A Keras implementation of YOLOv4 (Tensorflow backend)

Last update: Nov 29, 2022

Related tags

Overview

keras-yolo4

请使用更完善的版本:

https://github.com/miemie2013/Keras-YOLOv4

Please visit here for more complete model:

https://github.com/miemie2013/Keras-YOLOv4

Introduction

A Keras implementation of YOLOv4 (Tensorflow backend) inspired by https://github.com/AlexeyAB/darknet.

Frame code from https://github.com/qqwweee/keras-yolo3.

Quick Start

Download YOLOv4 weights from yolov4.weights.
Convert the Darknet YOLOv4 model to a Keras model.
Run YOLOv4 detection.

python convert.py

Running convert.py will get keras yolov4 weight file yolo4_weight.h5.

Usage

Inference

python test.py

Todo

Debug train code with CIoU loss. Loss code from https://github.com/david8862/keras-YOLOv3-model-set.

Comments

Error while converting to keras model

I have trained yolo.weights (with my custom dataset having 3 classes) and the detections are coming fine while testing with that model. But when i am converting it to keras model, I am getting below error

File "convert.py", line 172, in <module>
    yolo4_model = Yolo4(score, iou, anchors_path, classes_path, model_path, weights_path)
  File "convert.py", line 157, in __init__
    self.load_yolo()
  File "convert.py", line 98, in load_yolo
    buffer=weights_file.read(weights_size * 4))
TypeError: buffer is too small for requested array

Could someone tell me why it is happening and what changes I should make

opened by vihari1729 11

any experiences about the training loss ?

thanks for the nice repo.

I am training yolov4 on coco dataset for only the first 7 classes with a split 70:30, any experiences or hints about the training loss when the model seems acceptable?

below is my traing log, seem a long time to train.

Train on 82801 samples, val on 35486 samples, with batch size 32.
Epoch 1/50

   1/2587 [..............................] - ETA: 28:56:58 - loss: 17721.8926
   2/2587 [..............................] - ETA: 16:33:45 - loss: 17243.4922
   3/2587 [..............................] - ETA: 12:32:28 - loss: 16760.1377
   4/2587 [..............................] - ETA: 10:10:04 - loss: 16299.9021
   5/2587 [..............................] - ETA: 9:24:35 - loss: 15857.3045 
   6/2587 [..............................] - ETA: 9:14:57 - loss: 15430.3268
   7/2587 [..............................] - ETA: 9:07:24 - loss: 15008.8902
   8/2587 [..............................] - ETA: 9:01:51 - loss: 14608.8591
   9/2587 [..............................] - ETA: 9:12:25 - loss: 14219.5904
  10/2587 [..............................] - ETA: 9:20:37 - loss: 13848.0055
  11/2587 [..............................] - ETA: 9:18:39 - loss: 13490.1777
  12/2587 [..............................] - ETA: 8:59:26 - loss: 13150.2271
  13/2587 [..............................] - ETA: 8:44:20 - loss: 12822.8066
  14/2587 [..............................] - ETA: 8:34:12 - loss: 12509.4747
  15/2587 [..............................] - ETA: 8:32:09 - loss: 12211.5387
  16/2587 [..............................] - ETA: 8:26:59 - loss: 11925.6479
  17/2587 [..............................] - ETA: 8:20:37 - loss: 11649.0334
  18/2587 [..............................] - ETA: 8:21:20 - loss: 11381.8880
  19/2587 [..............................] - ETA: 8:14:07 - loss: 11124.9040
  20/2587 [..............................] - ETA: 8:11:29 - loss: 10882.4443
  21/2587 [..............................] - ETA: 8:07:56 - loss: 10645.2164
  22/2587 [..............................] - ETA: 8:03:53 - loss: 10420.4502
  23/2587 [..............................] - ETA: 7:58:37 - loss: 10203.8660
  24/2587 [..............................] - ETA: 7:54:08 - loss: 9995.8066 
  25/2587 [..............................] - ETA: 7:50:39 - loss: 9794.8682
  26/2587 [..............................] - ETA: 7:48:39 - loss: 9599.6988
  27/2587 [..............................] - ETA: 7:45:55 - loss: 9411.8161
  28/2587 [..............................] - ETA: 7:42:36 - loss: 9234.4692
  29/2587 [..............................] - ETA: 7:39:51 - loss: 9060.8342
  30/2587 [..............................] - ETA: 7:35:15 - loss: 8894.1920
  31/2587 [..............................] - ETA: 7:33:45 - loss: 8733.4837
  32/2587 [..............................] - ETA: 7:31:51 - loss: 8577.9887
  33/2587 [..............................] - ETA: 7:29:49 - loss: 8425.8984
  34/2587 [..............................] - ETA: 7:27:43 - loss: 8280.7446
  35/2587 [..............................] - ETA: 7:26:30 - loss: 8139.6654
  36/2587 [..............................] - ETA: 7:26:01 - loss: 8004.0953
  37/2587 [..............................] - ETA: 7:26:13 - loss: 7872.3828
  38/2587 [..............................] - ETA: 7:24:52 - loss: 7746.5956
  39/2587 [..............................] - ETA: 7:23:28 - loss: 7624.6465
  40/2587 [..............................] - ETA: 7:29:19 - loss: 7506.7023
  41/2587 [..............................] - ETA: 7:35:16 - loss: 7391.7025
  42/2587 [..............................] - ETA: 7:34:34 - loss: 7280.5936
  43/2587 [..............................] - ETA: 7:31:58 - loss: 7172.5243
  44/2587 [..............................] - ETA: 7:29:56 - loss: 7067.7942
  45/2587 [..............................] - ETA: 7:30:26 - loss: 6965.9564
  46/2587 [..............................] - ETA: 7:31:08 - loss: 6867.1803
  47/2587 [..............................] - ETA: 7:34:20 - loss: 6771.7925

