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Browse files- README.md +47 -47
- app.py +1 -1
- models/README.md +1 -1
- models/ethiopia_washed_custom_Noback_best_model.json +11 -11
- models/ethiopia_washed_history.json +281 -281
- models/ethiopia_washed_metrics.json +67 -67
- models/honduras_natural_custom_Noback_best_model.json +11 -11
- models/honduras_natural_history.json +281 -281
- models/honduras_natural_metrics.json +67 -67
README.md
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| 🚀 **Web 部署** | Flask、Gunicorn 與 Docker，可部署至 Hugging Face Spaces。 |
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| 🎨 **介面設計** | 暗色 Glassmorphism 儀表板，顯示模型輸出機率與架構資訊。 |
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## 🧠
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###
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- 圖片由開發者自行拍攝，使用 OpenCV 依輪廓裁切成單顆咖啡豆。
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- 裁切圖片由開發者人工標示為 `good`、`bad`、`back` 或 `idontknow`。
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- Dataset 為私人資料，**不隨 GitHub repository 或 Hugging Face Model repository 發布**。
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- Ethiopia Washed 與 Honduras Natural 已完成訓練；Kenya Natural 仍在資料整理與標註階段，暫不發布模型。
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###
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| 項目 | 設定 |
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###
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離線資料增強由 `data_augment.ipynb` 執行，每張來源 crop 產生兩個版本，包含 color jitter、random grayscale、Gaussian blur、sharpness adjustment 與 Gaussian noise。
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## 📊
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以下結果由修正後的 best-checkpoint 保存流程重新訓練取得。數值來自現有 augmented dataset 的 80/20 random validation split。
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|---|---:|---:|---:|---:|---:|---:|
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| Ethiopia Washed | 143 | 79.02% | 78.57% | 79.16% | 78.72% | 79.13% |
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| Honduras Natural | 114 | 78.07% | 69.30% | 73.89% | 70.76% | 79.22% |
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###
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|---|---|---:|---:|---:|---:|
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| Ethiopia Washed | bad | 84.42% | 78.31% | 81.25% | 83 |
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| Ethiopia Washed | good | 72.73% | 80.00% | 76.19% | 60 |
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| Honduras Natural | bad | 48.48% | 66.67% | 56.14% | 24 |
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| Honduras Natural | good | 90.12% | 81.11% | 85.38% | 90 |
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```text
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Ethiopia Washed: [[65, 18], [12, 48]]
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Honduras Natural: [[16, 8], [17, 73]]
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```
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> **
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Honduras Natural
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## 🚀
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### Web
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Python 3.10
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```bash
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conda create -n coffee-beans-env python=3.10
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python app.py
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```
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###
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```bash
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pip install -r requirements-dev.txt
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python train_all.py
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```
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## 📖
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## 📂
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```text
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Good-or-Bad-CoffeeBeans/
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├── models/ #
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├── static/ # CSS
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├── templates/ # Flask HTML
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├── app.py # Web
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├── train_all.py # Ethiopia
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├── requirements.txt # Web
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├── requirements-dev.txt#
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└── coffee_beans_data/ #
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```
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## ⚠️
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- Labels are manually assigned and are not a professional coffee-grading certification.
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- Each deployed model is specific to one bean type.
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- Softmax output scores are not calibrated confidence intervals.
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- Kenya Natural is under development and has no published model.
