Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 18 September 2026 and assigned it the closest of 31 fixed categories, at 97% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
Growth
12 measurements spanning 42 days, net +8. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 1,300–1,325 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
17 Sept 2026, 20:16
1,314
-2
10 Sept 2026, 10:37
1,316
-3
1 Sept 2026, 13:26
1,319
-2
29 Aug 2026, 19:49
1,321
+2
26 Aug 2026, 18:11
1,319
-2
24 Aug 2026, 01:06
1,321
-1
20 Aug 2026, 10:14
1,322
+3
17 Aug 2026, 11:46
1,319
+4
14 Aug 2026, 03:28
1,315
+12
10 Aug 2026, 12:38
1,303
-3
7 Aug 2026, 04:57
1,306
no change
6 Aug 2026, 16:39
1,306
first reading
Engagement
20 posts held, back to 17 July 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 page of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 5 August 2026. An engagement rate over an empty window would be a number about nothing.
Reaction mix
74 reactions across 18 posts, in 4 distinct kinds. The most used accounts for 64.9% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
48
64.9%
🤩
11
14.9%
😍
10
13.5%
🔥
5
6.76%
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 18 of the 20 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 74 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 17 July 2026 to 5 August 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
پاسخ اکثر ما به سوالی که در پست قبل مطرح شد احتمالاً «افزایش دقت مدل» هست؛ چون سالهاست معیار موفقیت مدلهای هوش مصنوعی رو با اعدادی مثل Accuracy، Dice Score، mAP یا IoU میسنجیم...
اما اجازه بدین یک سناریوی واقعی رو تصور کنیم ؛
فرض کنین یک مدل تشخیص تومور با دقت ۹۹٪ آموزش دادین . حالا قراره که این مدل بهصورت ۲۴ ساعته در یک بیمارستان به پزشکان خدمت کنه.
روز اول همه چیز عالیه.
اما چند هفته بعد...
سرور بهروزرسانی م…
اگر قصد داشته باشیم بیشتر در ادامه ی پست قبل صحبت کنیم اول بنظرم باید به سوال زیر پاسخ بدیم ؛
اگر امروز قرار باشه یک محصول بینایی ماشین برای استفاده ۲۴ ساعته در یک کارخانه یا بیمارستان طراحی کنیم ، بیشتر زمان خودمون رو صرف افزایش دقت مدل کنیم یا ساخت یک زیرساخت پایدار برای استقرار و نگهداری از اون مدل ؟
⚠️ چرا بعضی از پروژههای پردازش تصویر در مرحله تولید (Production) شکست میخورند؟
فرض کنیم دوتا تیم روی یک پروژه تشخیص عیب قطعات صنعتی کار میکنند.
هر دو تیم از یک مدل یکسان (مثلاً YOLO)، یک دیتاست مشابه و حتی یک GPU مشابه استفاده میکنند.
اما بعد از چند ماه، محصول یکی از تیمها در کارخانه بدون مشکل کار میکنه، در حالی که محصول تیم دیگر دائماً با خطا، افت دقت و قطعی سرویس مواجه است.
چه اتفاقی افتاده؟
بیشتر افراد اول…
ممنون از همه کسانی که در نظرسنجی شرکت کردند❤
تقریبا همه موارد درسته و جالب بود که پاسخها بین دقت مدل، کیفیت دادهها، زیرساخت DevOps و Docker تقسیم شده بود. حقیقت اینه که هیچکدام از این گزینهها بهتنهایی ضامن موفقیت یک محصول بینایی ماشین نیستند ؛ اما یک نکته وجود داره که باید بهش توجه کنیم...
اگر گزینه اول رو انتخاب کردید، تنها نیستید؛ بیشتر متخصصین همین پاسخ رو دارن. اما تجربه نشون میده در محصولات واقعی مبتنی بر…
🎯 به نظر شما مهمترین عامل موفقیت یک محصول بینایی ماشین چیست؟
۱. کیفیت دادهها62%
۲. زیرساخت DevOps و Docker16%
۳. سختافزار مناسب20%
۴. دقت بالای مدل (Accuracy)30%
The shares total 128%, above 100: this poll accepts more than one answer per voter. No per-option vote count is published, so the number of voters who chose each option is not derivable and is not shown.
🆕️ ارائه برنامه های درمانی و تشخیص های هوشمند پزشکی با Glass Health
۱. تحلیل دادههای پزشکی: Glass Health به پزشکان این امکان را میدهد که دادههای بزرگ پزشکی را به سرعت تحلیل کرده و الگوهای مهم را شناسایی کنند. این تحلیلها میتوانند به تشخیص زودهنگام بیماریها کمک کنند.