opened by Papageno2 5

"Unknown layer: Mish" while loading converted model from "yolo4_weight.h5"

Source code

from tensorflow import keras
new_model = keras.models.load_model('yolo4_weight.h5')

Error occurred:

Traceback (most recent call last):
  File "/home/source/keras-yolo4/mytest.py", line 13, in <module>
    new_model = keras.models.load_model(h5)
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/saving/save.py", line 146, in load_model
    return hdf5_format.load_model_from_hdf5(filepath, custom_objects, compile)
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/saving/hdf5_format.py", line 212, in load_model_from_hdf5
    custom_objects=custom_objects)
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/saving/model_config.py", line 55, in model_from_config
    return deserialize(config, custom_objects=custom_objects)
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/layers/serialization.py", line 89, in deserialize
    printable_module_name='layer')
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/utils/generic_utils.py", line 192, in deserialize_keras_object
    list(custom_objects.items())))
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py", line 1121, in from_config
    process_layer(layer_data)
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py", line 1105, in process_layer
    layer = deserialize_layer(layer_data, custom_objects=custom_objects)
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/layers/serialization.py", line 89, in deserialize
    printable_module_name='layer')
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/utils/generic_utils.py", line 181, in deserialize_keras_object
    config, module_objects, custom_objects, printable_module_name)
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/utils/generic_utils.py", line 166, in class_and_config_for_serialized_keras_object
    raise ValueError('Unknown ' + printable_module_name + ': ' + class_name)
ValueError: Unknown layer: Mish

opened by shangdibufashi 3

TypeError: buffer is too small for requested array

Loading weights. Weights Header: 1329865020 1348031555 1952981061 [1010723949] Converting 0 Traceback (most recent call last): File "convert.py", line 173, in yolo4_model = Yolo4(score, iou, anchors_path, classes_path, model_path, weights_path) File "convert.py", line 157, in init self.load_yolo() File "convert.py", line 130, in load_yolo buffer=weights_file.read(weights_size * 4)) TypeError: buffer is too small for requested array

opened by saanuregh 3

Your some layers are different from miemie2013's.

