Hugging Face's logo

Spaces:
erdoganpeker
/
hasari-api
Running

App Files Community
hasari-api
File size: 6,444 Bytes
e327f0d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
# Veri Hazirlik Rehberi — Arac Hasar Tespiti MVP

Bu rehber, `services/ml/` icin gereken tum veri setlerini ve pretrained
model agirliklarini nasil indirip dogrulayacaginizi anlatir.

## 0. Hizli Baslangic

```powershell
# 1) Bagimliliklar (sadece scriptler icin)
pip install -r scripts/requirements.txt

# 2) Plan/disk raporu (hicbir sey indirmez)
python scripts/download_data.py --all --dry-run
python scripts/download_pretrained.py --all --dry-run

# 3) Pretrained weights + CarDD HF mirror + CarParts-Seg (paralel)
python scripts/download_pretrained.py --yolo11
python scripts/download_data.py --cardd-hf
python scripts/download_data.py --carparts-ultra

# 4) Form dolduktan ve CarDD ZIP elinize ulastiginda:
python scripts/download_data.py --cardd-manual "C:\Users\Erdogan\Downloads\CarDD_release.zip"

# 5) Dogrulama
python scripts/verify_data.py
```

## 1. Veri Setleri

### 1.1. CarDD (Car Damage Detection)
- **Kaynak**: https://cardd-ustc.github.io
- **Yayin**: Wang et al., 2023. ~4000 goruntu, 6 sinif segmentation:
  dent, scratch, crack, glass_shatter, lamp_broken, tire_flat.
- **Lisans**: **Academic, non-commercial**. Ticari kullanim icin
  yazarlarla yazili izin gerekir. MVP demo/POC tamam, satis oncesi
  yeniden lisansla.
- **Erisim yolu A (resmi form, ~1-2 gun)**:
  Form gonderildikten sonra ZIP linki e-postaya gelir.
  ```powershell
  python scripts/download_data.py --cardd-manual "C:\path\to\CarDD_release.zip"
  ```
  Bu komut `services/ml/data/CarDD_release/` altina cikartir ve
  `services/ml/prepare_data.py` icin dogru yolu yazdirir.
- **Erisim yolu B (HF mirror, form bekleme yok)**:
  ```powershell
  python scripts/download_data.py --cardd-hf
  ```
  Hedef: `services/ml/data/cardd_hf/`. Lisans CarDD ile ayni — sadece
  data erisimi farkli.
- **Disk**: ~6.5 GB ham + ~7 GB YOLO dump (toplam ~14 GB icin yer ayirin).

### 1.2. Ultralytics CarParts-Seg (parca segmentasyonu)
- **Kaynak**: https://docs.ultralytics.com/datasets/segment/carparts-seg/
- **Boyut**: ~1.2 GB. 21 sinif (kapilar, tamponlar, farlar, ...).
- **Lisans**: Roboflow community license, ticari kullanima izinli.
- **Indirme**: Ultralytics otomatik halleder; biz tetikliyoruz:
  ```powershell
  python scripts/download_data.py --carparts-ultra
  python services/ml/prepare_parts_data.py --use_ultralytics ^
      --output_dir services/ml/data/parts_yolo
  ```

### 1.3. Roboflow severity (minor/moderate/severe)
- **Kaynak**: https://universe.roboflow.com (workspace/project kullanici secimi)
- **Erisim**: ROBOFLOW_API_KEY gerekli (`https://app.roboflow.com/settings/api`).
  Repo kokune `.env` ekleyin:
  ```
  ROBOFLOW_API_KEY=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
  ```
- **Indirme**:
  ```powershell
  python scripts/download_data.py --roboflow-severity ^
      --rf-workspace car-damage-detection-cardd ^
      --rf-project car-damage-severity ^
      --rf-version 1
  ```
- **NOT**: Workspace/project slug'lari Roboflow Universe'de arayip
  DOGRULAYIN. Default degerler placeholder'dir.

## 2. Pretrained Agirliklar

```powershell
# YOLO11 ailesi (n, s, m) — onerilen baslangic
python scripts/download_pretrained.py --yolo11

# YOLO26 ailesi (Ultralytics surumune bagli; yoksa atlanir)
python scripts/download_pretrained.py --yolo26

# CarDD-finetuned ckpt (HF'te varsa)
python scripts/download_pretrained.py --cardd-finetuned
```

Hedef: `services/ml/weights/*.pt` + `.sha256` sidecar.

