add models

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +1,12 @@
 ---
 title: BandiCount
-emoji: 📚
-colorFrom: red
-colorTo: red
+emoji: 🐨
 sdk: gradio
 sdk_version: 3.0.5
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
+# BandiCount
+
+State of the art object-detection model for detecting Australian native animal species in NSW national parks.

app.py

@@ -0,0 +1,28 @@
+import gradio as gr
+import torch
+import torchvision
+import numpy as np
+from PIL import Image
+
+# Load model weights
+model = torch.hub.load('ultralytics/yolov5', 'custom', "model_weights/datasets_1000_41class.pt")
+
+# Define a yolo prediction function
+def yolo(im, size=640):
+    g = (size / max(im.size))  # gain
+    im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS)  # resize
+
+    results = model(im)  # inference
+    results.render()  # updates results.imgs with boxes and labels
+    return Image.fromarray(results.imgs[0])
+
+
+inputs = gr.inputs.Image(type='pil', label="Original Image")
+outputs = gr.outputs.Image(type="pil", label="Output Image")
+
+title = "BandiCount: Detecting Australian native animal species"
+description = "BandiCount: Detecting Australian native animal species in NSW national parks, using object detection. Upload an image or click an example image to use."
+article = ""
+
+examples = [['BrushtailPossum.jpg'], ['Eagle.jpg'], ['Macropod.jpg'], ['cat.jpg'], ['echidna.gif'], ['fox_in_snow.mp4'], ['godzilla_fantail.png'], ['ibis.jpg'], ['koala1.jpeg'], ['koala2.jpg'], ['lyrebird.mp4']]
+gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, theme="huggingface").launch(cache_examples=True,enable_queue=True)

content/README.md

@@ -0,0 +1,3 @@
+# Content
+
+Figures and pictures for the documentation go here.

data/README.md

@@ -0,0 +1,36 @@
+# Data
+
+Data goes here. However, I don't own the data, so I am not going to stick it on github.
+
+Instead, let's go get the data!
+
+Download it from [kaggle](https://www.kaggle.com/datasets/aditya276/face-mask-dataset-yolo-format)
+
+Then, in terminal:
+
+```bash
+unzip archive.zip 
+```
+
+Next, we will need to make sure the files are ordered in the way that YOLO likes.
+
+```bash
+mkdir images/test/labels
+mkdir images/train/labels
+mkdir images/valid/labels
+mkdir images/test/images
+mkdir images/train/images
+mkdir images/valid/images
+
+mv images/test/*.txt images/test/labels
+mv images/train/*.txt images/train/labels
+mv images/valid/*.txt images/valid/labels
+
+mv images/test/* images/test/images
+mv images/train/* images/train/images
+mv images/valid/* images/validimages
+```
+
+## Another face mask dataset
+
+[This one looks better than kaggle](https://mvrigkas.github.io/FaceMaskDataset/)

data/fox_in_snow.mp4

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+size 5656379

data/lyrebird.mp4

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model_weights/72class_yolov5l.pt

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model_weights/datasets_1000_41class.pt

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+size 57647815

model_weights/datasets_150_72class.pt

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requirements.txt

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+
+   
+# pip install -r requirements.txt
+
+# Base ----------------------------------------
+matplotlib>=3.2.2
+numpy>=1.18.5
+opencv-python>=4.1.1
+Pillow>=7.1.2
+PyYAML>=5.3.1
+requests>=2.23.0
+scipy>=1.4.1  # Google Colab version
+torch>=1.7.0
+torchvision>=0.8.1
+tqdm>=4.41.0
+
+# Logging -------------------------------------
+tensorboard>=2.4.1
+# wandb
+
+# Plotting ------------------------------------
+pandas>=1.1.4
+seaborn>=0.11.0
+
+# Export --------------------------------------
+# coremltools>=4.1  # CoreML export
+# onnx>=1.9.0  # ONNX export
+# onnx-simplifier>=0.3.6  # ONNX simplifier
+# scikit-learn==0.19.2  # CoreML quantization
+# tensorflow>=2.4.1  # TFLite export
+# tensorflowjs>=3.9.0  # TF.js export
+# openvino-dev  # OpenVINO export
+
+# Extras --------------------------------------
+# albumentations>=1.0.3
+# Cython  # for pycocotools https://github.com/cocodataset/cocoapi/issues/172
+# pycocotools>=2.0  # COCO mAP
+# roboflow
+thop  # FLOPs computation