۲. پیشبینی نتایج درمان: با استفاده از الگوریتمهای پیشرفته یادگیری ماشین، این پلتفرم میتواند نتایج درمانی را پیشبینی کند و به پزشکان در انتخاب…
🆕️ معرفی #پروژه GitHub Trend | Croc
اگر تاکنون تجربه انتقال فایل بین دو کامپیوتر را با چالشهایی مانند ایجاد لینک، آپلود به فضای ابری یا تنظیمات شبکه داشتهاید، Croc میتواند یک راهحل ساده و کارآمد برای شما باشد. این ابزار متنباز به شما این امکان را میدهد که تنها با اجرای یک دستور، فایلها و پوشهها را بهصورت مستقیم و با امنیت بالا بین دو دستگاه منتقل کنید؛ حتی اگر هر دو دستگاه پشت NAT یا فایروال قرار داشته باشن…
🆕️ فناوری HoME به حل معمای پردازش تصاویر سهبعدی پزشکی کمک میکند..
تقسیمبندی دقیق تصاویر سهبعدی پزشکی (مثل CT- Scan یا MRI ) یکی از چالشهای بزرگ هوش مصنوعی در پزشکی است. هر تصویر حجم عظیمی از اطلاعات دارد و تنوع دستگاهها و کیفیتهای تصویر هم کار را سختتر میکند.
روش جدیدی به نام HoME مثل یک تیم متخصص عمل میکند:
مرحله ۱: تصویر به بخشهای کوچکتر تقسیم میشود و هر بخش به یک «متخصص محلی» سپرده میشود تا ویژگ…
#پروژه 3D UNet segmentation طحال با استفاده از Monai
در این پروژه ، مدل 3D UNet برای segment کردن طحال از تصاویر CT-SCAN سهبعدی با استفاده از فریمورک MONAI پیادهسازی شد. دیتاست شامل ۶۱ حجم سهبعدی است و چالش اصلی آن اندازه کوچک ناحیه هدف است.
مدل با ۵ سطح و residual blocks طراحی شده و از DiceCELoss برای بهینهسازی استفاده میکند. پیشپردازش دادهها با ترنسفورمهای MONAI انجام شد و از CacheDataset برای تسریع آموز…
📚50 سوال ماشین لرنینگ، علم داده و پایتون
📍 سوالهای عملی، نه صرفاً تئوری
+Ai_Tv
🆔️ intellimage channel:
https://t.me/Intellimage
https://eitaa.com/intellimage
❤2🤩1😍1
Showing the 12 most recent of 20 posts we hold for @Intellimage. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Polls
The poll we hold for this entry, as Telegram rendered it when we read the post. A poll’s figures keep moving after that, so each one is dated.
🎯 به نظر شما مهمترین عامل موفقیت یک محصول بینایی ماشین چیست؟
۱. کیفیت دادهها62%
۲. زیرساخت DevOps و Docker16%
۳. سختافزار مناسب20%
۴. دقت بالای مدل (Accuracy)30%
The shares total 128%, above 100: this poll accepts more than one answer per voter. No per-option vote count is published, so the number of voters who chose each option is not derivable and is not shown.
Percentages only — there are no per-option vote counts here, because Telegram publishes none. The public post preview gives each option’s share and a single voter total, and nothing else. Multiplying one by the other would produce a per-option tally that looks measured and is not: the shares are rounded to whole numbers before we ever see them. We print what was published and leave the column that does not exist empty.
The shares need not add up to 100. Rounding alone puts many polls at 99 or 101. A poll that allows more than one answer per voter runs well past 100 by design, and several here do. The bars are drawn against a fixed 100% track at each option’s own percentage rather than normalised to the total, so a poll that exceeds it shows that it does instead of being quietly rescaled.
Read from the 20 most recent posts we hold, published 17 July 2026 to 5 August 2026. Telegram labels each poll by kind — an anonymous poll, a quiz, a closed set of final results — and that label is reproduced rather than paraphrased.
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Mentions
Named by 3 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
Appears in Telegram’s recommendations for other channels
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
استایل ایرونی | Style 🧚♀️ @styleiranii · 47,893 Telegram ranks this channel #54 of 58 here — alongside 57 others — read 26 August 2026
This channel appears in 1 seed channel's Telegram-generated recommendation list in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
Cite this entry
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 17 September 2026 — this
entry's latest reading, not the date you are reading this.
“پردازش تصویر هوشمندIntellimage” (@Intellimage), 1,314 subscribers as measured 17 September 2026. Telegram Register, tgregister.com/channel/Intellimage.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.