The paradox of "too old" / "too young" to get a job in a new/another field because of lack of experience.

Here is a trick to overcome that archaic rule:

⭐ Set aside the Impostor Syndrome, and focus on your learning and sharing

... Image
📌 Join a friendly/helpful community (e.g., @fastdotai, IceVision, etc.)

⭐ Bond with some members, and build a genuine friendship: Be authentically genuine and thoughtful. People will recognize that.

📌 Work on mini-projects that would interest companies you want to join
⭐ Share those projects on GitHub without fear: Nobody will judge you. People are too busy with their own sh*t

📌 Share your progress with your community (ies), and ask for feedback (community and/or friends)
⭐ Share with them what you are learning

📌 Write blog posts about your work, and share them on Social Media
🚀 There you have it: You would have built an attractive portfolio, and the experience your target companies are looking for.

💪 Whatever the outcome would be, you would have learned a lot and have the proof you know what you are talking about.
⭐️If you are interested in mastering Object Detection (OD), follow @ai_fast_track, and get highly curated content in your feed.

⭐️ Here is an example of my 20 visual summaries about OD modeling and training

• • •

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More from @ai_fast_track

9 Dec
Interested in parsing any custom object detection dataset format?

I will show you how easy to parse your dataset, and train one of many IceVision models using your own data.

In this post, I walk you through the steps to parse data stored in CSV files by using the Chess dataset Image
You can apply the same logic to any other format.

• First, both COCO and VOC formats are transparently supported in IceVision.

• The CSV format is different than the COCO and VOC formats. Hence, the custom parsing.

• IceVision auto-generates your parser class skeleton.
• You only need to connect some attributes to their corresponding ones in your CSV file

• Once done, you instantiate your class, and parse your data

• From there, all the subsequent code is common to any other parsed data.

• Parsed data are a collection of record objects
Read 8 tweets
6 Dec
🎉Part 2- Summary of 10 summaries on:

Tips & Trick & Best Practices in training (not only) object detection models.

Don't miss any of those posts, follow @ai_fast_track to catch them in your feed.

🎁 Summary of summaries: ...
1- Training Object Detection Models Tips & Tricks

2- Pro Tip to fast track your object detection training

Read 12 tweets
5 Dec
Interested in testing several object detection models in one single notebook, in @kaggle Competitions?

Here is a getting started notebook where you could test 5 different models: VFNet, EfficientDet, RetinaNet, Faster-RCNN, and YOLOv5.

🎁 Kaggle NB: kaggle.com/faridone/icevi…
The kaggle notebook is a stripped one. Check out the long version in Colab.

🎁 Colab NB: airctic.com/0.11.0/getting…

That way, you have access to 2 platforms where you get familiar with IceVision
We have a #kaggle dedicated channel in our IceVision forum where kagglers share their knowledge.

• IceVision Forum: discord.gg/JDBeZYK

• IceVision Repo: github.com/airctic/IceVis…
Read 4 tweets
4 Dec
💡 Prop Tip to 🚀 fast track your object detection training

All @fastdotai users use this method for free!

It's fine_tune()

We noticed an important increase in the COCO Metric when we train object detection models using this method even with the simple default values
Fine tuning has 2 phases:

🧊 Phase 1: Freeze your model's backbone and neck. Train your model for a couple of epochs (e.g. 1 to 5)

📈 Phase 2: Unfreeze the whole model. Train for higher number of periods (depending on your dataset, and how hard/easy your data is)
Phase 1 is a kind of warm-up for your model head.

Here is an example showing how to call the fine_tune() method.

In this case, we freeze both the backbone and the neck for 1 epoch in phase 1, and train for 20 epochs in phase 2.
Read 5 tweets
2 Dec
Detecting small object detection is one of the most challenging tasks in object detection.

❓ Want to know an effective way to overcome that❓

Check out the SAHI integration in IceVision🧊
👇
All IceVision models transparently support SAHI (MMDetection, EfficientDet, Torchvision, and YOLOv5). The results are quite impressive!

SAHI is a very cool library that drastically improves detection of small objects.
📌 It is simply used at inference time

📌 It leverages high resolution images.

📌 Your training process remains the same as usual.

🔎 At the high level, this is how SAHI works:

1- It takes a high resolution (e.g.1920 x 1024 px), and divide it in patches (e.g. 128 x 128 px)
Read 7 tweets
30 Nov
🎉🎊🥳Day 30/30: Labeling data and training object detection models are time consuming and expensive.

Here is a Survey of Self-Supervised and Few-Shot Object Detection (FSOD). Those technique aim at alleviating those issues.
The authors have categorized, reviewed, and compared several few-shot and self-supervised object detection methods

📌 FSOD is about training a model on novel (unseen) object classes with little data. It still requires prior training on many labeled data of base (seen) classes
📌 Self-Supervised Learning (SSL) methods aim at learning representations from unlabeled data which transfer well to downstream tasks such as object detection

📌 Combining few-shot and self-supervised object detection is a promising research direction
Read 8 tweets

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