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Found 4 results

  1. Hello Fellow Forum-Goers, Lately I have been somewhat inactive on the forum, and also have not had the opportunity to go fossil hunting in New Jersey since I am at college. But those things do not deter me though. I am here today to tell you about a project I have been doing with fossil classification, specifically classification of some fossil species from the Cretaceous of New Jersey. The goal is to be able to give my computer of a fossil and have it tell me with a certain degree of confidence the probability that it is any one of several New Jersey Cretaceous fossil species. For this project, I began by taking photos of some of my fossils. Here are some examples of the what the photos looked like: Anomoeodus phaseolus Ischyodus bifurcatus Brachyrhizodus wichitaensis The data consisted of around 150 photos, spanning across 6 fossil species. Not represented in the photos above were: Belemnitella americana Enchodus petrosus Ischyrhiza mira To later label this data, I wrote a csv file with labels. From the contents of this file, you can see how many of each specie there were. Note how the common name for these species are used as the labels. id,species IMG_4749,Belemnite-1 IMG_4780,Belemnite-2 IMG_4812,Ray-1 IMG_4813,Ray-2 IMG_4814,Ray-3 IMG_4815,Ray-4 IMG_4816,Ray-5 IMG_4817,Ray-6 IMG_4818,Ray-7 IMG_4819,Ray-8 IMG_4820,Ray-9 IMG_4821,Ray-10 IMG_4822,Ray-11 IMG_4823,Ray-12 IMG_4824,Ray-13 IMG_4825,Ray-14 IMG_4826,Ray-15 IMG_4827,Ray-16 IMG_4828,Ray-17 IMG_4829,Ray-18 IMG_4830,Ray-19 IMG_4831,Ray-20 IMG_4832,Ray-21 IMG_4833,Ray-22 IMG_4834,Ratfish-1 IMG_4835,Ratfish-2 IMG_4836,Ratfish-3 IMG_4837,Ratfish-4 IMG_4838,Ratfish-5 IMG_4839,Ratfish-6 IMG_4840,Ratfish-7 IMG_4841,Ratfish-8 IMG_4842,Ratfish-9 IMG_4843,Ratfish-10 IMG_4844,Ratfish-11 IMG_4845,Ratfish-12 IMG_4846,Ratfish-13 IMG_4847,Ratfish-14 IMG_4848,Ratfish-15 IMG_4849,Ratfish-16 IMG_4850,Ratfish-17 IMG_4851,Ratfish-18 IMG_4852,Ratfish-19 IMG_4853,Ratfish-20 IMG_4854,Ratfish-21 IMG_4855,Ratfish-22 IMG_4856,Ratfish-23 IMG_4857,Ratfish-24 IMG_4858,Ratfish-25 IMG_4859,Ratfish-26 IMG_4860,Ratfish-27 IMG_4861,Ratfish-28 IMG_4862,Ratfish-29 IMG_4863,Ratfish-30 IMG_4864,Ratfish-31 IMG_4865,Ratfish-32 IMG_4866,Ratfish-33 IMG_4867,Ratfish-34 IMG_4868,Enchodus-1 IMG_4869,Enchodus-2 IMG_4870,Enchodus-3 IMG_4871,Enchodus-4 IMG_4872,Enchodus-5 IMG_4873,Enchodus-6 IMG_4875,Enchodus-7 IMG_4876,Enchodus-8 IMG_4877,Enchodus-9 IMG_4878,Enchodus-10 IMG_4879,Enchodus-11 IMG_4888,Enchodus-12 IMG_4889,Enchodus-13 IMG_4890,Enchodus-14 IMG_4891,Enchodus-15 IMG_4892,Enchodus-16 IMG_4903,Pychodont-1 IMG_4905,Pychodont-2 IMG_4906,Pychodont-3 IMG_4907,Pychodont-4 IMG_4908,Pychodont-5 IMG_4909,Pychodont-6 IMG_4910,Pychodont-7 IMG_4911,Pychodont-8 Now, with the labels and data. I began to make a program that fed in the images and then used Keras ( a machine learning library that has the tools for something called a convolutional neural network) in the programming language Python. Here is the beginning of the code: import numpy <- This gets me NumPy, which allows for easy use of vectors and matrices to work with data import collections <- This allows me to make better data structures called "dictionaries" import os <- This allows me to get the path of the image in my computer import imageio <- This allows me to write edited images to other folders from PIL import Image <- This allows me to manipulate the images, in way such as flipping or rotating. from random import shuffle <- This allows me to randomly shuffle the training data. This code puts each fossil image and its label into something like a container together, this "container" is called a dictionary species_dictionary = collections.OrderedDict() our_file = open("fossil_labels.csv","r") file_contents = our_file.read() file_contents = file_contents.split('\n') for iteration in range(1,len(file_contents)): file_contents[iteration] = file_contents[iteration].split(',') species_dictionary[file_contents[iteration][0]] = file_contents[iteration][1] I will skip the other code and now discuss convolutional neural networks, which are used in image classification So, right now I have all the fossil image with their label. The network can now use these to find clusters of pixels in a given photos that correspond to a particular fossil species. Overtime, the network letters that this or that cluster of pixels is common