## 3. Sirayla Yapilacaklar

1. `pip install -r scripts/requirements.txt`
2. `python scripts/download_pretrained.py --yolo11` (2-3 dk)
3. `python scripts/download_data.py --cardd-hf` (10-60 dk, baglantiya bagli)
4. `python scripts/download_data.py --carparts-ultra` (5-10 dk)
5. Paralel olarak CarDD resmi forma basvur (https://cardd-ustc.github.io).
6. CarDD HF mirror'i ile `prepare_data.py` calistirip baseline egit.
7. Resmi ZIP gelince `--cardd-manual` ile guncelle, modeli yeniden egit.
8. `python scripts/verify_data.py` ile her adim sonrasi dogrula.

## 4. Donanim Onerileri — RTX 5050 (8 GB VRAM, Blackwell)

- **PyTorch CUDA 12.8 wheel**:
  ```powershell
  pip install --index-url https://download.pytorch.org/whl/cu128 ^
      torch torchvision torchaudio
  ```
- **VRAM butcesi**:
  - `yolo11n-seg` `imgsz=640` `batch=16` → ~3.5 GB
  - `yolo11s-seg` `imgsz=640` `batch=12` → ~5.5 GB
  - `yolo11m-seg` `imgsz=640` `batch=6`  → ~7.5 GB (mixed precision)
- **Disk**: SSD'de en az 30 GB bos alan (ham + YOLO dump + checkpoint'ler).
- **RAM**: 16 GB yeterli; 32 GB ile data loader prefetch rahatlar.
- **Worker**: `workers=4` Windows'ta genelde stabil. Hatada `workers=0`.

## 5. Sorun Giderme

| Sorun | Cozum |
|------|-------|
| `huggingface_hub.errors.HfHubHTTPError 401` | `huggingface-cli login` (CarDD HF mirror public, normalde gerekmez) |
| `ROBOFLOW_API_KEY tanimli degil` | `.env` ekle veya `set ROBOFLOW_API_KEY=...` |
| Ultralytics `carparts-seg` indirilmiyor | `ultralytics` paketini guncelle: `pip install -U ultralytics` |
| CarDD ZIP cok yavas iniyor | HF mirror'a (1.1.B) dus, sonra resmi setle guncellersin |
| `torch.cuda.is_available() == False` | cu128 wheel'i kur, NVIDIA suruculerini guncelle |
| Disk dolu | Once `--dry-run` ile plan al; gereksiz `cardd_yolo/` kopyalarini sil |

## 6. Lisans Ozeti

| Set | Lisans | MVP icin | Ticari icin |
|-----|--------|----------|-------------|
| CarDD | Academic non-commercial | OK (POC) | Yazardan yazili izin |
| CarParts-Seg | Roboflow community | OK | OK |
| Roboflow severity | Project'e gore degisir | Kontrol et | Kontrol et |
| YOLO11/26 weights | AGPL-3.0 | OK | Ticari icin Ultralytics Enterprise |

**Yasal not**: Ulusal mevzuat (KVKK) ve Ultralytics AGPL etkilesimi, satis
asamasinda hukuksal incelemeden gecirilmelidir.

## 7. Dosya Yapisinin Beklenen Hali

```
services/ml/
  data/
    cardd_hf/                 # HF mirror snapshot
    CarDD_release/            # Form sonrasi resmi ZIP ictigi
      CarDD_COCO/
        annotations/
          instances_train2017.json
          instances_val2017.json
          instances_test2017.json
        train2017/  val2017/  test2017/
    cardd_yolo/               # prepare_data.py ciktisi
      images/{train,val,test}/
      labels/{train,val,test}/
    parts_yolo/               # prepare_parts_data.py ciktisi
    severity_roboflow/        # Roboflow ZIP ictigi
  weights/
    yolo11n-seg.pt + .sha256
    yolo11s-seg.pt + .sha256
    ...
scripts/.logs/                # Tum indirme/dogrulama loglari
```