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## 👨💻
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- Author: dachen8173
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- Stack: Python / PyTorch / Flask / OpenCV / JavaScript
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- Contact: op.dada.op@gmail.com
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- Instagram: [da_chen_527](https://www.instagram.com/da_chen_527) / [cofe_log](https://www.instagram.com/cofe_log)
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-
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| 🚀 **Web 部署** | Flask、Gunicorn 與 Docker，可部署至 Hugging Face Spaces。 |
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| 🎨 **介面設計** | 暗色 Glassmorphism 儀表板，顯示模型輸出機率與架構資訊。 |
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## 🧠 模型與資料集
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### 資料集
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- 圖片由開發者自行拍攝，使用 OpenCV 依輪廓裁切成單顆咖啡豆。
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- 裁切圖片由開發者人工標示為 `good`、`bad`、`back` 或 `idontknow`。
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- Dataset 為私人資料，**不隨 GitHub repository 或 Hugging Face Model repository 發布**。
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- Ethiopia Washed 與 Honduras Natural 已完成訓練；Kenya Natural 仍在資料整理與標註階段，暫不發布模型。
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### 模型
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| 項目 | 設定 |
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| 框架 | PyTorch |
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| 架構 | Custom CNN：3 個 Conv/BatchNorm/ReLU/MaxPool 區塊，加上 256-unit 全連接層 |
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| 任務 | 二元分類（`bad`、`good`） |
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| 輸入 | RGB，128 × 128 |
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| 前處理 | 補邊成正方形、縮放、轉換為 tensor，並以 mean/std 0.5 標準化 |
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| 最佳化器 | Adam，learning rate `5e-5` |
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| 損失函數 | 依訓練子集計算權重的交叉熵 |
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| 訓練 | 最多 40 epochs，early stopping patience 10 |
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| 切分 | 沿用既有 80/20 隨機訓練／驗證切分 |
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### 資料增強
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離線資料增強由 `data_augment.ipynb` 執行，每張來源 crop 產生兩個版本，包含 color jitter、random grayscale、Gaussian blur、sharpness adjustment 與 Gaussian noise。
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## 📊 驗證結果
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以下結果由修正後的 best-checkpoint 保存流程重新訓練取得。數值來自現有 augmented dataset 的 80/20 random validation split。
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| 咖啡豆類型 | 驗證樣本數 | Accuracy | Macro precision | Macro recall | Macro F1 | Weighted F1 |
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| Ethiopia Washed | 143 | 79.02% | 78.57% | 79.16% | 78.72% | 79.13% |
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| Honduras Natural | 114 | 78.07% | 69.30% | 73.89% | 70.76% | 79.22% |
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### 各類別結果
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| 咖啡豆類型 | 類別 | Precision | Recall | F1 | 樣本數 |
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| Ethiopia Washed | bad | 84.42% | 78.31% | 81.25% | 83 |
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| Ethiopia Washed | good | 72.73% | 80.00% | 76.19% | 60 |
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| Honduras Natural | bad | 48.48% | 66.67% | 56.14% | 24 |
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| Honduras Natural | good | 90.12% | 81.11% | 85.38% | 90 |
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混淆矩陣以實際標籤為列、預測標籤為欄，順序皆為 `[bad, good]`：
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```text
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Ethiopia Washed: [[65, 18], [12, 48]]
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Honduras Natural: [[16, 8], [17, 73]]
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```
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> **評估限制：** 為保留目前專案流程，資料是在離線增強後才進行切分，因此同一來源 crop 的增強版本可能同時出現在訓練集與驗證集。驗證集也用於 early stopping，目前沒有獨立測試集。這些數字只代表本次驗證結果，不應視為完全排除資料洩漏的 benchmark。
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Honduras Natural 對 `bad` 類別的表現仍較弱，尤其是 precision。單看 accuracy 會掩蓋此問題，因此同時呈現 macro 與各類別指標。
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機器可讀的結果位於 `models/*_metrics.json`，各 epoch 紀錄位於 `models/*_history.json`。
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## 🚀 快速開始
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### Web 應用程式
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建議使用 Python 3.10 或 3.11。
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```bash
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conda create -n coffee-beans-env python=3.10
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python app.py
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```
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服務啟動後，開啟 <http://localhost:5000>。
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應用程式啟動時，會從 `dada8173/coffee-bean-classifier-models` 的固定 revision `efc47d2ec57916c5ea152333bd801a07a6977f59` 下載權重。Hugging Face cache 會避免重複下載未變更的檔案；若 Hub 暫時無法使用，程式才會改用本機 `models/*.pth` 備援。需要強制使用本機模型時可設定 `USE_LOCAL_MODELS=1`；只有在刻意測試其他已發布版本時才應設定 `HF_MODEL_REVISION`。下載公開模型不需要 access token。
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### 訓練與 notebooks
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```bash
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pip install -r requirements-dev.txt
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python train_all.py
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```
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`train_all.py` 預設只訓練 Ethiopia Washed 與 Honduras Natural；Kenya Natural 仍在開發中。
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## 📖 使用方式
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1. 選擇對應咖啡豆類型的可用模型。
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2. 開啟瀏覽器相機，或上傳支援格式的圖片。
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3. 使用相機時可開啟即時辨識。
