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README.md

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+# Object Detection Model for Rotten Fruits
+**By Aiden Luo**
+
+# Model Description
+
+This is an object detection model that finds apples and oranges along with their rotten variants.
+The model is fine tuned from YOLOv11, which orignally uses COCO as its dataset.
+It is meant to detect rotten fruits on a conveyor belt when mass processing produce.
+
+# Training Data
+### Dataset
+
+[https://www.kaggle.com/datasets/muhammad0subhan/fruit-and-vegetable-disease-healthy-vs-rotten]
+
+**Classes**: 28
+
+**Images**: 29,291
+
+**Data Collection** 
+
+Dataset is a combination of other datasets containing fruit and vegetables, rotten and healthy, then
+manually valiadated and sorted.
+
+### Class Distribution and Annotations
+I used only 4 of the 28 classes (Apple_Healthy, Orange_Healthy,Apple_Rotten, Orange_Rotten).
+Annotated 1000 images using Roboflow's SAM3 autolabel, and validating 1000 of them.
+Manually added 578 detections, and fixed about 30% of the annotations as they were false positives.
+
+| Class Name    | Total Count | Training Count (70%) | Validation Count (20%) | Test Count (10%)|
+| ------------- | ----------- | -------------------- | ---------------------- | --------------- |
+| Apple         | 554         | 389                  | 111                    | 55              |
+| Orange        | 512         | 359                  | 102                    | 51              |
+| RottenApple   | 332         | 233                  | 66                     | 33              |
+| RottenOrange  | 246         | 172                  | 49                     | 25              |
+
+### Augmentations 
+
+- Rotation
+- Translate
+- Horizontal flipping
+- Mosaic
+
+### Training Procedure
+
+- **Framework** Ultralytics
+- **Hardware** NVIDIA Tesla T4
+- **Batch Size** 64
+- **Epochs** 100
+- **Patience** 50
+
+## Metrics (Epoch 100)
+
+| epoch | class/intances   | metrics/precision(B) | metrics/recall(B) | metrics/mAP50(B) | metrics/mAP50-95(B) | 
+| ----- | -----------------| -------------------- | ----------------- | ---------------- | ------------------- |
+| 100   | All(264)         | 0.944                | 0.899             | 0.964            | 0.899               |
+| 100   | Apple(79)        | 0.946                | 0.889             | 0.956            | 0.935               |
+| 100   | Orange(58)       | 0.916                | 0.914             | 0.957            | 0.902               |
+| 100   | RottenApple(73)  | 0.935                | 0.984             | 0.981            | 0.915               |
+| 100   | RottenOrange(54) | 0.978                | 0.808             | 0.961            | 0.844               |
+
+## Examples
+
+**Apple**
+: Red and green apples with a simple background.
+<img src='simpleapple.png' width="400" height="400">
+<img src='simplegreenapple.png' width="400" height="400">
+
+**Orange**
+: Oranges with a simple background.
+<img src='manyoranges.png' width="400" height="400">
+<img src='simpleorange.jpg' width="400" height="400">
+
+**RottenApple**
+: Moldy apples or deformed/old apples with a simple background.
+<img src='moldyapple.png' width="400" height="400">
+<img src='deformedapple.png' width="400" height="400">
+
+**RottenOrange**
+: Moldy oranges or deformed oranges with a simple background.
+<img src='deformedorange.png' width="400" height="400">
+<img src='moldyorange.png' width="400" height="400">
+
+## F1-Score
+<img src='BoxF1_curve.png' alt="F1 Curve Graph" width="700" height="700">
+
+## Confusion Matrix
+<img src='confusion_matrix_normalized.png' alt="Confusion Matrix Graph" width="700" height="700">
+
+## Train/Loss and Val/Loss Curves
+<img src='results.png' alt="Training and Validation Loss Curve Graph" width="700" height="700">
+
+## Performance Analysis
+
+My results show generally a very strong performance across the board, performing the best at all classes with a F1-score of 0.92 at a confidence level of 58%.
+It is good at both finding and identifying the object, however the confusion matrix does show some problems; the Orange class and background get mixed up fairly often
+despite a relatively strong diagonal shown in the matrix.  There is also no signs of obvious over or underfitting shown in the class/val loss curves, they both steadily curve down.
+However, the model could likely still be improved with more training or more images, as the curves don't seem like they've completely plateaud yet.
+
+## Limatations and Biases
+
+**Failure Cases**
+Struggles with the inside of fruits and human hands in background.
+<img src='failcase1.png' width="400" height="400">
+<img src='failcase2.png' width="400" height="400">
+
+**Poor Peforming classes**  
+Although they all have very high metrics, the Orange class likely performs the worst.  It gets mixed up on the background the most, likely because the
+dataset contains quite a few images with orange backgrounds.
+
+**Data Biases/Contextual limitations**
+Many images were blurry or low resolution, similarly some images contained logos or stock image words printed over the fruits.  Many of the fruits were all very similar in 
+species, there were fewer green apples and blood oranges in the dataset.  The model significantly degrades when the background is not simple or matches the examples.
+
+**Innappropriate Use Cases**
+This model is meant for conveyor belts, however anything that creates a non-static background such as human workers will mess with model accuracy.
+
+**Sample size limitations**
+The entire model could benefit from at least thousands more images in each class, but they can be very similar images as I want the model to be sucessful on a conveyor belt and not much else.  It's okay if it gets confused on human hands or can't detect the inside of a fruit as that usually won't happen on a conveyor belt.

args.yaml

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+task: detect
+mode: train
+model: yolo11n.pt
+data: /content/Dataset/data.yaml
+epochs: 100
+time: null
+patience: 50
+batch: 64
+imgsz: 640
+save: true
+save_period: -1
+cache: false
+device: null
+workers: 8
+project: null
+name: train
+exist_ok: false
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: false
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+multi_scale: 0.0
+compile: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+conf: null
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+end2end: null
+source: null
+vid_stride: 1
+stream_buffer: false
+visualize: false
+augment: false
+agnostic_nms: false
+classes: null
+retina_masks: false
+embed: null
+show: false
+save_frames: false
+save_txt: false
+save_conf: false
+save_crop: false
+show_labels: true
+show_conf: true
+show_boxes: true
+line_width: null
+format: torchscript
+keras: false
+optimize: false
+int8: false
+dynamic: false
+simplify: true
+opset: null
+workspace: null
+nms: false
+lr0: 0.01
+lrf: 0.01
+momentum: 0.937
+weight_decay: 0.0005
+warmup_epochs: 3.0
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+box: 7.5
+cls: 0.5
+dfl: 1.5
+pose: 12.0
+kobj: 1.0
+rle: 1.0
+angle: 1.0
+nbs: 64
+hsv_h: 0.015
+hsv_s: 0.7
+hsv_v: 0.4
+degrees: 0.0
+translate: 0.1
+scale: 0.5
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+bgr: 0.0
+mosaic: 1.0
+mixup: 0.0
+cutmix: 0.0
+copy_paste: 0.0
+copy_paste_mode: flip
+auto_augment: randaugment
+erasing: 0.4
+cfg: null
+tracker: botsort.yaml
+save_dir: /content/runs/detect/train

results.csv

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