to one a single fossil species. Then, it can recognize that cluster in a new or novel image that it has not been trained on. Here are the convolutional layers: model = Sequential() model.add(Conv2D(32, kernel_size = (3, 3), activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 1))) model.add(MaxPooling2D(pool_size=(2,2))) model.add(BatchNormalization()) model.add(Conv2D(64, kernel_size=(3,3), activation='relu')) model.add(MaxPooling2D(pool_size=(2,2))) model.add(BatchNormalization()) model.add(Conv2D(64, kernel_size=(3,3), activation='relu')) model.add(MaxPooling2D(pool_size=(2,2))) model.add(BatchNormalization()) model.add(Conv2D(96, kernel_size=(3,3), activation='relu')) model.add(MaxPooling2D(pool_size=(2,2))) model.add(BatchNormalization()) model.add(Conv2D(32, kernel_size=(3,3), activation='relu')) model.add(MaxPooling2D(pool_size=(2,2))) model.add(BatchNormalization()) model.add(Dropout(0.2)) model.add(Flatten()) model.add(Dense(128, activation='relu')) model.add(Dense(5, activation = 'softmax')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics = ['accuracy']) model.fit(training_images, training_labels, batch_size = 50, epochs = 10, verbose = 1) I do not expect this code to be fully understood.The network uses weights or sensitivities to different pixel clusters. Then as it learns how its predictions for a photo compares to the actual training photo I gave it, it updates the weights to reflect this. By the end this "error loss" should reach towards 0, and when it does, we know that its predictions correspond very close with the actual photo, now allowing it to classify fossil images for these six species well. If it were training on 10 species it would classify all 10 well. Here is the output of the training: # Epoch 1/10 # 164/164 [==============================] - 497s 3s/step - loss: 0.3975 - acc: 0.8329 # Epoch 2/10 # 164/164 [==============================] - 139s 846ms/step - loss: 0.1026 - acc: 0.9610 # Epoch 3/10 # 164/164 [==============================] - 139s 848ms/step - loss: 0.0427 - acc: 0.9902 # Epoch 4/10 # 164/164 [==============================] - 126s 771ms/step - loss: 0.0232 - acc: 0.9939 # Epoch 5/10 # 164/164 [==============================] - 119s 728ms/step - loss: 0.0153 - acc: 0.9963 # Epoch 6/10 # 164/164 [==============================] - 2258s 14s/step - loss: 0.0066 - acc: 0.9976 # Epoch 7/10 # 164/164 [==============================] - 141s 861ms/step - loss: 0.0047 - acc: 1.0000 # Epoch 8/10 # 164/164 [==============================] - 135s 824ms/step - loss: 0.0048 - acc: 1.0000 # Epoch 9/10 # 164/164 [==============================] - 132s 803ms/step - loss: 0.0027 - acc: 1.0000 # Epoch 10/10 # 164/164 [==============================] - 122s 746ms/step - loss: 0.0043 - acc: 1.0000 You can see that the loss keeps going down with more and more training. For the future, I definitely need to take more photos to get more data and allow it to train on a graphical processing unit (GPU) as opposed to the normal CPU that you use on a laptop. The GPU is better at parallel processing and can train the network in seconds (on my computer it took 15 minutes). Well that is the current state of the project. I still need to do more but thank you for staying here till the end. I hope you have a nice day. -Trevor
  2. Hello everyone, had a super quick trip to the cretaceous creeks of new jersey and found this particularly interesting large bone fragment, likely it is a chunk of miscellaneous bone material but it reminds me alot of a scute like ankylosaurus or some sort of other bone scute especially the edge, or from maybe something like a large turtle but I am entirely not sure if it's dinosaur, marine reptile, etc or if there is anyway to tell, looks super suspicious to me anyways so if anyone has any ideas I'd definitely love to hear them. (If more pictures are needed I will definitely be able to get some more angles if necessary)
  3. Another NJ Brook Unknown

    Hi, I was thinking this was an ammonite fragment or maybe a piece of H. Gabri (or am I way off - again). Then I saw some details that might help with an ID. Any suggestions? Thanks.
  4. Unknown NJ Brook Find

    Hi, Had this item for awhile and still unsure if it's something. (approx - 7/8"). Looks like a concretion one day, and then I see characteristics of something other days. ???? Thanks for the help.
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