ValueError: cannot reshape array of size 1091 into shape (85)
Explanation of the problem
Upon cloning the repository and attempting to run the provided demo, an error occurred, resulting in a traceback. The error message suggests that the issue lies within the
utils.py file at line 191. Specifically, a
ValueError is raised when attempting to reshape an array. The array, which has a size of 1091, is being reshaped into the shape (85), causing the error. This error disrupts the execution of the demo and prevents it from running successfully.
To set up the demo, the repository was cloned, and the necessary files, namely
yolov3.weights, were downloaded. Following that, a series of commands were executed to prepare the environment and run the demo. The
convert_weights.py script was used to convert the weights into the desired format, using the
--data_format NHWC flag. Subsequently, the
convert_weights_pb.py script was run to generate a frozen model with the same data format specification. Finally, the
demo.py script was executed, providing input and output image paths, the frozen model file path, and specifying the data format as NHWC. However, the demo script encountered the aforementioned error, leading to the suspicion that the issue might be related to the data format used (NHWC) instead of NCHW.
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Problem solution for ValueError: cannot reshape array of size 1091 into shape (85)
One proposed solution involves addressing the error by finding all lines in the code that only contain zeros. Instead of using
np.nonzero, an alternative approach is suggested. By calculating the sum of each line using
np.sum and identifying the indexes where the sum is not equal to zero, a new array is created. The provided code snippet demonstrates this process, where the
temp variable holds the
image_pred array, and
sum_t stores the sum of each line. The
non_zero_idx variable captures the indexes where the sum is not equal to zero. Finally, the
image_pred array is updated by selecting only the rows corresponding to these non-zero indexes. This approach aims to resolve the value error and allow the demo to run successfully.
temp = image_pred sum_t = np.sum(temp, axis=1) non_zero_idx = sum_t != 0 image_pred = image_pred[non_zero_idx, :]
Another solution involves modifying the
utils.py file. Within the code, a loop iterates over the predictions, and the shape of
image_pred is determined. Previously, the
np.nonzero function was used to find the non-zero elements in
image_pred, but it caused the value error. The suggested modification replaces this line with the calculation and filtering based on the sum of each line. The
image_pred array is reshaped to match the specified shape and ensure compatibility. By applying this modification, it is expected that the error encountered during the demo execution can be resolved.
for i, image_pred in enumerate(predictions): shape = image_pred.shape temp = image_pred sum_t = np.sum(temp, axis=1) non_zero_idx = sum_t != 0 image_pred = image_pred[non_zero_idx, :] image_pred = image_pred.reshape(-1, shape[-1])
Other popular problems with hunglc007 tensorflow-yolov4-tflite
Problem: TensorFlow Lite Interpreter Compatibility Issues
The TensorFlow Lite interpreter may not be compatible with the latest version of the YOLOv4 model, causing errors and crashes when running the model.
It is recommended to update to the latest version of the TensorFlow Lite interpreter to ensure compatibility. If that doesn’t work, try downgrading the version of the YOLOv4 model to an earlier version that is compatible with the TensorFlow Lite interpreter.
Problem: Input Shape Mismatch Error
An input shape mismatch error occurs when the shape of the input image passed to the YOLOv4 model does not match the expected shape defined in the model’s configuration file.
Verify that the input image shape matches the expected shape defined in the model’s configuration file, and resize the image if necessary. Also, make sure that the input image is in the correct format, such as RGB or BGR.
Problem: Performance Degradation with Large Model Sizes
The YOLOv4 model may experience performance degradation when the model size is too large, causing slow inference times and high memory usage.
To improve performance, try optimizing the model by reducing the number of parameters and layers, or by using pruning techniques. Another solution is to run the model on hardware that is optimized for deep learning, such as GPUs or TPUs
A brief introduction to hunglc007 tensorflow-yolov4-tflite
hunglc007 tensorflow-yolov4-tflite is an open-source project that implements YOLOv4, a state-of-the-art object detection model, using TensorFlow and TensorFlow Lite. YOLOv4 is a deep learning-based model that can detect objects in real-time with high accuracy. The project is designed to be easy to use, with a focus on fast and efficient object detection for a wide range of use cases, such as security and surveillance, self-driving cars, and image processing.
The implementation of YOLOv4 in hunglc007 tensorflow-yolov4-tflite is optimized for deployment on mobile devices, with the use of TensorFlow Lite. TensorFlow Lite is a lightweight and efficient framework for running deep learning models on resource-constrained devices, such as smartphones and Raspberry Pis. This optimization allows for the deployment of the object detection model in real-time, even on devices with limited computational resources. The project also includes pre-trained weights and example code for object detection, making it easy for developers to get started with object detection using YOLOv4 and TensorFlow Lite.
Most popular use cases for hunglc007 tensorflow-yolov4-tflite
- Object Detection in Real-time: hunglc007 tensorflow-yolov4-tflite can be used to detect objects in real-time using a webcam or video feed. The model can be integrated into a computer vision pipeline, allowing for the detection of objects in real-time as the video is being captured.
import cv2 import numpy as np # Load the TensorFlow Lite model interpreter = tf.lite.Interpreter(model_path="yolov4.tflite") interpreter.allocate_tensors() # Get input and output tensors input_details = interpreter.get_input_details() output_details = interpreter.get_output_details() # Open the video capture cap = cv2.VideoCapture(0) while True: # Capture a frame from the video ret, frame = cap.read() # Preprocess the frame for input to the TensorFlow Lite model frame = cv2.resize(frame, (416, 416)) frame = np.expand_dims(frame, axis=0) # Run the TensorFlow Lite model interpreter.set_tensor(input_details['index'], frame) interpreter.invoke() # Get the results from the model boxes = interpreter.get_tensor(output_details['index']) classes = interpreter.get_tensor(output_details['index']) scores = interpreter.get_tensor(output_details['index']) # Draw the bounding boxes on the frame for i in range(boxes.shape): if scores[0, i] > 0.5: x1, y1, x2, y2 = boxes[0, i] cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2) # Show the frame cv2.imshow("Object Detection", frame) if cv2.waitKey(1) & 0xFF == ord('q'): break # Release the video capture cap.release() cv2.destroyAllWindows()
- Image Classification and Segmentation: In addition to object detection, hunglc007 tensorflow-yolov4-tflite can also be used for image classification and segmentation tasks. The model can be fine-tuned on a specific task or dataset, allowing for highly accurate results in these areas.
- Autonomous Systems: hunglc__ tensorflow-yolov4-tflite can be used in autonomous systems, such as self-driving cars, to detect objects in the environment and make decisions based on that information. The ability to run object detection in real-time on resource-constrained devices makes it an ideal choice for deployment in these types of systems.
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