Train YOLO-NAS - SOTA Object Detection Model - on Custom Dataset

Discover the power of YOLO-NAS, Deci's next-generation object detection model, in this comprehensive guide. We'll walk you through the Python setup, installing YOLO-NAS, running inferences with the pre-trained COCO model, and even training YOLO-NAS on your custom dataset. Dive into the superior real-time object detection capabilities of this game-changing model and learn how to use it to optimize your own AI projects.

Chapters:

00:00 Introduction and Model Overview
01:57 Python Environment Setup and Installing YOLO-NAS
04:56 Inference with pre-trained COCO model
07:21 YOLO-NAS Inference Output Format
09:06 Finding Open-source Datasets
10:19 Training YOLO-NAS on Custom Dataset
16:34 Evaluate Trained Model
17:28 Inference with Trained Model
18:39 Conclusion

Resources:

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#YOLO_NAS #Deci #ObjectDetection #NeuralArchitectureSearch #Python #COCO #MachineLearning #ArtificialIntelligence #CustomDataset #Inference #ModelEvaluation #OpenSource #Datasets

[elbaz_afandy]

error ImportError Traceback (most recent call last)
in <cell line: 1>()
----> 1 from super_gradients.training import models
2
3 model = models.get(MODEL_ARCH,

13 frames
in <module>
36 from . import Image
37 from ._deprecate import deprecate
---> 38 from ._util import is_directory, is_path
39
40

ImportError: cannot import name 'is_directory' from 'PIL._util'

[TheEBcodkings]

please make a video on model selection, its a great idea!

[SinMxchine]

As someone that's just getting into this, I really like how user friendly Roboflow is. Only thing stopping me from continuing to use it unfortunately is the price point per month on top of only allowing 5, 000 images per month with the paid plan. I really wish you all had 4 tiers. Free can stay the same, add the first paid plan for $100 that gives users 4K images, mid tier plan for $200 that gives users 8K images, and top tier plan for $250 that give 10K images + some extra bonuses. This is more competitive imo and would bring more people to the platform over finding cheaper alternatives that allow more freedom to try out multiple new things. I could see you guys being the top dog in this market with how easy setup is and with your YouTube guides which are easy and fast to understand. It's that price point to image ratio that is truly killing me here.

[uslfkvh]

best yolo NAS custom dataset tutorial, ever !

[yashrathod]

This tutorial is awesome I was successfully able to train the model. Also I would like to see a video on model selection too. Thank you so much for such an amazing work. ❤

[liliyalopez]

Clear instructions and a very well-documented Google Colab notebook. Thank you!

[fazlehasan]

Yes we want a video on model selection please make it

[bhooshandeshpande]

Definitely need a video on model selection

[irinahetman]

Hi Piotr!
Greetings from Middle Earth. Yes please, I would like to know more on different models selection depending on use case.

[crusaix]

super good video! very interested in the model selections

[vivaliberte]

Thanks Peter. I just start to try it on my dataset.

[marilynlucas]

Amazing channel. I am enjoying your work a lot. Keep it up

[petrykpyatochkin]

thank you for the video. Would be very interesting in model selection video. Thank you!

[judeloza]

For those of you having issues with the Visualize Inference Result section of the Colab below I have an updated version that should extinguish any errors.

import supervision as sv
import numpy as np
import cv2
import matplotlib.pyplot as plt

# Assuming result.prediction contains the predictions and result.class_names contains the class names
detections = sv.Detections(
xyxy=result.prediction.bboxes_xyxy,
confidence=result.prediction.confidence,

)

# Create BoxAnnotator object
box_annotator = sv.BoxAnnotator()

# Annotate the image
annotated_frame = box_annotator.annotate(
scene=image.copy(),
detections=detections
)

# Generate labels
labels = [
{confidence:0.2f}"
for confidence, class_id in zip(result.prediction.confidence,
]

# Add labels to the annotated frame
for (bbox, label) in zip(result.prediction.bboxes_xyxy, labels):
x_min, y_min, x_max, y_max = bbox
cv2.putText(
annotated_frame,
label,
(int(x_min), int(y_min) - 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.9,
(255, 255, 255),
2
)

# Convert the image from BGR to RGB
annotated_frame_rgb = cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)

# Display the annotated image
%matplotlib inline

plt.figure(figsize=(12, 12))

plt.axis('off')
plt.show()

[irfankobber]

You should make a video on training the model. The entire process till it achieves good result

[FahadRamzan-ricr]

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
super-gradients 3.3.0+master requires pyparsing==2.4.5, but you have pyparsing 2.4.7 which is incompatible.

[sarahhesham]

Also, my dataset images are of size not equal to 640x640, where can I apply this in the coding?

[qryixxm]

is there any need to define TensorBoard ? or it's automatically connected when model is finished ?

[danielarmandovidalsoroa]

For some reason, this instruction does not work anymore:
result = list(model.predict(image, conf=0.35))[0]
----> 4 result = list(model.predict(image, conf=0.35))[0]
TypeError: 'ImageDetectionPrediction' object is not iterable

I did this instead and it worked
result = model.predict(image, conf=0.35)

Perhaps the output format from the model has changed during these 11 months, idk. I'm just leaving this here in case others find the same problem

[shubhamkapadia]

Is there any activation function used in YOLO NAS. If yes, which one and where can I find that information?