Tweet

@strickvl

@ai_fast_track

More from @ai_fast_track

AI Fast Track (45/45)

@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

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

AI Fast Track (45/45)

@ai_fast_track

8 Dec

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

...

@fastdotai

📌 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)

Read 6 tweets

AI Fast Track (45/45)

@ai_fast_track

6 Dec

@ai_fast_track

🎉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: ...

https://twitter.com/ai_fast_track/status/1458493806362378244

1- Training Object Detection Models Tips & Tricks

https://twitter.com/ai_fast_track/status/1458493806362378244

https://twitter.com/ai_fast_track/status/1466998473312677894

2- Pro Tip to fast track your object detection training

https://twitter.com/ai_fast_track/status/1466998473312677894

Read 12 tweets

AI Fast Track (45/45)

@ai_fast_track

5 Dec

@kaggle

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

AI Fast Track (45/45)

@ai_fast_track

4 Dec

@fastdotai

💡 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

AI Fast Track (45/45)

@ai_fast_track

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

Share this page!

AI Fast Track (45/45)

Try unrolling a thread yourself!

More from @ai_fast_track

AI Fast Track (45/45)

AI Fast Track (45/45)

AI Fast Track (45/45)

AI Fast Track (45/45)

AI Fast Track (45/45)

AI Fast Track (45/45)

Did Thread Reader help you today?

Like this author's thread?