in @miemie2013's code

x = layers.ZeroPadding2D(padding=((1, 0), (1, 0)))(x)
x = conv2d_unit(x, i512, 3, strides=2)
s16 = conv2d_unit(x, i256, 1, strides=1)
x = conv2d_unit(x, i256, 1, strides=1)
x = stack_residual_block(x, i256, i256, n=8)
x = conv2d_unit(x, i256, 1, strides=1)
s16 = layers.Concatenate()([x, s16])
x = conv2d_unit(s16, i512, 1, strides=1)

where s16 is the output of Concatenate()

x = layers.UpSampling2D(2)(x)
s16 = conv2d_unit(s16, i256, 1, strides=1, act='leaky')
x = layers.Concatenate()([s16, x])

s16 then computer through conv2d_unit and concatenate with upsampling output x

as for your code:

y38 = DarknetConv2D_BN_Leaky(256, (1,1))(darknet.layers[204].output)
y38 = Concatenate()([y38, y19_upsample])

where darknet.layers[204].output is the output of DarknetConv2D_BN_Mish, not the concatenate layer darknet.layers[201].outputs.

So, I am curious which version is right, or both right?

opened by RuaHU 1

Lambda laye not compatible in tf2+

in tf2+, Lambda layer could not be used anymore, is there any solution to solve the issue especially th error:
ValueError: tf.function-decorated function tried to create variables on non-first call.

opened by robbebluecp 0
Batching with inference

I have converted a custom YOLO4 model to the tensorflow saved_model format. I can successfully use this for single image inference. Now when I feed in a batch of images for client side batching I get the results back in one list without separation of the results into frames so I can't work out which detections go with which frames. Can you provide any info on whether this behavior is expected or how I might remedy it?

opened by tylertroy 0

ValueError: Shapes (1, 1, 1024, 75) and (255, 1024, 1, 1) are incompatible

Traceback (most recent call last):
  File "test.py", line 58, in <module>
    yolo4_model.load_weights(model_path)
  File "/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py", line 492, in load_wrapper
    return load_function(*args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/keras/engine/network.py", line 1230, in load_weights
    f, self.layers, reshape=reshape)
  File "/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py", line 1237, in load_weights_from_hdf5_group
    K.batch_set_value(weight_value_tuples)
  File "/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py", line 2960, in batch_set_value
    tf_keras_backend.batch_set_value(tuples)
  File "/home/mayur/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/backend.py", line 3323, in batch_set_value
    x.assign(np.asarray(value, dtype=dtype(x)))
  File "/home/mayur/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py", line 819, in assign
    self._shape.assert_is_compatible_with(value_tensor.shape)
  File "/home/mayur/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_shape.py", line 1110, in assert_is_compatible_with
    raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (1, 1, 1024, 75) and (255, 1024, 1, 1) are incompatible

opened by mayurmahurkar 5

[BUG] iou computation in preprocess_true_boxes() should not use scaled bbox

in model.py, preprocess_true_boxes(): for i in range(3): anchors_xywh = np.zeros((3, 4)) anchors_xywh[:, 0:2] = np.floor(bbox_xywh_scaled[i, 0:2]).astype(np.int32) + 0.5 anchors_xywh[:, 2:4] = anchors[i] iou_scale = bbox_iou_data(bbox_xywh_scaled[i][np.newaxis, :], anchors_xywh) iou.append(iou_scale)

since anchors_xywh is in original scale, we shouldn't use bbox_xywh instead of bbox_xywh_scaled to compute iou?

opened by maoqingy 0
What do the four parameters after loss mean during training, loss of each category or other else?

Epoch 89/150 loss: [70.428894][6.8589654][49.6575623][13.912364] 1234/1234 [==============================] - 638s 517ms/step - loss: 48.12988.202549][5.05134583][33.3962708][9.7549324]]]]]]] Epoch end eval mAP on weight logs/222/ep089-loss48.130.h5 Epoch 89 mAP 0.2658832599269524 Epoch 90/150 loss: [59.5338707][5.99079227][46.7998619][6.74321651] 1234/1234 [==============================] - 655s 531ms/step - loss: 47.88505.1289482][3.23023152][27.289856][4.60885954]]]]] Epoch end eval mAP on weight logs/222/ep090-loss47.885.h5 Epoch 90 mAP 0.26986168503362695

opened by Kuuuo 0

Owner

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