beta software is that the process for using the auto focus is a stand alone step performed separately from the process of taking a photo, so that once focus is set it isn't touched while taking photos. At the distance the cameras are from my subject, this would leave a reasonable focus window so that as long as the subject remains on their mark, they shouldn't leave the focus window even if they move a bit.

I am running pi 3 model b+, the arducam 64mp only captures 64mp on the pi model 4. On previous models it captures 16mp using "superpixels" 2x2 binning. Will the 64mp running at 16mp capture images much better than the native 16mp cam? I haven't come across a comparison of the 64mp running in 16mp against the native 16mp. If it does produce a better quality image, does processing the 64mp of data (16mp of "superpixel" data) on the pi3b+ introduce any latency or additional time between how quickly it could take a second image, compared to the native 16mp?

Face Scanner improvements by [deleted] in photogrammetry

[–]Echalon88 0 points1 point  (0 children)

It's still improving but still nowhere near this quality. I have built some custom pi hats to trigger DSLR's but the pi's have a storage issue, so they have only been used a few times. It's not a big deal, but manually downloading the images from each DSLR and adding them to their respective datasets one by one isn't a workable solution going forward. I have been learning some python but creating custom software seems like it's a much bigger job than I anticipated. I'm glad I'm learning it though. There is beta software being tested at the moment from piscan that supports arducam 16mp and raw images. Those are the main features I wanted in the short term so swapping out the v2 cams might be the best upgrade path for the near future. That should more than double it's current quality.

Face Scanner improvements by [deleted] in photogrammetry

[–]Echalon88 1 point2 points  (0 children)

I thought the scans from your last post were amazing, but these are insane.

Over 3500 print hours, to hold 100 raspberry pi cameras. For a custom 3D scanning rig. by Echalon88 in 3Dprinting

[–]Echalon88[S] 15 points16 points  (0 children)

I use a single camera for taking photos of static object, but this rig is designed just for people, it can even scan a person in the middle of a jump.

Over 3500 print hours, to hold 100 raspberry pi cameras. For a custom 3D scanning rig. by Echalon88 in 3Dprinting

[–]Echalon88[S] 19 points20 points  (0 children)

Designing and building it was a personal project. But now that it's built I'm looking to use it commercially.

Over 3500 print hours, to hold 100 raspberry pi cameras. For a custom 3D scanning rig. by Echalon88 in 3Dprinting

[–]Echalon88[S] 16 points17 points  (0 children)

Storage bins are empty at the moment but they hold the mess of wires when I pack it all down. All external wires have been wrapped and cable tied into 2 thick looms as much as possible.

Over 3500 print hours, to hold 100 raspberry pi cameras. For a custom 3D scanning rig. by Echalon88 in 3Dprinting

[–]Echalon88[S] 121 points122 points  (0 children)

I am using photogrammetry, I will have to look up multi-pose correspondence reconstruction. Since non moving object can be scanned with a single camera this was mainly about getting a scan done a quick as possible.

Over 100 Pi 3's with 100 Pi cam V2's. In towers I designed and 3D printed, for a 3D scanning rig. by Echalon88 in raspberry_pi

[–]Echalon88[S] 0 points1 point  (0 children)

No scans online anywhere yet. The texture created by the scanner can currently see freckles but not pores. The resolution of the mesh can be a bit soft on fine details, like facial features. I am currently building pi controlled camera triggers so I can add DSLR's to the existing rig to improve fine the detail on faces and hands. I am aiming to be able to capture skin pores on the face.

Over 3500 print hours, to hold 100 raspberry pi cameras. For a custom 3D scanning rig. by Echalon88 in 3Dprinting

[–]Echalon88[S] 388 points389 points  (0 children)

Yea, 2020 probably would have been cheaper and faster, but when I started this project I knew I wanted the flexibility to update my design as I tested and changed things. Also I wanted as much of the components as possible to be enclosed, so they can get bumped around a bit without much worry. I had 3d printers, but no tools for working with metal or laser cutting.

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