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Segformer-B5 (85M parameters) fine-tuned for 8-class semantic segmentation of outdoor apple orchard scenes captured from a robotic platform.
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Segformer-B5 (85M parameters) fine-tuned for 8-class semantic segmentation of outdoor apple orchard scenes captured from a robotic platform.
This repo contains two checkpoints:
Fontes
1 fonteVerificado 8 de set.
Artefatos de modelo
2 artefatosTrechos de fonte
2 trechos| File | When to use |
|---|
Segformer85Mv1.pt | Original v1, trained only on the spring oak_0415 dataset. Best baseline. |
Segformer85Mv2.pt ⭐ | v1 + fine-tuned on a second dataset (different camera, autumn season). Use this for general deployment — same accuracy on the original orchard, dramatically better generalization to new cameras / new seasons. |
from huggingface_hub import hf_hub_download
from transformers import SegformerForSemanticSegmentation
import torch, cv2, numpy as np
import torch.nn.functional as F
# 1. Download weights — pick v1 OR v2
ckpt_path = hf_hub_download(repo_id="WEN0256/Segformer85Mv1", filename="Segformer85Mv2.pt")
# ^^^^^^^^^^^^^^^^^
# use v2 by default
# 2. Init architecture from base + load fine-tuned weights
NAMES = ["tree","ground","person","sky","road","mountain","building","background"]
model = SegformerForSemanticSegmentation.from_pretrained(
"nvidia/segformer-b5-finetuned-ade-640-640",
num_labels=8,
id2label={i:n for i,n in enumerate(NAMES)},
label2id={n:i for i,n in enumerate(NAMES)},
ignore_mismatched_sizes=True,
).cuda().eval()
model.load_state_dict(torch.load(ckpt_path, map_location="cuda")["model"])
# 3. Inference
img = cv2.imread("your_image.jpg")
H, W = img.shape[:2]
H32, W32 = (H//32)*32, (W//32)*32
rgb = cv2.cvtColor(cv2.resize(img, (W32, H32)), cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
mean = np.array([0.485, 0.456, 0.406]); std = np.array([0.229, 0.224, 0.225])
x = torch.from_numpy(((rgb - mean) / std).transpose(2,0,1)).unsqueeze(0).float().cuda()
with torch.no_grad():
logits = model(pixel_values=x).logits
logits = F.interpolate(logits, size=(H, W), mode="bilinear", align_corners=False)
pred = logits.argmax(1)[0].cpu().numpy() # H x W, values 0..7
A ready-to-use predict.py is included in this repo.
| ID | Class | Notes |
|---|---|---|
| 0 | tree | Apple trees (priority class for downstream tasks) |
| 1 | ground | Grass / dirt / orchard floor |
| 2 | person | Workers in scene |
| 3 | sky | |
| 4 | road | Path between rows |
| 5 | mountain | Distant terrain |
| 6 | building | Sheds, equipment shelters |
| 7 | background | Unknown / unlabeled regions (model output rare) |
| Base model | nvidia/segformer-b5-finetuned-ade-640-640 |
| Parameters | ~85M |
| Decoder head | Reinitialized for 8 classes |
| Input format | RGB, normalized with ImageNet mean/std |
mean | [0.485, 0.456, 0.406] |
std | [0.229, 0.224, 0.225] |
| Input resolution | Any H×W where both are multiples of 32 |
| Trained at | 1024×576 (native 16:9) |
Validated on a temporally-disjoint hold-out from the same recording (frames 4501+, no leakage):
| Metric | Value |
|---|---|
| Tree IoU | 0.742 |
| mIoU (7 real classes) | 0.714 |
| Pixel accuracy | 0.834 |
Same v1 hold-out → no regression on old domain:
| Metric | v1 | v2 |
|---|---|---|
| Tree IoU (orig orchard, no leak) | 0.742 | 0.742 ✅ |
| mIoU (orig orchard) | 0.714 | 0.712 |
NEW orchard hold-out (different camera, autumn season — Aug+Sep capture):
| Metric | v1 | v2 |
|---|---|---|
| Tree recall on new orchard | ~0.55 (estimated) | 0.999 🚀 |
Visual qualitative: v1 sometimes misclassifies autumn foliage as person (red); v2 cleanly segments it as tree. See samples/ for side-by-side examples.
