Closed. This question needs details or clarity. It is not currently accepting answers.
Want to improve this question? Add details and clarify the problem by editing this post.
Closed 5 days ago.
Improve this question
I'm new to YOLOv8, I just want the model to detect only some classes, not all the 80 classes the model trained on. How can I specify YOLOv8 model to detect only one class? For example only person.
from ultralytics import YOLO
model = YOLO('YOLOv8m.pt')
I remember we can do this with YOLOv5, but I couldn't do same with YOLOv8:
model = torch.hub.load("ultralytics/yolov5", 'custom', path='yolov5s.pt')
model.classes = [0] # Only person
model.conf = 0.6
Just specify classes in predict with the class IDs you want to predict
from ultralytics.yolo.engine.model import YOLO
model = YOLO("yolov8n.pt")
model.predict(source="0", show=True, stream=True, classes=0)
for i, (result) in enumerate(results):
print('Do something with class 0')
Related
Closed. This question needs details or clarity. It is not currently accepting answers.
Want to improve this question? Add details and clarify the problem by editing this post.
Closed 2 years ago.
Improve this question
I have an excel file with 1000 different values, I am trying to train my artificial intelligence with these values. While the Test Size is 0.33, artificial intelligence should be trained with 670 values, but only 21 values are trained. What is the source of the problem?
You probably mean a number of batches trained using fit. Every batch comprises 32 items by default
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 2 years ago.
Improve this question
In a data frame with 1000 texts, after doing preprocessing lemmatization, how can I find out how many words have been lemmatized in each text?
Why did you run your model for just 3 epochs? I would suggest you to run it for about 20 epochs, and then see if the validation accuracy is not reducing. And the thing, I can tell you is that You need to change your this line of code:
model.add(Embedding(300000,60,input_length=300))
To this:
model.add(Embedding(k, 60,input_length=300))
Where you can set k as 256 or 512 or a number close to them. But 300000 would be just too much. By that, your network would focus more on the embedding layer, when the main job is of encoder and decoder.
Another thing, you should increase your LSTM units (maybe to a number like 128 or 256) in both encoder and decoder, and remove the recurrent_dropout parameter (since, you are dropping out using the dropout layer after encoder). If that still doesn't help then you can even add Batch Normalization layers to your model.
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 2 years ago.
Improve this question
Is there an easy native way to implement tfa.optimizers.CyclicalLearningRate w/ QNetwork on DqnAgent?
Trying to avoid writing my own DqnAgent.
I guess the better question might be, what is a proper way to implement callbacks on DqnAgent?
From the tutorial you linked, the part where they set the optimizer is
optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)
train_step_counter = tf.Variable(0)
agent = dqn_agent.DqnAgent(
train_env.time_step_spec(),
train_env.action_spec(),
q_network=q_net,
optimizer=optimizer,
td_errors_loss_fn=common.element_wise_squared_loss,
train_step_counter=train_step_counter)
agent.initialize()
So you can replace optimizer with whatever optimizer you would rather use. Based on the documentation something like
optimizer = tf.keras.optimizers.Adam(learning_rate=tfa.optimizers.CyclicalLearningRate)
should work, barring any potential compatibility issues coming from that they are using the tf 1.0 adam in the tutorial.
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 2 years ago.
Improve this question
I have seen it on Shutterstock or on many websites. if you upload an image with automatically generate suggested tags.
That's commonly done using (Deep) Artificial Neural Networks (NNs).
The idea is that you feed an image into a trained NN model and it will predict a classification of the entire image, detect objects present in the image, or even label regions inside the image. There's a lot of freedom in what can be achieved. Since good models are not easy to obtain (without large amounts of data and intense training resources), there exist pretrained models that can be finetuned by the user in order to make it work on your own particular dataset (unfortunately, these models are often somewhat overfit to the dataset they have been trained on such that finetuning is necessary most of the time). I think this link will point you further into the direction how these automatically suggested tags can be generated.
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 5 years ago.
Improve this question
I have to questions regarding keras and Convolutional Neural Networks:
1.
How to create bounding boxes using keras and convolution neural network ?
2.
How to use keras vgg16 application retraining?