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4. 查看預測標籤與模型輸出分數。
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瀏覽器相機功能需要 HTTPS 或 localhost，並須取得使用者授權。
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## 📂 專案結構
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```text
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Good-or-Bad-CoffeeBeans/
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├── models/ # 模型設定、訓練紀錄與驗證指標
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├── static/ # CSS、JavaScript 與範例圖片
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├── templates/ # Flask HTML 模板
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├── app.py # Web 推論與模型載入
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├── train_all.py # Ethiopia／Honduras 訓練與評估
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├── requirements.txt # Web 推論相依套件
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├── requirements-dev.txt# 訓練與 notebook 相依套件
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└── coffee_beans_data/ # 私人本機資料集；Git 會忽略此目錄
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```
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## ⚠️ 限制
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- Labels are manually assigned and are not a professional coffee-grading certification.
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- Each deployed model is specific to one bean type.
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- Softmax output scores are not calibrated confidence intervals.
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- Kenya Natural is under development and has no published model.
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## 👨💻 開發者與授權
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- Author: dachen8173
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- Stack: Python / PyTorch / Flask / OpenCV / JavaScript
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- Contact: op.dada.op@gmail.com
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- Instagram: [da_chen_527](https://www.instagram.com/da_chen_527) / [cofe_log](https://www.instagram.com/cofe_log)
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原始碼採用 MIT License；資料集維持私人狀態，不隨本專案發布。
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app.py
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USE_LOCAL_MODELS = os.environ.get("USE_LOCAL_MODELS", "").lower() in {
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"1",
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"HF_MODEL_REPO_ID", "dada8173/coffee-bean-classifier-models"
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)
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MODEL_REPO_REVISION = os.environ.get(
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"HF_MODEL_REVISION", "efc47d2ec57916c5ea152333bd801a07a6977f59"
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)
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USE_LOCAL_MODELS = os.environ.get("USE_LOCAL_MODELS", "").lower() in {
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"1",
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models/README.md
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At startup, `app.py` downloads each matching `.pth` file from
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`dada8173/coffee-bean-classifier-models` using pinned commit
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`
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the default when intentionally deploying another revision. Downloads use Hugging
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Face's version-aware local cache. If the Hub is unavailable, the application
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falls back to a matching local weight; set `USE_LOCAL_MODELS=1` to force that
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At startup, `app.py` downloads each matching `.pth` file from
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`dada8173/coffee-bean-classifier-models` using pinned commit
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`efc47d2ec57916c5ea152333bd801a07a6977f59`. `HF_MODEL_REVISION` can override
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the default when intentionally deploying another revision. Downloads use Hugging
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Face's version-aware local cache. If the Hub is unavailable, the application
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falls back to a matching local weight; set `USE_LOCAL_MODELS=1` to force that
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models/ethiopia_washed_custom_Noback_best_model.json
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{
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"display_name": "Ethiopia Washed - Custom CNN (Noback)"
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"model_file": "ethiopia_washed_custom_Noback_best_model.pth",
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"metrics_file": "ethiopia_washed_metrics.json",
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"display_name": "Ethiopia Washed - Custom CNN (Noback)"
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"train_accuracy": 0.5061511423550088,
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"validation_loss": 0.6918301582336426,
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"train_accuracy": 0.4253075571177504,
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"validation_loss": 0.691796600818634,
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"train_accuracy": 0.4446397188049209,
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"validation_loss": 0.6918022632598877,
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"validation_accuracy": 0.5874125874125874
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"train_accuracy": 0.6186291739894552,
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models/ethiopia_washed_metrics.json
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models/honduras_natural_custom_Noback_best_model.json
CHANGED
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models/honduras_natural_history.json
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