| Class | IoU | Precision | Recall |
|---|---|---|---|
| tree | 0.742 | 0.79 | 0.93 |
| ground | 0.851 | 0.91 | 0.93 |
| person | 0.719 | 0.82 | 0.85 |
| sky | 0.769 | 0.83 | 0.91 |
| road | 0.804 | 0.86 | 0.92 |
| mountain | 0.437 | 0.62 | 0.66 |
| building | 0.711 | 0.84 | 0.83 |
vines→tree, moutain→mountain typo fixed)<=4500 train (5177 samples), frames >4500 validation (155 samples) — no neighbor leakageignore_index=255 so the model only adapts its tree decisions, leaving other classes untouched| Hyperparameter | Value |
|---|---|
| Optimizer | AdamW, weight_decay 0.01 |
| LR | 2e-5, cosine schedule |
| Epochs | 30 |
| Batch | 2 × grad_accum 4 (effective 8) |
| Resolution | 1024×576 |
| Precision | bfloat16 |
| Loss | weighted cross-entropy |
| Class weights | tree 1.5, ground 0.5, person 1.5, sky 1.0, road 1.0, mountain 1.0, building 1.0, background 0.1 |
| Hardware | RTX 5090 (32 GB), ~2.3 hours |
| Hyperparameter | Value |
|---|---|
| LR | 5e-6 (10× lower for safe fine-tune) |
| Epochs | 8 (best at epoch 3) |
ignore_index | 255 (for unlabeled pixels in new data) |
| Everything else | Same as v1 |
| Hardware | RTX 5090, ~13 minutes |
This model was trained on a single Korean apple orchard (spring 2024) with a single robot platform, plus a small fine-tune on a second autumn capture. Expect degradation on:
For deployment in a new context, plan to fine-tune on 100-300 in-domain images.
| File | Purpose |
|---|---|
Segformer85Mv1.pt | Original v1 weights (339 MB) |
Segformer85Mv2.pt | v1 + Orchard Navigation fine-tune (339 MB) ⭐ |
predict.py | Standalone inference script (defaults to v2) |
README.md | This file |
samples/*.jpg | v1 prediction examples (in-domain) |
samples_v6_vs_v7/*.jpg | v1 vs v2 side-by-side on new orchard (showcases v2 improvement) |
train_v6_5090.py | v1 training script |
finetune_v7.py | v2 fine-tune script |
history_v6.json | v1 per-epoch training history |
history_v7.json |
Apache 2.0
--- license: apache-2.0 language: - en tags: - semantic-segmentation - segformer - agriculture - orchard - apple - outdoor library_name: transformers pipeline_tag: image-segmentation base_model: nvidia/segformer-b5-finetuned-ade-640-640 --- # Segformer85M — Apple Orchard Semantic Segmentation Segformer-B5 (85M parameters) fine-tuned for **8-class semantic segmentation** of outdoor apple orchard scenes captured from a robotic platform. This repo contains **two checkpoints**: | File | When to use | |------|-------------| | **`Segformer85Mv1.pt`** | Original v1, trained only on the spring oak_0415 dataset. Best baseline. | | **`Segformer85Mv2.pt`** ⭐ | v1 + fine-tuned on a second dataset (different camera, autumn season). **Use this for general deployment** — same accuracy on the original orchard, dramatically better generalization to new cameras / new seasons. | ## Quick Use ```python from huggingface_hub import hf_hub_download from transformers import SegformerForSemanticSegmentation import torch, cv2, numpy as np import torch.nn.functional as F # 1. Download weights — pick v1 OR v2 ckpt_path = hf_hub_download(repo_id="WEN0256/Segformer85Mv1", filename="Segformer85Mv2.pt") # ^^^^^^^^^^^^^^^^^ # use v2 by default # 2. Init architecture from base + load fine-tuned weights NAMES = ["tree","ground","person","sky","road","mountain","building","background"] model = SegformerForSemanticSegmentation.from_pretrained( "nvidia/segformer-b5-finetuned-ade-640-640", num_labels=8, id2label={i:n for i,n in enumerate(NAMES)}, label2id={n:i for i,n in enumerate(NAMES)}, ignore_mismatched_sizes=True, ).cuda().eval() model.load_state_dict(torch.load(ckpt_path, map_location="cuda")["model"]) # 3. Inference img = cv2.imread("your_image.jpg") H, W = img.shape[:2] H32, W32 = (H//32)*32, (W//32)*32 rgb = cv2.cvtColor(cv2.resize(img, (W32, H32)), cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0 mean = np.array([0.485, 0.456, 0.406]); std = np.array([0.229, 0.224, 0.225]) x = torch.from_numpy(((rgb - mean) / std).transpose(2,0,1)).unsqueeze(0).float().cuda() with torch.no_grad(): logits = model(pixel_values=x).logits logits = F.interpolate(logits, size=(H, W), mode="bilinear", align_corners=False) pred = logits.argmax(1)[0].cpu().numpy() # H x W, values 0..7 ``` A ready-to-use `predict.py` is included in this repo. ## Classes (id → name) | ID | Class | Notes | |---...
Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Library transformers · Repo WEN0256/Segformer85Mv1
| v2 fine-tune history |
v6_OOD_full_res.mp4 | 1-minute OOD inference video at native resolution |